KBench / tools /factory /difficulty.json
ZMC2019's picture
Reorganise: group 313 tasks into 17 families under tasks/, generators under tools/ (part 2)
2e4c7fe verified
Raw
History Blame Contribute Delete
195 kB
[
{
"name": "activation-recompute-fusion",
"tier": "T3",
"why": "GEMM with a fused activation/gate epilogue: needs a hand-written MMA pipeline",
"frac": 0.3298,
"analysis": {
"grouped": false,
"custom_conv": false,
"epilogue": true,
"n_mm": 4,
"sdpa": false,
"conv": false,
"reduce": false,
"masky": false,
"quant": false
},
"metric": "TFLOP/s",
"roofline_us": 2356.0963452342858,
"ref_metric": 230.8908
},
{
"name": "adafactor-factored-update",
"tier": "T2",
"why": "bandwidth kernel with a reduction/gather: shared-memory tiling and a warp reduction",
"frac": 0.0677,
"analysis": {
"grouped": false,
"custom_conv": false,
"epilogue": false,
"n_mm": 0,
"sdpa": false,
"conv": false,
"reduce": true,
"masky": false,
"quant": false
},
"metric": "GB/s",
"roofline_us": 520.8333333333334,
"ref_metric": 325.0389
},
{
"name": "adaptive-sparsity-threshold",
"tier": "T2",
"why": "bandwidth kernel with a reduction/gather: shared-memory tiling and a warp reduction",
"frac": 0.0131,
"analysis": {
"grouped": true,
"custom_conv": true,
"epilogue": false,
"n_mm": 0,
"sdpa": false,
"conv": false,
"reduce": true,
"masky": false,
"quant": true
},
"metric": "GB/s",
"roofline_us": 359.1478429734707,
"ref_metric": 63.0037
},
{
"name": "alibi-slopes-attention",
"tier": "T3",
"why": "attention with custom masking/sparsity: no library kernel applies as-is, so the tiled online-softmax pipeline is written by hand",
"frac": 0.0111,
"analysis": {
"grouped": false,
"custom_conv": true,
"epilogue": false,
"n_mm": 2,
"sdpa": false,
"conv": false,
"reduce": true,
"masky": true,
"quant": false
},
"metric": "TFLOP/s",
"roofline_us": 785.3654484114286,
"ref_metric": 7.758
},
{
"name": "altup-predict-step",
"tier": "T3",
"why": "GEMM with a fused activation/gate epilogue: needs a hand-written MMA pipeline",
"frac": 0.0167,
"analysis": {
"grouped": false,
"custom_conv": false,
"epilogue": true,
"n_mm": 2,
"sdpa": false,
"conv": false,
"reduce": true,
"masky": false,
"quant": false
},
"metric": "GB/s",
"roofline_us": 651.0456651449203,
"ref_metric": 79.9281
},
{
"name": "any-res-image-split",
"tier": "T1",
"why": "elementwise/bandwidth chain: the win is fusing the passes into one",
"frac": 0.0269,
"analysis": {
"grouped": false,
"custom_conv": true,
"epilogue": false,
"n_mm": 0,
"sdpa": false,
"conv": false,
"reduce": false,
"masky": false,
"quant": false
},
"metric": "GB/s",
"roofline_us": 336.456298828125,
"ref_metric": 128.8881
},
{
"name": "async-weight-prefetch-pipeline",
"tier": "T3",
"why": "GEMM with a fused activation/gate epilogue: needs a hand-written MMA pipeline",
"frac": null,
"analysis": {
"grouped": false,
"custom_conv": true,
"epilogue": true,
"n_mm": 1,
"sdpa": false,
"conv": false,
"reduce": true,
"masky": false,
"quant": false
},
"metric": null,
"roofline_us": null,
"ref_metric": null
},
{
"name": "attention-lse-merge",
"tier": "T3",
"why": "multi-GEMM block: needs a hand-written, pipelined MMA sequence to beat",
"frac": 0.0749,
"analysis": {
"grouped": false,
"custom_conv": false,
"epilogue": false,
"n_mm": 1,
"sdpa": false,
"conv": false,
"reduce": true,
"masky": false,
"quant": false
},
"metric": "GB/s",
"roofline_us": 1057.9427083333333,
"ref_metric": 359.4947
},
{
"name": "attention-qk-norm-inline",
"tier": "T3",
"why": "attention with custom masking/sparsity: no library kernel applies as-is, so the tiled online-softmax pipeline is written by hand",
"frac": 0.0141,
"analysis": {
"grouped": false,
"custom_conv": true,
"epilogue": false,
"n_mm": 2,
"sdpa": false,
"conv": false,
"reduce": true,
"masky": true,
"quant": false
},
"metric": "TFLOP/s",
"roofline_us": 785.3654484114286,
"ref_metric": 9.8599
},
{
"name": "attention-sink-decode",
"tier": "T3",
"why": "attention with custom masking/sparsity: no library kernel applies as-is, so the tiled online-softmax pipeline is written by hand",
"frac": 0.0086,
"analysis": {
"grouped": false,
"custom_conv": false,
"epilogue": false,
"n_mm": 2,
"sdpa": false,
"conv": false,
"reduce": true,
"masky": true,
"quant": false
},
"metric": "GB/s",
"roofline_us": 416.6666666666667,
"ref_metric": 41.1527
},
{
"name": "attn-density-profile-fused",
"tier": "T3",
"why": "multi-GEMM block: needs a hand-written, pipelined MMA sequence to beat",
"frac": 0.011,
"analysis": {
"grouped": false,
"custom_conv": false,
"epilogue": false,
"n_mm": 1,
"sdpa": false,
"conv": false,
"reduce": true,
"masky": false,
"quant": false
},
"metric": "TFLOP/s",
"roofline_us": 1056.964608,
"ref_metric": 7.6661
},
{
"name": "audio-codec-rvq-quantize",
"tier": "T3",
"why": "quantised matmul: cuBLAS will not fuse the dequant, so the MMA pipeline is hand-written -- async copy, double buffering, fragment layouts",
"frac": 0.0202,
"analysis": {
"grouped": false,
"custom_conv": false,
"epilogue": false,
"n_mm": 1,
"sdpa": false,
"conv": false,
"reduce": true,
"masky": false,
"quant": true
},
"metric": "TFLOP/s",
"roofline_us": 431.41412571428566,
"ref_metric": 14.1646
},
{
"name": "audio-mel-spectrogram-fused",
"tier": "T3",
"why": "multi-GEMM block: needs a hand-written, pipelined MMA sequence to beat",
"frac": 0.0855,
"analysis": {
"grouped": false,
"custom_conv": false,
"epilogue": false,
"n_mm": 1,
"sdpa": false,
"conv": false,
"reduce": true,
"masky": false,
"quant": false
},
"metric": "GB/s",
"roofline_us": 315.9046173095703,
"ref_metric": 410.4547
},
{
"name": "awq-dequant-gemm",
"tier": "T3",
"why": "quantised matmul: cuBLAS will not fuse the dequant, so the MMA pipeline is hand-written -- async copy, double buffering, fragment layouts",
"frac": 0.0536,
"analysis": {
"grouped": true,
"custom_conv": false,
"epilogue": false,
"n_mm": 1,
"sdpa": false,
"conv": false,
"reduce": false,
"masky": true,
"quant": true
},
"metric": "TFLOP/s",
"roofline_us": 785.3654484114286,
"ref_metric": 37.5263
},
{
"name": "based-backward",
"tier": "T3",
"why": "attention with custom masking/sparsity: no library kernel applies as-is, so the tiled online-softmax pipeline is written by hand",
"frac": 0.0022,
"analysis": {
"grouped": false,
"custom_conv": true,
"epilogue": false,
"n_mm": 5,
"sdpa": false,
"conv": false,
"reduce": true,
"masky": true,
"quant": false
},
"metric": "TFLOP/s",
"roofline_us": 533.2278593828571,
"ref_metric": 1.5611
},
{
"name": "based-forward",
"tier": "T3",
"why": "attention with custom masking/sparsity: no library kernel applies as-is, so the tiled online-softmax pipeline is written by hand",
"frac": 0.0082,
"analysis": {
"grouped": false,
"custom_conv": true,
"epilogue": false,
"n_mm": 5,
"sdpa": false,
"conv": false,
"reduce": true,
"masky": true,
"quant": false
},
"metric": "TFLOP/s",
"roofline_us": 480.11598701714286,
"ref_metric": 5.7556
},
{
"name": "beam-search-step",
"tier": "T2",
"why": "bandwidth kernel with a reduction/gather: shared-memory tiling and a warp reduction",
"frac": 0.0122,
"analysis": {
"grouped": false,
"custom_conv": false,
"epilogue": false,
"n_mm": 0,
"sdpa": false,
"conv": false,
"reduce": true,
"masky": true,
"quant": true
},
"metric": "GB/s",
"roofline_us": 416.67938232421875,
"ref_metric": 58.5811
},
{
"name": "block-sparse-video-attn",
"tier": "T3",
"why": "quantised matmul: cuBLAS will not fuse the dequant, so the MMA pipeline is hand-written -- async copy, double buffering, fragment layouts",
"frac": 0.0112,
"analysis": {
"grouped": false,
"custom_conv": true,
"epilogue": false,
"n_mm": 4,
"sdpa": false,
"conv": false,
"reduce": true,
"masky": true,
"quant": true
},
"metric": "TFLOP/s",
"roofline_us": 1346.5872910628573,
"ref_metric": 7.8659
},
{
"name": "blockwise-fp8-gemm",
"tier": "T3",
"why": "quantised matmul: cuBLAS will not fuse the dequant, so the MMA pipeline is hand-written -- async copy, double buffering, fragment layouts",
"frac": null,
"analysis": {
"grouped": false,
"custom_conv": false,
"epilogue": false,
"n_mm": 1,
"sdpa": false,
"conv": false,
"reduce": false,
"masky": false,
"quant": true
},
"metric": null,
"roofline_us": null,
"ref_metric": 0.0
},
{
"name": "cache-hit-skip-gate",
"tier": "T2",
"why": "bandwidth kernel with a reduction/gather: shared-memory tiling and a warp reduction",
"frac": 0.0576,
"analysis": {
"grouped": false,
"custom_conv": false,
"epilogue": false,
"n_mm": 0,
"sdpa": false,
"conv": false,
"reduce": false,
"masky": true,
"quant": false
},
"metric": "GB/s",
"roofline_us": 593.3761596679688,
"ref_metric": 276.7193
},
{
"name": "cascade-prefix-attention",
"tier": "T3",
"why": "attention with custom masking/sparsity: no library kernel applies as-is, so the tiled online-softmax pipeline is written by hand",
"frac": null,
"analysis": {
"grouped": false,
"custom_conv": true,
"epilogue": false,
"n_mm": 1,
"sdpa": true,
"conv": false,
"reduce": false,
"masky": true,
"quant": false
},
"metric": null,
"roofline_us": null,
"ref_metric": 47.2345
},
{
"name": "causal-conv1d-decode-step",
"tier": "T2",
"why": "bandwidth kernel with a reduction/gather: shared-memory tiling and a warp reduction",
"frac": 0.0288,
"analysis": {
"grouped": false,
"custom_conv": true,
"epilogue": true,
"n_mm": 0,
"sdpa": false,
"conv": false,
"reduce": true,
"masky": false,
"quant": false
},
"metric": "GB/s",
"roofline_us": 625.0,
"ref_metric": 138.3901
},
{
"name": "causal-conv1d-fused",
"tier": "T1",
"why": "elementwise/bandwidth chain: the win is fusing the passes into one",
"frac": 0.0198,
"analysis": {
"grouped": false,
"custom_conv": true,
"epilogue": true,
"n_mm": 0,
"sdpa": false,
"conv": false,
"reduce": false,
"masky": false,
"quant": false
},
"metric": "GB/s",
"roofline_us": 833.3492279052734,
"ref_metric": 94.9032
},
{
"name": "causal-conv3d-backward",
"tier": "T3",
"why": "convolution with custom padding/grouping/tiling: cuDNN's path for this case is not the fast one, so the tiled kernel is the task",
"frac": 0.0205,
"analysis": {
"grouped": false,
"custom_conv": true,
"epilogue": false,
"n_mm": 1,
"sdpa": false,
"conv": true,
"reduce": false,
"masky": false,
"quant": false
},
"metric": "TFLOP/s",
"roofline_us": 728.0113371428571,
"ref_metric": 14.3173
},
{
"name": "causal-conv3d-cache-step",
"tier": "T3",
"why": "convolution with custom padding/grouping/tiling: cuDNN's path for this case is not the fast one, so the tiled kernel is the task",
"frac": 0.043,
"analysis": {
"grouped": false,
"custom_conv": true,
"epilogue": false,
"n_mm": 1,
"sdpa": false,
"conv": true,
"reduce": false,
"masky": false,
"quant": false
},
"metric": "TFLOP/s",
"roofline_us": 465.9272557714285,
"ref_metric": 30.1211
},
{
"name": "causal-conv3d-forward",
"tier": "T3",
"why": "convolution with custom padding/grouping/tiling: cuDNN's path for this case is not the fast one, so the tiled kernel is the task",
"frac": 0.0473,
"analysis": {
"grouped": false,
"custom_conv": true,
"epilogue": false,
"n_mm": 1,
"sdpa": false,
"conv": true,
"reduce": false,
"masky": false,
"quant": false
},
"metric": "TFLOP/s",
"roofline_us": 533.8749805714285,
"ref_metric": 33.0775
},
{
"name": "causal-conv3d-fp8",
"tier": "T3",
"why": "quantised matmul: cuBLAS will not fuse the dequant, so the MMA pipeline is hand-written -- async copy, double buffering, fragment layouts",
"frac": 0.0514,
"analysis": {
"grouped": false,
"custom_conv": true,
"epilogue": false,
"n_mm": 1,
"sdpa": false,
"conv": true,
"reduce": false,
"masky": false,
"quant": true
},
"metric": "TFLOP/s",
"roofline_us": 640.6499766857143,
"ref_metric": 36.0032
},
{
"name": "causal-video-kv-cache-decode",
"tier": "T3",
"why": "multi-GEMM block: needs a hand-written, pipelined MMA sequence to beat",
"frac": 0.0162,
"analysis": {
"grouped": false,
"custom_conv": true,
"epilogue": false,
"n_mm": 2,
"sdpa": false,
"conv": false,
"reduce": true,
"masky": false,
"quant": false
},
"metric": "TFLOP/s",
"roofline_us": 2503.3523668114285,
"ref_metric": 11.353
},
{
"name": "cfg-distill-single-pass",
"tier": "T2",
"why": "bandwidth kernel with a reduction/gather: shared-memory tiling and a warp reduction",
"frac": 0.0588,
"analysis": {
"grouped": false,
"custom_conv": true,
"epilogue": false,
"n_mm": 0,
"sdpa": false,
"conv": false,
"reduce": true,
"masky": false,
"quant": false
},
"metric": "GB/s",
"roofline_us": 494.4801330566406,
"ref_metric": 282.1732
},
{
"name": "chunked-prefill-attention",
"tier": "T3",
"why": "attention with custom masking/sparsity: no library kernel applies as-is, so the tiled online-softmax pipeline is written by hand",
"frac": 0.0122,
"analysis": {
"grouped": false,
"custom_conv": true,
"epilogue": false,
"n_mm": 4,
"sdpa": false,
"conv": false,
"reduce": true,
"masky": true,
"quant": false
},
"metric": "TFLOP/s",
"roofline_us": 520.7887520914285,
"ref_metric": 8.5226
},
{
"name": "chunked-prefill-gather",
"tier": "T2",
"why": "bandwidth kernel with a reduction/gather: shared-memory tiling and a warp reduction",
"frac": 0.208,
"analysis": {
"grouped": true,
"custom_conv": false,
"epilogue": false,
"n_mm": 0,
"sdpa": false,
"conv": false,
"reduce": true,
"masky": false,
"quant": true
},
"metric": "GB/s",
"roofline_us": 472.1725995962818,
"ref_metric": 998.4066
},
{
"name": "chunked-zloss-cross-entropy",
"tier": "T3",
"why": "grouped / variable-M GEMM: there is no single library call for it, so the per-group tiling and scheduling are the task",
"frac": 0.2702,
"analysis": {
"grouped": true,
"custom_conv": true,
"epilogue": false,
"n_mm": 3,
"sdpa": false,
"conv": false,
"reduce": true,
"masky": true,
"quant": false
},
"metric": "TFLOP/s",
"roofline_us": 113092.62457124572,
"ref_metric": 189.1689
},
{
"name": "clip-logit-scale-fused",
"tier": "T3",
"why": "multi-GEMM block: needs a hand-written, pipelined MMA sequence to beat",
"frac": 0.0525,
"analysis": {
"grouped": false,
"custom_conv": false,
"epilogue": false,
"n_mm": 1,
"sdpa": false,
"conv": false,
"reduce": true,
"masky": false,
"quant": false
},
"metric": "TFLOP/s",
"roofline_us": 785.3654484114286,
"ref_metric": 36.728
},
{
"name": "cogvideox-expert-adaln-block",
"tier": "T3",
"why": "GEMM with a fused activation/gate epilogue: needs a hand-written MMA pipeline",
"frac": 0.0479,
"analysis": {
"grouped": false,
"custom_conv": false,
"epilogue": true,
"n_mm": 1,
"sdpa": false,
"conv": false,
"reduce": true,
"masky": false,
"quant": false
},
"metric": "GB/s",
"roofline_us": 419.771671295166,
"ref_metric": 230.0551
},
{
"name": "cogvideox-joint-3d-attention",
"tier": "T3",
"why": "multi-GEMM block: needs a hand-written, pipelined MMA sequence to beat",
"frac": 0.0263,
"analysis": {
"grouped": false,
"custom_conv": false,
"epilogue": false,
"n_mm": 2,
"sdpa": false,
"conv": false,
"reduce": true,
"masky": false,
"quant": false
},
"metric": "TFLOP/s",
"roofline_us": 5546.911615268571,
"ref_metric": 18.4283
},
{
"name": "cogvideox-shared-ffn-expert-gate",
"tier": "T3",
"why": "GEMM with a fused activation/gate epilogue: needs a hand-written MMA pipeline",
"frac": 0.0643,
"analysis": {
"grouped": false,
"custom_conv": false,
"epilogue": true,
"n_mm": 2,
"sdpa": false,
"conv": false,
"reduce": false,
"masky": false,
"quant": false
},
"metric": "TFLOP/s",
"roofline_us": 3883.1585455542854,
"ref_metric": 44.9937
},
{
"name": "cogvideox-sincos-3d-embed",
"tier": "T1",
"why": "elementwise/bandwidth chain: the win is fusing the passes into one",
"frac": 0.0716,
"analysis": {
"grouped": false,
"custom_conv": false,
"epilogue": false,
"n_mm": 0,
"sdpa": false,
"conv": false,
"reduce": false,
"masky": false,
"quant": false
},
"metric": "GB/s",
"roofline_us": 419.771671295166,
"ref_metric": 343.5859
},
{
"name": "cogvideox-vae-spatial-norm3d",
"tier": "T3",
"why": "attention with custom masking/sparsity: no library kernel applies as-is, so the tiled online-softmax pipeline is written by hand",
"frac": 0.0187,
"analysis": {
"grouped": false,
"custom_conv": true,
"epilogue": false,
"n_mm": 2,
"sdpa": false,
"conv": false,
"reduce": true,
"masky": true,
"quant": false
},
"metric": "GB/s",
"roofline_us": 511.77978515625,
"ref_metric": 89.8228
},
{
"name": "comba-backward",
"tier": "T3",
"why": "attention with custom masking/sparsity: no library kernel applies as-is, so the tiled online-softmax pipeline is written by hand",
"frac": 0.0017,
"analysis": {
"grouped": false,
"custom_conv": false,
"epilogue": false,
"n_mm": 8,
"sdpa": false,
"conv": false,
"reduce": true,
"masky": true,
"quant": false
},
"metric": "TFLOP/s",
"roofline_us": 441.7680647314286,
"ref_metric": 1.1779
},
{
"name": "comba-decode-step",
"tier": "T2",
"why": "bandwidth kernel with a reduction/gather: shared-memory tiling and a warp reduction",
"frac": 0.0926,
"analysis": {
"grouped": false,
"custom_conv": false,
"epilogue": false,
"n_mm": 0,
"sdpa": false,
"conv": false,
"reduce": true,
"masky": false,
"quant": false
},
"metric": "GB/s",
"roofline_us": 473.34909439086914,
"ref_metric": 444.3857
},
{
"name": "comba-forward",
"tier": "T3",
"why": "multi-GEMM block: needs a hand-written, pipelined MMA sequence to beat",
"frac": 0.0001,
"analysis": {
"grouped": false,
"custom_conv": false,
"epilogue": false,
"n_mm": 1,
"sdpa": false,
"conv": false,
"reduce": true,
"masky": false,
"quant": false
},
"metric": "TFLOP/s",
"roofline_us": 220.8840323657143,
"ref_metric": 0.0881
},
{
"name": "compressed-kv-video-attn",
"tier": "T3",
"why": "multi-GEMM block: needs a hand-written, pipelined MMA sequence to beat",
"frac": 0.0266,
"analysis": {
"grouped": false,
"custom_conv": false,
"epilogue": false,
"n_mm": 2,
"sdpa": false,
"conv": false,
"reduce": true,
"masky": false,
"quant": false
},
"metric": "TFLOP/s",
"roofline_us": 1187.2516739657142,
"ref_metric": 18.6145
},
{
"name": "conformer-conv-module",
"tier": "T3",
"why": "convolution with custom padding/grouping/tiling: cuDNN's path for this case is not the fast one, so the tiled kernel is the task",
"frac": 0.0405,
"analysis": {
"grouped": false,
"custom_conv": true,
"epilogue": true,
"n_mm": 3,
"sdpa": false,
"conv": true,
"reduce": true,
"masky": false,
"quant": false
},
"metric": "TFLOP/s",
"roofline_us": 871.5351771428572,
"ref_metric": 28.3327
},
{
"name": "conv3d-layout-transform",
"tier": "T1",
"why": "elementwise/bandwidth chain: the win is fusing the passes into one",
"frac": 0.0469,
"analysis": {
"grouped": false,
"custom_conv": false,
"epilogue": false,
"n_mm": 0,
"sdpa": false,
"conv": false,
"reduce": false,
"masky": false,
"quant": false
},
"metric": "GB/s",
"roofline_us": 358.88671875,
"ref_metric": 225.026
},
{
"name": "conv3d-tiled-decode-blend",
"tier": "T3",
"why": "convolution with custom padding/grouping/tiling: cuDNN's path for this case is not the fast one, so the tiled kernel is the task",
"frac": 0.0358,
"analysis": {
"grouped": false,
"custom_conv": true,
"epilogue": false,
"n_mm": 1,
"sdpa": false,
"conv": true,
"reduce": false,
"masky": false,
"quant": false
},
"metric": "TFLOP/s",
"roofline_us": 943.5026929371428,
"ref_metric": 25.0856
},
{
"name": "cross-attention-varlen",
"tier": "T3",
"why": "grouped / variable-M GEMM: there is no single library call for it, so the per-group tiling and scheduling are the task",
"frac": 0.0256,
"analysis": {
"grouped": true,
"custom_conv": false,
"epilogue": false,
"n_mm": 2,
"sdpa": false,
"conv": false,
"reduce": true,
"masky": false,
"quant": false
},
"metric": "TFLOP/s",
"roofline_us": 571.0966081828572,
"ref_metric": 17.9415
},
{
"name": "cross-layer-fusion-2layer",
"tier": "T3",
"why": "attention with custom masking/sparsity: no library kernel applies as-is, so the tiled online-softmax pipeline is written by hand",
"frac": null,
"analysis": {
"grouped": false,
"custom_conv": false,
"epilogue": true,
"n_mm": 8,
"sdpa": true,
"conv": false,
"reduce": true,
"masky": true,
"quant": false
},
"metric": null,
"roofline_us": null,
"ref_metric": null
},
{
"name": "ddim-inversion-step",
"tier": "T1",
"why": "elementwise/bandwidth chain: the win is fusing the passes into one",
"frac": 0.1082,
"analysis": {
"grouped": false,
"custom_conv": false,
"epilogue": false,
"n_mm": 0,
"sdpa": false,
"conv": false,
"reduce": false,
"masky": false,
"quant": false
},
"metric": "GB/s",
"roofline_us": 593.3761596679688,
"ref_metric": 519.2024
},
{
"name": "deepseek-mla-lowrank-proj",
"tier": "T3",
"why": "multi-GEMM block: needs a hand-written, pipelined MMA sequence to beat",
"frac": 0.0539,
"analysis": {
"grouped": false,
"custom_conv": false,
"epilogue": false,
"n_mm": 3,
"sdpa": false,
"conv": false,
"reduce": true,
"masky": false,
"quant": false
},
"metric": "TFLOP/s",
"roofline_us": 1237.8709313828572,
"ref_metric": 37.7497
},
{
"name": "deepseek-mla-proj-backward",
"tier": "T3",
"why": "multi-GEMM block: needs a hand-written, pipelined MMA sequence to beat",
"frac": 0.0426,
"analysis": {
"grouped": false,
"custom_conv": false,
"epilogue": false,
"n_mm": 3,
"sdpa": false,
"conv": false,
"reduce": true,
"masky": false,
"quant": false
},
"metric": "TFLOP/s",
"roofline_us": 2830.0766646857146,
"ref_metric": 29.8194
},
{
"name": "deepseek-mla-vabsorb-outproj",
"tier": "T3",
"why": "MLA V-absorb folds the value projection into the output projection: a fused GEMM pair, not a plain one",
"frac": 0.0579,
"analysis": {
"grouped": false,
"custom_conv": false,
"epilogue": false,
"n_mm": 2,
"sdpa": false,
"conv": false,
"reduce": false,
"masky": false,
"quant": false
},
"metric": "TFLOP/s",
"roofline_us": 736.2801078857143,
"ref_metric": 40.5305
},
{
"name": "deepseek-node-dispatch-pack",
"tier": "T2",
"why": "bandwidth kernel with a reduction/gather: shared-memory tiling and a warp reduction",
"frac": 0.2865,
"analysis": {
"grouped": true,
"custom_conv": false,
"epilogue": false,
"n_mm": 0,
"sdpa": false,
"conv": false,
"reduce": true,
"masky": true,
"quant": true
},
"metric": "GB/s",
"roofline_us": 456.0343556416531,
"ref_metric": 1375.1752
},
{
"name": "deepseek-v32-indexer-decode",
"tier": "T3",
"why": "multi-GEMM block: needs a hand-written, pipelined MMA sequence to beat",
"frac": 0.0335,
"analysis": {
"grouped": false,
"custom_conv": true,
"epilogue": false,
"n_mm": 1,
"sdpa": false,
"conv": false,
"reduce": true,
"masky": false,
"quant": false
},
"metric": "GB/s",
"roofline_us": 314.6203358968099,
"ref_metric": 160.7546
},
{
"name": "deepseek-v32-sparse-mla-decode",
"tier": "T3",
"why": "multi-GEMM block: needs a hand-written, pipelined MMA sequence to beat",
"frac": 0.0257,
"analysis": {
"grouped": false,
"custom_conv": true,
"epilogue": false,
"n_mm": 2,
"sdpa": false,
"conv": false,
"reduce": true,
"masky": false,
"quant": false
},
"metric": "GB/s",
"roofline_us": 570.6787109375,
"ref_metric": 123.543
},
{
"name": "delta-product-backward",
"tier": "T3",
"why": "attention with custom masking/sparsity: no library kernel applies as-is, so the tiled online-softmax pipeline is written by hand",
"frac": 0.0007,
"analysis": {
"grouped": false,
"custom_conv": false,
"epilogue": false,
"n_mm": 8,
"sdpa": false,
"conv": false,
"reduce": true,
"masky": true,
"quant": false
},
"metric": "TFLOP/s",
"roofline_us": 398.81839177142854,
"ref_metric": 0.5091
},
{
"name": "delta-rule-forward",
"tier": "T3",
"why": "multi-GEMM block: needs a hand-written, pipelined MMA sequence to beat",
"frac": 0.0003,
"analysis": {
"grouped": false,
"custom_conv": false,
"epilogue": false,
"n_mm": 1,
"sdpa": false,
"conv": false,
"reduce": true,
"masky": false,
"quant": false
},
"metric": "TFLOP/s",
"roofline_us": 539.9387457828572,
"ref_metric": 0.2231
},
{
"name": "deltaformer-backward",
"tier": "T3",
"why": "attention with custom masking/sparsity: no library kernel applies as-is, so the tiled online-softmax pipeline is written by hand",
"frac": 0.0033,
"analysis": {
"grouped": false,
"custom_conv": true,
"epilogue": false,
"n_mm": 4,
"sdpa": false,
"conv": false,
"reduce": true,
"masky": true,
"quant": false
},
"metric": "TFLOP/s",
"roofline_us": 1570.7308968228572,
"ref_metric": 2.3119
},
{
"name": "deltaformer-forward",
"tier": "T3",
"why": "attention with custom masking/sparsity: no library kernel applies as-is, so the tiled online-softmax pipeline is written by hand",
"frac": 0.0133,
"analysis": {
"grouped": false,
"custom_conv": true,
"epilogue": false,
"n_mm": 4,
"sdpa": false,
"conv": false,
"reduce": true,
"masky": true,
"quant": false
},
"metric": "TFLOP/s",
"roofline_us": 3141.4617936457144,
"ref_metric": 9.3324
},
{
"name": "depthwise-separable-conv3d",
"tier": "T3",
"why": "convolution with custom padding/grouping/tiling: cuDNN's path for this case is not the fast one, so the tiled kernel is the task",
"frac": 0.0209,
"analysis": {
"grouped": false,
"custom_conv": true,
"epilogue": true,
"n_mm": 2,
"sdpa": false,
"conv": true,
"reduce": false,
"masky": false,
"quant": false
},
"metric": "TFLOP/s",
"roofline_us": 396.33420288,
"ref_metric": 14.6111
},
{
"name": "diff-attention",
"tier": "T3",
"why": "attention with custom masking/sparsity: no library kernel applies as-is, so the tiled online-softmax pipeline is written by hand",
"frac": 0.0148,
"analysis": {
"grouped": false,
"custom_conv": true,
"epilogue": false,
"n_mm": 2,
"sdpa": false,
"conv": false,
"reduce": true,
"masky": true,
"quant": false
},
"metric": "TFLOP/s",
"roofline_us": 1570.7308968228572,
"ref_metric": 10.3476
},
{
"name": "dist-allgather-gemm-overlap",
"tier": "T3",
"why": "2-GPU compute/communication overlap: the GEMM is a library call but the overlap schedule is the task",
"frac": null,
"analysis": {
"grouped": false,
"custom_conv": true,
"epilogue": false,
"n_mm": 1,
"sdpa": false,
"conv": false,
"reduce": false,
"masky": false,
"quant": false
},
"metric": "TFLOP/s",
"roofline_us": 1570.7308968228572,
"ref_metric": null
},
{
"name": "dist-allreduce-rmsnorm",
"tier": "T2",
"why": "bandwidth kernel with a reduction/gather: shared-memory tiling and a warp reduction",
"frac": null,
"analysis": {
"grouped": false,
"custom_conv": false,
"epilogue": false,
"n_mm": 0,
"sdpa": false,
"conv": false,
"reduce": true,
"masky": false,
"quant": false
},
"metric": "GB/s",
"roofline_us": 14062.5,
"ref_metric": null
},
{
"name": "dist-moe-a2a-dispatch",
"tier": "T2",
"why": "bandwidth kernel with a reduction/gather: shared-memory tiling and a warp reduction",
"frac": null,
"analysis": {
"grouped": true,
"custom_conv": false,
"epilogue": false,
"n_mm": 0,
"sdpa": false,
"conv": false,
"reduce": true,
"masky": false,
"quant": true
},
"metric": "GB/s",
"roofline_us": 4687.5,
"ref_metric": null
},
{
"name": "dist-oneshot-allreduce",
"tier": "T1",
"why": "elementwise/bandwidth chain: the win is fusing the passes into one",
"frac": null,
"analysis": {
"grouped": false,
"custom_conv": false,
"epilogue": false,
"n_mm": 0,
"sdpa": false,
"conv": false,
"reduce": false,
"masky": false,
"quant": false
},
"metric": "GB/s",
"roofline_us": 3750.0,
"ref_metric": null
},
{
"name": "dist-reduce-scatter-rmsnorm",
"tier": "T2",
"why": "bandwidth kernel with a reduction/gather: shared-memory tiling and a warp reduction",
"frac": null,
"analysis": {
"grouped": false,
"custom_conv": false,
"epilogue": false,
"n_mm": 0,
"sdpa": false,
"conv": false,
"reduce": true,
"masky": false,
"quant": false
},
"metric": "GB/s",
"roofline_us": 8437.5,
"ref_metric": null
},
{
"name": "dist-tp-embedding-allreduce",
"tier": "T2",
"why": "bandwidth kernel with a reduction/gather: shared-memory tiling and a warp reduction",
"frac": null,
"analysis": {
"grouped": false,
"custom_conv": false,
"epilogue": false,
"n_mm": 0,
"sdpa": false,
"conv": false,
"reduce": false,
"masky": true,
"quant": false
},
"metric": "GB/s",
"roofline_us": 14062.5,
"ref_metric": null
},
{
"name": "distill-kl-logits-fused",
"tier": "T2",
"why": "bandwidth kernel with a reduction/gather: shared-memory tiling and a warp reduction",
"frac": 0.0218,
"analysis": {
"grouped": false,
"custom_conv": true,
"epilogue": false,
"n_mm": 0,
"sdpa": false,
"conv": false,
"reduce": true,
"masky": false,
"quant": false
},
"metric": "GB/s",
"roofline_us": 1223.14453125,
"ref_metric": 104.5081
},
{
"name": "dora-magnitude-decompose",
"tier": "T3",
"why": "multi-GEMM block: needs a hand-written, pipelined MMA sequence to beat",
"frac": 0.2518,
"analysis": {
"grouped": false,
"custom_conv": false,
"epilogue": false,
"n_mm": 2,
"sdpa": false,
"conv": false,
"reduce": true,
"masky": false,
"quant": false
},
"metric": "TFLOP/s",
"roofline_us": 813.1676920685715,
"ref_metric": 176.2439
},
{
"name": "dpmsolverpp-step",
"tier": "T2",
"why": "bandwidth kernel with a reduction/gather: shared-memory tiling and a warp reduction",
"frac": 0.0478,
"analysis": {
"grouped": false,
"custom_conv": false,
"epilogue": false,
"n_mm": 0,
"sdpa": false,
"conv": false,
"reduce": false,
"masky": true,
"quant": false
},
"metric": "GB/s",
"roofline_us": 494.4801330566406,
"ref_metric": 229.6033
},
{
"name": "dpo-loss-fused",
"tier": "T2",
"why": "bandwidth kernel with a reduction/gather: shared-memory tiling and a warp reduction",
"frac": 0.0482,
"analysis": {
"grouped": false,
"custom_conv": true,
"epilogue": true,
"n_mm": 0,
"sdpa": false,
"conv": false,
"reduce": true,
"masky": true,
"quant": false
},
"metric": "GB/s",
"roofline_us": 1448.974609375,
"ref_metric": 231.4997
},
{
"name": "draft-tree-build",
"tier": "T2",
"why": "bandwidth kernel with a reduction/gather: shared-memory tiling and a warp reduction",
"frac": 0.0126,
"analysis": {
"grouped": false,
"custom_conv": false,
"epilogue": false,
"n_mm": 0,
"sdpa": false,
"conv": false,
"reduce": true,
"masky": true,
"quant": true
},
"metric": "GB/s",
"roofline_us": 478.604007512331,
"ref_metric": 60.5123
},
{
"name": "dsa-sparse-attention",
"tier": "T3",
"why": "attention with custom masking/sparsity: no library kernel applies as-is, so the tiled online-softmax pipeline is written by hand",
"frac": 0.0061,
"analysis": {
"grouped": false,
"custom_conv": true,
"epilogue": false,
"n_mm": 3,
"sdpa": false,
"conv": false,
"reduce": true,
"masky": true,
"quant": false
},
"metric": "TFLOP/s",
"roofline_us": 294.5120431542857,
"ref_metric": 4.2646
},
{
"name": "dyt-dynamic-tanh-backward",
"tier": "T2",
"why": "bandwidth kernel with a reduction/gather: shared-memory tiling and a warp reduction",
"frac": 0.0323,
"analysis": {
"grouped": false,
"custom_conv": false,
"epilogue": true,
"n_mm": 0,
"sdpa": false,
"conv": false,
"reduce": true,
"masky": false,
"quant": false
},
"metric": "GB/s",
"roofline_us": 703.1393051147461,
"ref_metric": 155.2228
},
{
"name": "dyt-dynamic-tanh-norm",
"tier": "T1",
"why": "elementwise/bandwidth chain: the win is fusing the passes into one",
"frac": 0.0482,
"analysis": {
"grouped": false,
"custom_conv": false,
"epilogue": true,
"n_mm": 0,
"sdpa": false,
"conv": false,
"reduce": false,
"masky": false,
"quant": false
},
"metric": "GB/s",
"roofline_us": 468.75953674316406,
"ref_metric": 231.458
},
{
"name": "embedding-backward-scatter",
"tier": "T2",
"why": "bandwidth kernel with a reduction/gather: shared-memory tiling and a warp reduction",
"frac": 0.0542,
"analysis": {
"grouped": true,
"custom_conv": false,
"epilogue": false,
"n_mm": 0,
"sdpa": false,
"conv": false,
"reduce": true,
"masky": false,
"quant": false
},
"metric": "GB/s",
"roofline_us": 1041.8701171875,
"ref_metric": 260.3287
},
{
"name": "entropy-bonus-reduce",
"tier": "T2",
"why": "bandwidth kernel with a reduction/gather: shared-memory tiling and a warp reduction",
"frac": 0.0234,
"analysis": {
"grouped": false,
"custom_conv": true,
"epilogue": false,
"n_mm": 0,
"sdpa": false,
"conv": false,
"reduce": true,
"masky": false,
"quant": false
},
"metric": "GB/s",
"roofline_us": 1223.14453125,
"ref_metric": 112.5576
},
{
"name": "feature-cache-blend",
"tier": "T1",
"why": "elementwise/bandwidth chain: the win is fusing the passes into one",
"frac": 0.0524,
"analysis": {
"grouped": false,
"custom_conv": true,
"epilogue": false,
"n_mm": 0,
"sdpa": false,
"conv": false,
"reduce": false,
"masky": false,
"quant": false
},
"metric": "GB/s",
"roofline_us": 741.7201995849609,
"ref_metric": 251.3262
},
{
"name": "flash-attn-backward",
"tier": "T3",
"why": "attention with custom masking/sparsity: no library kernel applies as-is, so the tiled online-softmax pipeline is written by hand",
"frac": null,
"analysis": {
"grouped": false,
"custom_conv": true,
"epilogue": false,
"n_mm": 5,
"sdpa": false,
"conv": false,
"reduce": true,
"masky": true,
"quant": false
},
"metric": null,
"roofline_us": null,
"ref_metric": 0.0
},
{
"name": "flex-blockmask-attention",
"tier": "T3",
"why": "grouped / variable-M GEMM: there is no single library call for it, so the per-group tiling and scheduling are the task",
"frac": 0.0081,
"analysis": {
"grouped": true,
"custom_conv": true,
"epilogue": false,
"n_mm": 2,
"sdpa": false,
"conv": false,
"reduce": true,
"masky": true,
"quant": false
},
"metric": "TFLOP/s",
"roofline_us": 583.0801583542857,
"ref_metric": 5.6462
},
{
"name": "flow-match-euler-step",
"tier": "T1",
"why": "elementwise/bandwidth chain: the win is fusing the passes into one",
"frac": 0.0709,
"analysis": {
"grouped": false,
"custom_conv": false,
"epilogue": false,
"n_mm": 0,
"sdpa": false,
"conv": false,
"reduce": false,
"masky": false,
"quant": false
},
"metric": "GB/s",
"roofline_us": 494.4801330566406,
"ref_metric": 340.4479
},
{
"name": "flow-match-loss-backward",
"tier": "T2",
"why": "bandwidth kernel with a reduction/gather: shared-memory tiling and a warp reduction",
"frac": 0.0555,
"analysis": {
"grouped": false,
"custom_conv": false,
"epilogue": false,
"n_mm": 0,
"sdpa": false,
"conv": false,
"reduce": true,
"masky": false,
"quant": false
},
"metric": "GB/s",
"roofline_us": 544.0735816955566,
"ref_metric": 266.289
},
{
"name": "flux-attention-backward",
"tier": "T3",
"why": "multi-GEMM block: needs a hand-written, pipelined MMA sequence to beat",
"frac": 0.0333,
"analysis": {
"grouped": false,
"custom_conv": false,
"epilogue": false,
"n_mm": 5,
"sdpa": false,
"conv": false,
"reduce": true,
"masky": false,
"quant": false
},
"metric": "TFLOP/s",
"roofline_us": 7190.235428571429,
"ref_metric": 23.3135
},
{
"name": "flux-attn-fp8",
"tier": "T3",
"why": "quantised matmul: cuBLAS will not fuse the dequant, so the MMA pipeline is hand-written -- async copy, double buffering, fragment layouts",
"frac": 0.0382,
"analysis": {
"grouped": false,
"custom_conv": false,
"epilogue": false,
"n_mm": 2,
"sdpa": false,
"conv": false,
"reduce": true,
"masky": false,
"quant": true
},
"metric": "TFLOP/s",
"roofline_us": 5752.188342857143,
"ref_metric": 26.7575
},
{
"name": "flux-double-stream-block",
"tier": "T3",
"why": "GEMM with a fused activation/gate epilogue: needs a hand-written MMA pipeline",
"frac": 0.0519,
"analysis": {
"grouped": false,
"custom_conv": false,
"epilogue": true,
"n_mm": 8,
"sdpa": false,
"conv": false,
"reduce": true,
"masky": false,
"quant": false
},
"metric": "TFLOP/s",
"roofline_us": 10478.18628534857,
"ref_metric": 36.3505
},
{
"name": "flux-dual-stream-mlp",
"tier": "T3",
"why": "GEMM with a fused activation/gate epilogue: needs a hand-written MMA pipeline",
"frac": 0.0629,
"analysis": {
"grouped": false,
"custom_conv": false,
"epilogue": true,
"n_mm": 2,
"sdpa": false,
"conv": false,
"reduce": true,
"masky": false,
"quant": false
},
"metric": "TFLOP/s",
"roofline_us": 5522.1008091428575,
"ref_metric": 44.051
},
{
"name": "flux-final-layer-unpatchify",
"tier": "T3",
"why": "GEMM with a fused activation/gate epilogue: needs a hand-written MMA pipeline",
"frac": 0.0195,
"analysis": {
"grouped": false,
"custom_conv": false,
"epilogue": true,
"n_mm": 2,
"sdpa": false,
"conv": false,
"reduce": true,
"masky": false,
"quant": false
},
"metric": "GB/s",
"roofline_us": 478.515625,
"ref_metric": 93.4332
},
{
"name": "flux-fused-qkv-mlp-proj",
"tier": "T3",
"why": "GEMM with a fused activation/gate epilogue: needs a hand-written MMA pipeline",
"frac": 0.0607,
"analysis": {
"grouped": false,
"custom_conv": false,
"epilogue": true,
"n_mm": 1,
"sdpa": false,
"conv": false,
"reduce": true,
"masky": false,
"quant": false
},
"metric": "TFLOP/s",
"roofline_us": 4831.838208,
"ref_metric": 42.4919
},
{
"name": "flux-joint-attention",
"tier": "T3",
"why": "multi-GEMM block: needs a hand-written, pipelined MMA sequence to beat",
"frac": 0.0385,
"analysis": {
"grouped": false,
"custom_conv": false,
"epilogue": false,
"n_mm": 2,
"sdpa": false,
"conv": false,
"reduce": true,
"masky": false,
"quant": false
},
"metric": "TFLOP/s",
"roofline_us": 5752.188342857143,
"ref_metric": 26.9205
},
{
"name": "flux-modulation-backward",
"tier": "T3",
"why": "GEMM with a fused activation/gate epilogue: needs a hand-written MMA pipeline",
"frac": 0.0321,
"analysis": {
"grouped": false,
"custom_conv": false,
"epilogue": true,
"n_mm": 2,
"sdpa": false,
"conv": false,
"reduce": true,
"masky": false,
"quant": false
},
"metric": "GB/s",
"roofline_us": 395.5078125,
"ref_metric": 154.0267
},
{
"name": "flux-modulation-fused",
"tier": "T3",
"why": "GEMM with a fused activation/gate epilogue: needs a hand-written MMA pipeline",
"frac": 0.0568,
"analysis": {
"grouped": false,
"custom_conv": false,
"epilogue": true,
"n_mm": 2,
"sdpa": false,
"conv": false,
"reduce": true,
"masky": false,
"quant": false
},
"metric": "GB/s",
"roofline_us": 366.2109375,
"ref_metric": 272.4412
},
{
"name": "flux-qk-rmsnorm-rope",
"tier": "T2",
"why": "bandwidth kernel with a reduction/gather: shared-memory tiling and a warp reduction",
"frac": 0.0277,
"analysis": {
"grouped": false,
"custom_conv": false,
"epilogue": false,
"n_mm": 0,
"sdpa": false,
"conv": false,
"reduce": true,
"masky": false,
"quant": false
},
"metric": "GB/s",
"roofline_us": 395.5078125,
"ref_metric": 132.7251
},
{
"name": "flux-rope2d-apply",
"tier": "T1",
"why": "elementwise/bandwidth chain: the win is fusing the passes into one",
"frac": 0.0391,
"analysis": {
"grouped": false,
"custom_conv": false,
"epilogue": false,
"n_mm": 0,
"sdpa": false,
"conv": false,
"reduce": false,
"masky": false,
"quant": false
},
"metric": "GB/s",
"roofline_us": 366.2109375,
"ref_metric": 187.8583
},
{
"name": "flux-single-stream-block",
"tier": "T3",
"why": "GEMM with a fused activation/gate epilogue: needs a hand-written MMA pipeline",
"frac": 0.0527,
"analysis": {
"grouped": false,
"custom_conv": false,
"epilogue": true,
"n_mm": 5,
"sdpa": false,
"conv": false,
"reduce": true,
"masky": false,
"quant": false
},
"metric": "TFLOP/s",
"roofline_us": 10478.18628534857,
"ref_metric": 36.8941
},
{
"name": "forgetting-attention",
"tier": "T3",
"why": "attention with custom masking/sparsity: no library kernel applies as-is, so the tiled online-softmax pipeline is written by hand",
"frac": 0.0103,
"analysis": {
"grouped": false,
"custom_conv": true,
"epilogue": false,
"n_mm": 2,
"sdpa": false,
"conv": false,
"reduce": true,
"masky": true,
"quant": false
},
"metric": "TFLOP/s",
"roofline_us": 785.3654484114286,
"ref_metric": 7.2105
},
{
"name": "forgetting-attn-backward",
"tier": "T3",
"why": "attention with custom masking/sparsity: no library kernel applies as-is, so the tiled online-softmax pipeline is written by hand",
"frac": 0.0091,
"analysis": {
"grouped": false,
"custom_conv": true,
"epilogue": false,
"n_mm": 2,
"sdpa": false,
"conv": false,
"reduce": true,
"masky": true,
"quant": false
},
"metric": "TFLOP/s",
"roofline_us": 883.5361294628572,
"ref_metric": 6.4016
},
{
"name": "fp4-stochastic-round",
"tier": "T2",
"why": "bandwidth kernel with a reduction/gather: shared-memory tiling and a warp reduction",
"frac": 0.0067,
"analysis": {
"grouped": false,
"custom_conv": false,
"epilogue": false,
"n_mm": 0,
"sdpa": false,
"conv": false,
"reduce": true,
"masky": true,
"quant": true
},
"metric": "GB/s",
"roofline_us": 413.818359375,
"ref_metric": 32.213
},
{
"name": "fp8-attention-backward",
"tier": "T3",
"why": "quantised matmul: cuBLAS will not fuse the dequant, so the MMA pipeline is hand-written -- async copy, double buffering, fragment layouts",
"frac": 0.0159,
"analysis": {
"grouped": false,
"custom_conv": true,
"epilogue": false,
"n_mm": 5,
"sdpa": false,
"conv": false,
"reduce": true,
"masky": true,
"quant": true
},
"metric": "TFLOP/s",
"roofline_us": 1963.4136210285712,
"ref_metric": 11.1141
},
{
"name": "fp8-blockwise-gemm",
"tier": "T3",
"why": "quantised matmul: cuBLAS will not fuse the dequant, so the MMA pipeline is hand-written -- async copy, double buffering, fragment layouts",
"frac": 0.0604,
"analysis": {
"grouped": false,
"custom_conv": false,
"epilogue": false,
"n_mm": 1,
"sdpa": false,
"conv": false,
"reduce": false,
"masky": false,
"quant": true
},
"metric": "TFLOP/s",
"roofline_us": 785.3654484114286,
"ref_metric": 42.2638
},
{
"name": "fp8-flash-attention",
"tier": "T3",
"why": "quantised matmul: cuBLAS will not fuse the dequant, so the MMA pipeline is hand-written -- async copy, double buffering, fragment layouts",
"frac": 0.0143,
"analysis": {
"grouped": false,
"custom_conv": true,
"epilogue": false,
"n_mm": 2,
"sdpa": false,
"conv": false,
"reduce": true,
"masky": true,
"quant": true
},
"metric": "TFLOP/s",
"roofline_us": 785.3654484114286,
"ref_metric": 10.0445
},
{
"name": "fp8-grouped-gemm-masked",
"tier": "T3",
"why": "quantised matmul: cuBLAS will not fuse the dequant, so the MMA pipeline is hand-written -- async copy, double buffering, fragment layouts",
"frac": 0.0726,
"analysis": {
"grouped": true,
"custom_conv": false,
"epilogue": false,
"n_mm": 1,
"sdpa": false,
"conv": false,
"reduce": false,
"masky": true,
"quant": true
},
"metric": "TFLOP/s",
"roofline_us": 687.19476736,
"ref_metric": 50.7868
},
{
"name": "fp8-kv-decode-attention",
"tier": "T3",
"why": "quantised matmul: cuBLAS will not fuse the dequant, so the MMA pipeline is hand-written -- async copy, double buffering, fragment layouts",
"frac": 0.0052,
"analysis": {
"grouped": false,
"custom_conv": false,
"epilogue": false,
"n_mm": 2,
"sdpa": false,
"conv": false,
"reduce": true,
"masky": false,
"quant": true
},
"metric": "GB/s",
"roofline_us": 214.84375,
"ref_metric": 24.9763
},
{
"name": "fp8-per-token-quant-transpose",
"tier": "T2",
"why": "bandwidth kernel with a reduction/gather: shared-memory tiling and a warp reduction",
"frac": 0.0238,
"analysis": {
"grouped": false,
"custom_conv": false,
"epilogue": false,
"n_mm": 0,
"sdpa": false,
"conv": false,
"reduce": true,
"masky": false,
"quant": true
},
"metric": "GB/s",
"roofline_us": 423.17708333333337,
"ref_metric": 114.3638
},
{
"name": "fp8-quant-dequant-fused",
"tier": "T2",
"why": "bandwidth kernel with a reduction/gather: shared-memory tiling and a warp reduction",
"frac": 0.0496,
"analysis": {
"grouped": false,
"custom_conv": false,
"epilogue": false,
"n_mm": 0,
"sdpa": false,
"conv": false,
"reduce": true,
"masky": false,
"quant": true
},
"metric": "GB/s",
"roofline_us": 366.2109375,
"ref_metric": 237.8472
},
{
"name": "frame-anchor-attention",
"tier": "T3",
"why": "multi-GEMM block: needs a hand-written, pipelined MMA sequence to beat",
"frac": 0.0283,
"analysis": {
"grouped": false,
"custom_conv": false,
"epilogue": false,
"n_mm": 2,
"sdpa": false,
"conv": false,
"reduce": true,
"masky": false,
"quant": false
},
"metric": "TFLOP/s",
"roofline_us": 3779.5712204799997,
"ref_metric": 19.7999
},
{
"name": "fsdp-allgather-unflatten",
"tier": "T2",
"why": "bandwidth kernel with a reduction/gather: shared-memory tiling and a warp reduction",
"frac": 0.1541,
"analysis": {
"grouped": true,
"custom_conv": false,
"epilogue": false,
"n_mm": 0,
"sdpa": false,
"conv": false,
"reduce": true,
"masky": false,
"quant": false
},
"metric": "GB/s",
"roofline_us": 400.10035658876103,
"ref_metric": 739.7784
},
{
"name": "fsdp-flatten-shard-params",
"tier": "T1",
"why": "elementwise/bandwidth chain: the win is fusing the passes into one",
"frac": 0.402,
"analysis": {
"grouped": false,
"custom_conv": false,
"epilogue": false,
"n_mm": 0,
"sdpa": false,
"conv": false,
"reduce": false,
"masky": false,
"quant": true
},
"metric": "GB/s",
"roofline_us": 585.1526061693827,
"ref_metric": 1929.6087
},
{
"name": "fsdp-mixed-precision-cast",
"tier": "T2",
"why": "bandwidth kernel with a reduction/gather: shared-memory tiling and a warp reduction",
"frac": 0.0701,
"analysis": {
"grouped": false,
"custom_conv": false,
"epilogue": false,
"n_mm": 0,
"sdpa": false,
"conv": false,
"reduce": true,
"masky": false,
"quant": true
},
"metric": "GB/s",
"roofline_us": 453.2120501001676,
"ref_metric": 336.6286
},
{
"name": "fsdp-reduce-scatter-bucket",
"tier": "T2",
"why": "bandwidth kernel with a reduction/gather: shared-memory tiling and a warp reduction",
"frac": 0.0554,
"analysis": {
"grouped": false,
"custom_conv": false,
"epilogue": false,
"n_mm": 0,
"sdpa": false,
"conv": false,
"reduce": true,
"masky": false,
"quant": false
},
"metric": "GB/s",
"roofline_us": 568.7585814545553,
"ref_metric": 265.7684
},
{
"name": "fused-adamw-multitensor",
"tier": "T1",
"why": "elementwise/bandwidth chain: the win is fusing the passes into one",
"frac": 0.0655,
"analysis": {
"grouped": true,
"custom_conv": false,
"epilogue": false,
"n_mm": 0,
"sdpa": false,
"conv": false,
"reduce": false,
"masky": false,
"quant": false
},
"metric": "GB/s",
"roofline_us": 4050.701856613159,
"ref_metric": 314.3086
},
{
"name": "fused-linear-cross-entropy",
"tier": "T3",
"why": "attention with custom masking/sparsity: no library kernel applies as-is, so the tiled online-softmax pipeline is written by hand",
"frac": 0.3054,
"analysis": {
"grouped": false,
"custom_conv": true,
"epilogue": false,
"n_mm": 3,
"sdpa": false,
"conv": false,
"reduce": true,
"masky": true,
"quant": false
},
"metric": "TFLOP/s",
"roofline_us": 221325.80043044573,
"ref_metric": 213.7734
},
{
"name": "fused-muon-newton-schulz-batched",
"tier": "T3",
"why": "multi-GEMM block: needs a hand-written, pipelined MMA sequence to beat",
"frac": 0.0508,
"analysis": {
"grouped": false,
"custom_conv": false,
"epilogue": false,
"n_mm": 3,
"sdpa": false,
"conv": false,
"reduce": true,
"masky": false,
"quant": false
},
"metric": "TFLOP/s",
"roofline_us": 6626.520970971428,
"ref_metric": 35.531
},
{
"name": "fused-qk-norm-rope",
"tier": "T2",
"why": "bandwidth kernel with a reduction/gather: shared-memory tiling and a warp reduction",
"frac": 0.0281,
"analysis": {
"grouped": false,
"custom_conv": false,
"epilogue": false,
"n_mm": 0,
"sdpa": false,
"conv": false,
"reduce": true,
"masky": false,
"quant": false
},
"metric": "GB/s",
"roofline_us": 361.32822434107464,
"ref_metric": 134.6813
},
{
"name": "fused-residual-rmsnorm",
"tier": "T2",
"why": "bandwidth kernel with a reduction/gather: shared-memory tiling and a warp reduction",
"frac": 0.0678,
"analysis": {
"grouped": false,
"custom_conv": false,
"epilogue": false,
"n_mm": 0,
"sdpa": false,
"conv": false,
"reduce": true,
"masky": false,
"quant": false
},
"metric": "GB/s",
"roofline_us": 312.50476837158203,
"ref_metric": 325.4356
},
{
"name": "fused-rmsnorm-fp8-quant",
"tier": "T2",
"why": "bandwidth kernel with a reduction/gather: shared-memory tiling and a warp reduction",
"frac": 0.0245,
"analysis": {
"grouped": false,
"custom_conv": false,
"epilogue": false,
"n_mm": 0,
"sdpa": false,
"conv": false,
"reduce": true,
"masky": false,
"quant": true
},
"metric": "GB/s",
"roofline_us": 473.6328125,
"ref_metric": 117.4205
},
{
"name": "fused-topk-topp-filter",
"tier": "T2",
"why": "bandwidth kernel with a reduction/gather: shared-memory tiling and a warp reduction",
"frac": 0.0121,
"analysis": {
"grouped": false,
"custom_conv": false,
"epilogue": false,
"n_mm": 0,
"sdpa": false,
"conv": false,
"reduce": true,
"masky": true,
"quant": false
},
"metric": "GB/s",
"roofline_us": 416.6698455810547,
"ref_metric": 58.1855
},
{
"name": "gae-advantage-scan",
"tier": "T1",
"why": "elementwise/bandwidth chain: the win is fusing the passes into one",
"frac": 0.0009,
"analysis": {
"grouped": false,
"custom_conv": false,
"epilogue": false,
"n_mm": 0,
"sdpa": false,
"conv": false,
"reduce": false,
"masky": false,
"quant": false
},
"metric": "GB/s",
"roofline_us": 546.875,
"ref_metric": 4.3194
},
{
"name": "gated-delta-product-forward",
"tier": "T2",
"why": "bandwidth kernel with a reduction/gather: shared-memory tiling and a warp reduction",
"frac": 0.0003,
"analysis": {
"grouped": false,
"custom_conv": false,
"epilogue": false,
"n_mm": 0,
"sdpa": false,
"conv": false,
"reduce": true,
"masky": false,
"quant": false
},
"metric": "TFLOP/s",
"roofline_us": 478.58207012571427,
"ref_metric": 0.2277
},
{
"name": "gated-deltanet-backward",
"tier": "T3",
"why": "attention with custom masking/sparsity: no library kernel applies as-is, so the tiled online-softmax pipeline is written by hand",
"frac": null,
"analysis": {
"grouped": false,
"custom_conv": false,
"epilogue": false,
"n_mm": 8,
"sdpa": false,
"conv": false,
"reduce": true,
"masky": true,
"quant": false
},
"metric": null,
"roofline_us": null,
"ref_metric": 1.5324
},
{
"name": "gated-deltanet-decode-step",
"tier": "T2",
"why": "bandwidth kernel with a reduction/gather: shared-memory tiling and a warp reduction",
"frac": 0.0926,
"analysis": {
"grouped": false,
"custom_conv": false,
"epilogue": false,
"n_mm": 0,
"sdpa": false,
"conv": false,
"reduce": true,
"masky": false,
"quant": false
},
"metric": "GB/s",
"roofline_us": 472.43356704711914,
"ref_metric": 444.6262
},
{
"name": "gated-deltanet-forward",
"tier": "T3",
"why": "attention with custom masking/sparsity: no library kernel applies as-is, so the tiled online-softmax pipeline is written by hand",
"frac": null,
"analysis": {
"grouped": false,
"custom_conv": true,
"epilogue": false,
"n_mm": 8,
"sdpa": false,
"conv": false,
"reduce": true,
"masky": true,
"quant": false
},
"metric": null,
"roofline_us": null,
"ref_metric": 0.0
},
{
"name": "geglu-backward-fused",
"tier": "T2",
"why": "bandwidth kernel with a reduction/gather: shared-memory tiling and a warp reduction",
"frac": 0.022,
"analysis": {
"grouped": false,
"custom_conv": false,
"epilogue": true,
"n_mm": 0,
"sdpa": false,
"conv": false,
"reduce": true,
"masky": false,
"quant": false
},
"metric": "GB/s",
"roofline_us": 781.2754313151042,
"ref_metric": 105.7764
},
{
"name": "gla-backward",
"tier": "T3",
"why": "attention with custom masking/sparsity: no library kernel applies as-is, so the tiled online-softmax pipeline is written by hand",
"frac": 0.0008,
"analysis": {
"grouped": false,
"custom_conv": false,
"epilogue": false,
"n_mm": 4,
"sdpa": false,
"conv": false,
"reduce": true,
"masky": true,
"quant": false
},
"metric": "TFLOP/s",
"roofline_us": 441.7680647314286,
"ref_metric": 0.5921
},
{
"name": "gla-decode-step",
"tier": "T2",
"why": "bandwidth kernel with a reduction/gather: shared-memory tiling and a warp reduction",
"frac": 0.1227,
"analysis": {
"grouped": false,
"custom_conv": false,
"epilogue": false,
"n_mm": 0,
"sdpa": false,
"conv": false,
"reduce": true,
"masky": false,
"quant": false
},
"metric": "GB/s",
"roofline_us": 474.2431640625,
"ref_metric": 589.0488
},
{
"name": "gla-forward",
"tier": "T2",
"why": "bandwidth kernel with a reduction/gather: shared-memory tiling and a warp reduction",
"frac": 0.0005,
"analysis": {
"grouped": false,
"custom_conv": false,
"epilogue": false,
"n_mm": 0,
"sdpa": false,
"conv": false,
"reduce": true,
"masky": false,
"quant": false
},
"metric": "TFLOP/s",
"roofline_us": 441.7680647314286,
"ref_metric": 0.334
},
{
"name": "gptq-actorder-gemm",
"tier": "T3",
"why": "quantised matmul: cuBLAS will not fuse the dequant, so the MMA pipeline is hand-written -- async copy, double buffering, fragment layouts",
"frac": 0.057,
"analysis": {
"grouped": false,
"custom_conv": false,
"epilogue": false,
"n_mm": 1,
"sdpa": false,
"conv": false,
"reduce": false,
"masky": false,
"quant": true
},
"metric": "TFLOP/s",
"roofline_us": 785.3654484114286,
"ref_metric": 39.9084
},
{
"name": "gqa-decode-attention",
"tier": "T3",
"why": "multi-GEMM block: needs a hand-written, pipelined MMA sequence to beat",
"frac": 0.036,
"analysis": {
"grouped": false,
"custom_conv": true,
"epilogue": false,
"n_mm": 2,
"sdpa": false,
"conv": false,
"reduce": true,
"masky": false,
"quant": false
},
"metric": "GB/s",
"roofline_us": 833.3333333333334,
"ref_metric": 172.887
},
{
"name": "gqa-paged-decode-attention",
"tier": "T3",
"why": "attention with custom masking/sparsity: no library kernel applies as-is, so the tiled online-softmax pipeline is written by hand",
"frac": 0.0232,
"analysis": {
"grouped": false,
"custom_conv": true,
"epilogue": false,
"n_mm": 2,
"sdpa": false,
"conv": false,
"reduce": true,
"masky": true,
"quant": false
},
"metric": "GB/s",
"roofline_us": 626.373291015625,
"ref_metric": 111.225
},
{
"name": "grad-global-norm-clip",
"tier": "T2",
"why": "bandwidth kernel with a reduction/gather: shared-memory tiling and a warp reduction",
"frac": 0.0794,
"analysis": {
"grouped": true,
"custom_conv": false,
"epilogue": false,
"n_mm": 0,
"sdpa": false,
"conv": false,
"reduce": true,
"masky": false,
"quant": false
},
"metric": "GB/s",
"roofline_us": 2842.398484547933,
"ref_metric": 381.1733
},
{
"name": "grad-topk-error-feedback",
"tier": "T2",
"why": "bandwidth kernel with a reduction/gather: shared-memory tiling and a warp reduction",
"frac": 0.0462,
"analysis": {
"grouped": false,
"custom_conv": false,
"epilogue": false,
"n_mm": 0,
"sdpa": false,
"conv": false,
"reduce": true,
"masky": true,
"quant": true
},
"metric": "GB/s",
"roofline_us": 319.0104166666667,
"ref_metric": 221.5401
},
{
"name": "gradient-accumulation-fused",
"tier": "T1",
"why": "elementwise/bandwidth chain: the win is fusing the passes into one",
"frac": 0.1379,
"analysis": {
"grouped": false,
"custom_conv": false,
"epilogue": false,
"n_mm": 0,
"sdpa": false,
"conv": false,
"reduce": false,
"masky": false,
"quant": false
},
"metric": "GB/s",
"roofline_us": 2095.4757928848267,
"ref_metric": 661.882
},
{
"name": "grammar-jump-ahead-compile",
"tier": "T2",
"why": "bandwidth kernel with a reduction/gather: shared-memory tiling and a warp reduction",
"frac": 0.0113,
"analysis": {
"grouped": true,
"custom_conv": false,
"epilogue": false,
"n_mm": 0,
"sdpa": false,
"conv": false,
"reduce": true,
"masky": true,
"quant": true
},
"metric": "GB/s",
"roofline_us": 417.3787434895833,
"ref_metric": 54.1914
},
{
"name": "gridwide-barrier-primitive",
"tier": "T2",
"why": "bandwidth kernel with a reduction/gather: shared-memory tiling and a warp reduction",
"frac": null,
"analysis": {
"grouped": false,
"custom_conv": false,
"epilogue": false,
"n_mm": 0,
"sdpa": false,
"conv": false,
"reduce": true,
"masky": false,
"quant": true
},
"metric": null,
"roofline_us": null,
"ref_metric": null
},
{
"name": "grouped-fp8-gemm",
"tier": "T3",
"why": "quantised matmul: cuBLAS will not fuse the dequant, so the MMA pipeline is hand-written -- async copy, double buffering, fragment layouts",
"frac": null,
"analysis": {
"grouped": true,
"custom_conv": false,
"epilogue": false,
"n_mm": 1,
"sdpa": false,
"conv": false,
"reduce": false,
"masky": false,
"quant": true
},
"metric": null,
"roofline_us": null,
"ref_metric": 0.0
},
{
"name": "groupnorm3d-backward",
"tier": "T2",
"why": "bandwidth kernel with a reduction/gather: shared-memory tiling and a warp reduction",
"frac": 0.0192,
"analysis": {
"grouped": false,
"custom_conv": true,
"epilogue": true,
"n_mm": 0,
"sdpa": false,
"conv": false,
"reduce": true,
"masky": false,
"quant": false
},
"metric": "GB/s",
"roofline_us": 418.701171875,
"ref_metric": 92.1255
},
{
"name": "groupnorm3d-forward",
"tier": "T2",
"why": "bandwidth kernel with a reduction/gather: shared-memory tiling and a warp reduction",
"frac": 0.0309,
"analysis": {
"grouped": false,
"custom_conv": true,
"epilogue": true,
"n_mm": 0,
"sdpa": false,
"conv": false,
"reduce": true,
"masky": false,
"quant": false
},
"metric": "GB/s",
"roofline_us": 370.27994791666663,
"ref_metric": 148.2469
},
{
"name": "grpo-advantage-whiten",
"tier": "T2",
"why": "bandwidth kernel with a reduction/gather: shared-memory tiling and a warp reduction",
"frac": 0.0625,
"analysis": {
"grouped": false,
"custom_conv": false,
"epilogue": false,
"n_mm": 0,
"sdpa": false,
"conv": false,
"reduce": true,
"masky": false,
"quant": false
},
"metric": "GB/s",
"roofline_us": 325.5208333333333,
"ref_metric": 299.8599
},
{
"name": "grpo-logprob-ratio",
"tier": "T2",
"why": "bandwidth kernel with a reduction/gather: shared-memory tiling and a warp reduction",
"frac": 0.0378,
"analysis": {
"grouped": false,
"custom_conv": true,
"epilogue": false,
"n_mm": 0,
"sdpa": false,
"conv": false,
"reduce": true,
"masky": true,
"quant": false
},
"metric": "GB/s",
"roofline_us": 1223.14453125,
"ref_metric": 181.6374
},
{
"name": "gsa-backward",
"tier": "T3",
"why": "attention with custom masking/sparsity: no library kernel applies as-is, so the tiled online-softmax pipeline is written by hand",
"frac": 0.0009,
"analysis": {
"grouped": false,
"custom_conv": false,
"epilogue": false,
"n_mm": 8,
"sdpa": false,
"conv": false,
"reduce": true,
"masky": true,
"quant": false
},
"metric": "TFLOP/s",
"roofline_us": 441.7680647314286,
"ref_metric": 0.6484
},
{
"name": "gsa-forward",
"tier": "T2",
"why": "bandwidth kernel with a reduction/gather: shared-memory tiling and a warp reduction",
"frac": 0.0002,
"analysis": {
"grouped": false,
"custom_conv": false,
"epilogue": false,
"n_mm": 0,
"sdpa": false,
"conv": false,
"reduce": true,
"masky": false,
"quant": false
},
"metric": "TFLOP/s",
"roofline_us": 392.6827242057143,
"ref_metric": 0.1697
},
{
"name": "guided-decoding-mask",
"tier": "T2",
"why": "bandwidth kernel with a reduction/gather: shared-memory tiling and a warp reduction",
"frac": 0.0849,
"analysis": {
"grouped": false,
"custom_conv": false,
"epilogue": false,
"n_mm": 0,
"sdpa": false,
"conv": false,
"reduce": true,
"masky": true,
"quant": true
},
"metric": "GB/s",
"roofline_us": 317.3828125,
"ref_metric": 407.682
},
{
"name": "hgrn2-forward",
"tier": "T2",
"why": "bandwidth kernel with a reduction/gather: shared-memory tiling and a warp reduction",
"frac": 0.0005,
"analysis": {
"grouped": false,
"custom_conv": false,
"epilogue": false,
"n_mm": 0,
"sdpa": false,
"conv": false,
"reduce": true,
"masky": false,
"quant": false
},
"metric": "TFLOP/s",
"roofline_us": 441.7680647314286,
"ref_metric": 0.3181
},
{
"name": "hierarchical-coarse-fine-attn",
"tier": "T3",
"why": "multi-GEMM block: needs a hand-written, pipelined MMA sequence to beat",
"frac": 0.0166,
"analysis": {
"grouped": false,
"custom_conv": true,
"epilogue": false,
"n_mm": 4,
"sdpa": false,
"conv": false,
"reduce": true,
"masky": false,
"quant": false
},
"metric": "TFLOP/s",
"roofline_us": 2278.967588571428,
"ref_metric": 11.6272
},
{
"name": "hifigan-mrf-resblock",
"tier": "T3",
"why": "convolution with custom padding/grouping/tiling: cuDNN's path for this case is not the fast one, so the tiled kernel is the task",
"frac": 0.1416,
"analysis": {
"grouped": false,
"custom_conv": true,
"epilogue": false,
"n_mm": 2,
"sdpa": false,
"conv": true,
"reduce": false,
"masky": false,
"quant": false
},
"metric": "TFLOP/s",
"roofline_us": 517.6969508571428,
"ref_metric": 99.1042
},
{
"name": "hunyuan-attention-backward",
"tier": "T3",
"why": "multi-GEMM block: needs a hand-written, pipelined MMA sequence to beat",
"frac": 0.0266,
"analysis": {
"grouped": false,
"custom_conv": false,
"epilogue": false,
"n_mm": 5,
"sdpa": false,
"conv": false,
"reduce": true,
"masky": false,
"quant": false
},
"metric": "TFLOP/s",
"roofline_us": 9498.157196434286,
"ref_metric": 18.6427
},
{
"name": "hunyuan-attn-fp8",
"tier": "T3",
"why": "quantised matmul: cuBLAS will not fuse the dequant, so the MMA pipeline is hand-written -- async copy, double buffering, fragment layouts",
"frac": 0.0382,
"analysis": {
"grouped": false,
"custom_conv": false,
"epilogue": false,
"n_mm": 2,
"sdpa": false,
"conv": false,
"reduce": true,
"masky": false,
"quant": true
},
"metric": "TFLOP/s",
"roofline_us": 10602.433553554285,
"ref_metric": 26.7682
},
{
"name": "hunyuan-dualstream-attn-proj",
"tier": "T3",
"why": "two per-stream output projections plus per-sample gates: a 2-group GEMM with M=1e5 against M=1e2",
"frac": 0.0601,
"analysis": {
"grouped": false,
"custom_conv": false,
"epilogue": false,
"n_mm": 2,
"sdpa": false,
"conv": false,
"reduce": false,
"masky": false,
"quant": false
},
"metric": "TFLOP/s",
"roofline_us": 2222.6455756799996,
"ref_metric": 42.0512
},
{
"name": "hunyuan-dualstream-block",
"tier": "T3",
"why": "GEMM with a fused activation/gate epilogue: needs a hand-written MMA pipeline",
"frac": 0.0515,
"analysis": {
"grouped": false,
"custom_conv": false,
"epilogue": true,
"n_mm": 8,
"sdpa": false,
"conv": false,
"reduce": true,
"masky": false,
"quant": false
},
"metric": "TFLOP/s",
"roofline_us": 10013.409467245714,
"ref_metric": 36.0544
},
{
"name": "hunyuan-final-layer-unpatchify",
"tier": "T3",
"why": "GEMM with a fused activation/gate epilogue: needs a hand-written MMA pipeline",
"frac": 0.0235,
"analysis": {
"grouped": false,
"custom_conv": false,
"epilogue": true,
"n_mm": 2,
"sdpa": false,
"conv": false,
"reduce": true,
"masky": false,
"quant": false
},
"metric": "GB/s",
"roofline_us": 433.7131977081299,
"ref_metric": 112.7753
},
{
"name": "hunyuan-joint-attention",
"tier": "T3",
"why": "multi-GEMM block: needs a hand-written, pipelined MMA sequence to beat",
"frac": 0.0393,
"analysis": {
"grouped": false,
"custom_conv": false,
"epilogue": false,
"n_mm": 2,
"sdpa": false,
"conv": false,
"reduce": true,
"masky": false,
"quant": false
},
"metric": "TFLOP/s",
"roofline_us": 10657.726464,
"ref_metric": 27.522
},
{
"name": "hunyuan-mmdit-modulation",
"tier": "T3",
"why": "GEMM with a fused activation/gate epilogue: needs a hand-written MMA pipeline",
"frac": 0.0509,
"analysis": {
"grouped": false,
"custom_conv": false,
"epilogue": true,
"n_mm": 2,
"sdpa": false,
"conv": false,
"reduce": true,
"masky": false,
"quant": false
},
"metric": "GB/s",
"roofline_us": 284.46197509765625,
"ref_metric": 244.1265
},
{
"name": "hunyuan-modulation-backward",
"tier": "T2",
"why": "bandwidth kernel with a reduction/gather: shared-memory tiling and a warp reduction",
"frac": 0.0309,
"analysis": {
"grouped": false,
"custom_conv": false,
"epilogue": false,
"n_mm": 0,
"sdpa": false,
"conv": false,
"reduce": true,
"masky": false,
"quant": false
},
"metric": "GB/s",
"roofline_us": 585.9375,
"ref_metric": 148.3044
},
{
"name": "hunyuan-qk-norm-fused",
"tier": "T2",
"why": "bandwidth kernel with a reduction/gather: shared-memory tiling and a warp reduction",
"frac": 0.0547,
"analysis": {
"grouped": false,
"custom_conv": false,
"epilogue": false,
"n_mm": 0,
"sdpa": false,
"conv": false,
"reduce": true,
"masky": false,
"quant": false
},
"metric": "GB/s",
"roofline_us": 853.3859252929688,
"ref_metric": 262.4447
},
{
"name": "hunyuan-rope3d-apply",
"tier": "T1",
"why": "elementwise/bandwidth chain: the win is fusing the passes into one",
"frac": 0.0316,
"analysis": {
"grouped": false,
"custom_conv": false,
"epilogue": false,
"n_mm": 0,
"sdpa": false,
"conv": false,
"reduce": false,
"masky": false,
"quant": false
},
"metric": "GB/s",
"roofline_us": 643.0912017822266,
"ref_metric": 151.4511
},
{
"name": "hunyuan-singlestream-block",
"tier": "T3",
"why": "GEMM with a fused activation/gate epilogue: needs a hand-written MMA pipeline",
"frac": 0.0527,
"analysis": {
"grouped": false,
"custom_conv": false,
"epilogue": true,
"n_mm": 5,
"sdpa": false,
"conv": false,
"reduce": true,
"masky": false,
"quant": false
},
"metric": "TFLOP/s",
"roofline_us": 10013.409467245714,
"ref_metric": 36.8809
},
{
"name": "hunyuan-split-ffn",
"tier": "T3",
"why": "GEMM with a fused activation/gate epilogue: needs a hand-written MMA pipeline",
"frac": 0.0652,
"analysis": {
"grouped": false,
"custom_conv": false,
"epilogue": true,
"n_mm": 2,
"sdpa": false,
"conv": false,
"reduce": false,
"masky": false,
"quant": false
},
"metric": "TFLOP/s",
"roofline_us": 9939.781456457144,
"ref_metric": 45.625
},
{
"name": "hunyuan-split-ffn-backward",
"tier": "T3",
"why": "GEMM with a fused activation/gate epilogue: needs a hand-written MMA pipeline",
"frac": 0.0607,
"analysis": {
"grouped": false,
"custom_conv": false,
"epilogue": true,
"n_mm": 4,
"sdpa": false,
"conv": false,
"reduce": false,
"masky": false,
"quant": false
},
"metric": "TFLOP/s",
"roofline_us": 7951.825165165714,
"ref_metric": 42.5032
},
{
"name": "hunyuan-token-replace-i2v",
"tier": "T3",
"why": "GEMM with a fused activation/gate epilogue: needs a hand-written MMA pipeline",
"frac": 0.0512,
"analysis": {
"grouped": false,
"custom_conv": false,
"epilogue": true,
"n_mm": 2,
"sdpa": false,
"conv": false,
"reduce": true,
"masky": false,
"quant": false
},
"metric": "GB/s",
"roofline_us": 566.4825439453125,
"ref_metric": 245.6268
},
{
"name": "hunyuan-varlen-multiresolution",
"tier": "T3",
"why": "grouped / variable-M GEMM: there is no single library call for it, so the per-group tiling and scheduling are the task",
"frac": 0.0255,
"analysis": {
"grouped": true,
"custom_conv": false,
"epilogue": false,
"n_mm": 2,
"sdpa": false,
"conv": false,
"reduce": true,
"masky": false,
"quant": false
},
"metric": "TFLOP/s",
"roofline_us": 9484.700607634286,
"ref_metric": 17.8453
},
{
"name": "hybrid-layer-output-gate",
"tier": "T3",
"why": "GEMM with a fused activation/gate epilogue: needs a hand-written MMA pipeline",
"frac": 0.0619,
"analysis": {
"grouped": false,
"custom_conv": false,
"epilogue": true,
"n_mm": 2,
"sdpa": false,
"conv": false,
"reduce": true,
"masky": false,
"quant": false
},
"metric": "TFLOP/s",
"roofline_us": 1767.0722589257143,
"ref_metric": 43.3355
},
{
"name": "hybrid-ssm-attn-parallel-decode",
"tier": "T3",
"why": "attention with custom masking/sparsity: no library kernel applies as-is, so the tiled online-softmax pipeline is written by hand",
"frac": 0.0247,
"analysis": {
"grouped": false,
"custom_conv": true,
"epilogue": false,
"n_mm": 3,
"sdpa": false,
"conv": false,
"reduce": true,
"masky": true,
"quant": false
},
"metric": "GB/s",
"roofline_us": 731.3569386800131,
"ref_metric": 118.5879
},
{
"name": "image-token-merge-pixelunshuffle",
"tier": "T2",
"why": "bandwidth kernel with a reduction/gather: shared-memory tiling and a warp reduction",
"frac": 0.0725,
"analysis": {
"grouped": false,
"custom_conv": false,
"epilogue": false,
"n_mm": 0,
"sdpa": false,
"conv": false,
"reduce": true,
"masky": false,
"quant": false
},
"metric": "GB/s",
"roofline_us": 421.142578125,
"ref_metric": 348.0133
},
{
"name": "instruction-interpreter-dispatch",
"tier": "T3",
"why": "quantised matmul: cuBLAS will not fuse the dequant, so the MMA pipeline is hand-written -- async copy, double buffering, fragment layouts",
"frac": null,
"analysis": {
"grouped": false,
"custom_conv": false,
"epilogue": true,
"n_mm": 1,
"sdpa": false,
"conv": false,
"reduce": true,
"masky": true,
"quant": true
},
"metric": null,
"roofline_us": null,
"ref_metric": null
},
{
"name": "int4-kv-cache-quant",
"tier": "T2",
"why": "bandwidth kernel with a reduction/gather: shared-memory tiling and a warp reduction",
"frac": 0.0173,
"analysis": {
"grouped": false,
"custom_conv": false,
"epilogue": false,
"n_mm": 0,
"sdpa": false,
"conv": false,
"reduce": true,
"masky": false,
"quant": true
},
"metric": "GB/s",
"roofline_us": 546.875,
"ref_metric": 83.2316
},
{
"name": "int4-weight-only-decode-gemv",
"tier": "T3",
"why": "quantised matmul: cuBLAS will not fuse the dequant, so the MMA pipeline is hand-written -- async copy, double buffering, fragment layouts",
"frac": 0.004,
"analysis": {
"grouped": false,
"custom_conv": false,
"epilogue": false,
"n_mm": 1,
"sdpa": false,
"conv": false,
"reduce": true,
"masky": true,
"quant": true
},
"metric": "GB/s",
"roofline_us": 218.20704142252606,
"ref_metric": 19.1005
},
{
"name": "int8-w8a8-gemm",
"tier": "T3",
"why": "quantised matmul: cuBLAS will not fuse the dequant, so the MMA pipeline is hand-written -- async copy, double buffering, fragment layouts",
"frac": 0.073,
"analysis": {
"grouped": false,
"custom_conv": false,
"epilogue": false,
"n_mm": 1,
"sdpa": false,
"conv": false,
"reduce": false,
"masky": false,
"quant": true
},
"metric": "TFLOP/s",
"roofline_us": 785.3654484114286,
"ref_metric": 51.0749
},
{
"name": "istft-overlap-add",
"tier": "T1",
"why": "elementwise/bandwidth chain: the win is fusing the passes into one",
"frac": 0.0212,
"analysis": {
"grouped": false,
"custom_conv": true,
"epilogue": false,
"n_mm": 0,
"sdpa": false,
"conv": false,
"reduce": false,
"masky": false,
"quant": false
},
"metric": "GB/s",
"roofline_us": 516.661008199056,
"ref_metric": 101.8793
},
{
"name": "kda-backward",
"tier": "T3",
"why": "attention with custom masking/sparsity: no library kernel applies as-is, so the tiled online-softmax pipeline is written by hand",
"frac": 0.0017,
"analysis": {
"grouped": false,
"custom_conv": false,
"epilogue": false,
"n_mm": 8,
"sdpa": false,
"conv": false,
"reduce": true,
"masky": true,
"quant": false
},
"metric": "TFLOP/s",
"roofline_us": 539.9387457828572,
"ref_metric": 1.1689
},
{
"name": "kda-decode-step",
"tier": "T2",
"why": "bandwidth kernel with a reduction/gather: shared-memory tiling and a warp reduction",
"frac": 0.0926,
"analysis": {
"grouped": false,
"custom_conv": false,
"epilogue": false,
"n_mm": 0,
"sdpa": false,
"conv": false,
"reduce": true,
"masky": false,
"quant": false
},
"metric": "GB/s",
"roofline_us": 474.25031661987305,
"ref_metric": 444.5032
},
{
"name": "kda-forward",
"tier": "T3",
"why": "attention with custom masking/sparsity: no library kernel applies as-is, so the tiled online-softmax pipeline is written by hand",
"frac": 0.0046,
"analysis": {
"grouped": false,
"custom_conv": false,
"epilogue": false,
"n_mm": 8,
"sdpa": false,
"conv": false,
"reduce": true,
"masky": true,
"quant": false
},
"metric": "TFLOP/s",
"roofline_us": 539.9387457828572,
"ref_metric": 3.2374
},
{
"name": "kv-block-pack-for-transfer",
"tier": "T1",
"why": "elementwise/bandwidth chain: the win is fusing the passes into one",
"frac": 0.0454,
"analysis": {
"grouped": false,
"custom_conv": false,
"epilogue": false,
"n_mm": 0,
"sdpa": false,
"conv": false,
"reduce": false,
"masky": false,
"quant": false
},
"metric": "GB/s",
"roofline_us": 586.536726138244,
"ref_metric": 218.0431
},
{
"name": "kv-cache-4bit-groupwise",
"tier": "T3",
"why": "quantised matmul: cuBLAS will not fuse the dequant, so the MMA pipeline is hand-written -- async copy, double buffering, fragment layouts",
"frac": 0.0026,
"analysis": {
"grouped": false,
"custom_conv": false,
"epilogue": false,
"n_mm": 2,
"sdpa": false,
"conv": false,
"reduce": true,
"masky": true,
"quant": true
},
"metric": "GB/s",
"roofline_us": 520.8333333333334,
"ref_metric": 12.6002
},
{
"name": "kv-cache-block-compaction",
"tier": "T1",
"why": "elementwise/bandwidth chain: the win is fusing the passes into one",
"frac": 0.1288,
"analysis": {
"grouped": false,
"custom_conv": false,
"epilogue": false,
"n_mm": 0,
"sdpa": false,
"conv": false,
"reduce": false,
"masky": false,
"quant": true
},
"metric": "GB/s",
"roofline_us": 625.0381469726562,
"ref_metric": 618.4209
},
{
"name": "kv-cache-fp8-quant-paged",
"tier": "T2",
"why": "bandwidth kernel with a reduction/gather: shared-memory tiling and a warp reduction",
"frac": 0.0314,
"analysis": {
"grouped": false,
"custom_conv": false,
"epilogue": false,
"n_mm": 0,
"sdpa": false,
"conv": false,
"reduce": true,
"masky": false,
"quant": true
},
"metric": "GB/s",
"roofline_us": 473.6328125,
"ref_metric": 150.486
},
{
"name": "kv-cache-page-alloc",
"tier": "T2",
"why": "bandwidth kernel with a reduction/gather: shared-memory tiling and a warp reduction",
"frac": 0.0564,
"analysis": {
"grouped": false,
"custom_conv": false,
"epilogue": false,
"n_mm": 0,
"sdpa": false,
"conv": false,
"reduce": true,
"masky": true,
"quant": true
},
"metric": "GB/s",
"roofline_us": 417.9287243944903,
"ref_metric": 270.757
},
{
"name": "kv-layout-contig-to-paged",
"tier": "T1",
"why": "elementwise/bandwidth chain: the win is fusing the passes into one",
"frac": 0.1294,
"analysis": {
"grouped": true,
"custom_conv": false,
"epilogue": false,
"n_mm": 0,
"sdpa": false,
"conv": false,
"reduce": false,
"masky": false,
"quant": false
},
"metric": "GB/s",
"roofline_us": 636.1883870946865,
"ref_metric": 621.0023
},
{
"name": "kv-repage-block-size-convert",
"tier": "T2",
"why": "bandwidth kernel with a reduction/gather: shared-memory tiling and a warp reduction",
"frac": 0.0694,
"analysis": {
"grouped": false,
"custom_conv": false,
"epilogue": false,
"n_mm": 0,
"sdpa": false,
"conv": false,
"reduce": false,
"masky": true,
"quant": true
},
"metric": "GB/s",
"roofline_us": 645.2573339144388,
"ref_metric": 333.0287
},
{
"name": "latent-blend-i2v-mask",
"tier": "T1",
"why": "elementwise/bandwidth chain: the win is fusing the passes into one",
"frac": 0.0521,
"analysis": {
"grouped": false,
"custom_conv": true,
"epilogue": false,
"n_mm": 0,
"sdpa": false,
"conv": false,
"reduce": false,
"masky": false,
"quant": false
},
"metric": "GB/s",
"roofline_us": 509.93263721466064,
"ref_metric": 250.1366
},
{
"name": "latent-interpolate-keyframe",
"tier": "T1",
"why": "elementwise/bandwidth chain: the win is fusing the passes into one",
"frac": 0.019,
"analysis": {
"grouped": false,
"custom_conv": false,
"epilogue": false,
"n_mm": 0,
"sdpa": false,
"conv": false,
"reduce": false,
"masky": false,
"quant": false
},
"metric": "GB/s",
"roofline_us": 314.7125244140625,
"ref_metric": 90.995
},
{
"name": "latent-normalize-perchannel",
"tier": "T2",
"why": "bandwidth kernel with a reduction/gather: shared-memory tiling and a warp reduction",
"frac": 0.072,
"analysis": {
"grouped": false,
"custom_conv": false,
"epilogue": false,
"n_mm": 0,
"sdpa": false,
"conv": false,
"reduce": true,
"masky": false,
"quant": false
},
"metric": "GB/s",
"roofline_us": 385.52284240722656,
"ref_metric": 345.367
},
{
"name": "latent-patchify-3d",
"tier": "T1",
"why": "elementwise/bandwidth chain: the win is fusing the passes into one",
"frac": 0.1973,
"analysis": {
"grouped": false,
"custom_conv": false,
"epilogue": false,
"n_mm": 0,
"sdpa": false,
"conv": false,
"reduce": false,
"masky": false,
"quant": true
},
"metric": "GB/s",
"roofline_us": 300.02593994140625,
"ref_metric": 946.9542
},
{
"name": "laurel-lowrank-residual",
"tier": "T3",
"why": "multi-GEMM block: needs a hand-written, pipelined MMA sequence to beat",
"frac": 0.0308,
"analysis": {
"grouped": false,
"custom_conv": false,
"epilogue": false,
"n_mm": 2,
"sdpa": false,
"conv": false,
"reduce": true,
"masky": false,
"quant": false
},
"metric": "GB/s",
"roofline_us": 520.9875106811523,
"ref_metric": 147.9671
},
{
"name": "layernorm-residual-fp8-backward",
"tier": "T2",
"why": "bandwidth kernel with a reduction/gather: shared-memory tiling and a warp reduction",
"frac": 0.0289,
"analysis": {
"grouped": false,
"custom_conv": false,
"epilogue": false,
"n_mm": 0,
"sdpa": false,
"conv": false,
"reduce": true,
"masky": false,
"quant": true
},
"metric": "GB/s",
"roofline_us": 823.974609375,
"ref_metric": 138.6289
},
{
"name": "layerscale-residual-fused",
"tier": "T1",
"why": "elementwise/bandwidth chain: the win is fusing the passes into one",
"frac": 0.2129,
"analysis": {
"grouped": false,
"custom_conv": false,
"epilogue": false,
"n_mm": 0,
"sdpa": false,
"conv": false,
"reduce": false,
"masky": false,
"quant": false
},
"metric": "GB/s",
"roofline_us": 703.1345367431641,
"ref_metric": 1021.9772
},
{
"name": "lightning-attn-backward",
"tier": "T3",
"why": "attention with custom masking/sparsity: no library kernel applies as-is, so the tiled online-softmax pipeline is written by hand",
"frac": 0.0007,
"analysis": {
"grouped": false,
"custom_conv": false,
"epilogue": false,
"n_mm": 4,
"sdpa": false,
"conv": false,
"reduce": false,
"masky": true,
"quant": false
},
"metric": "TFLOP/s",
"roofline_us": 441.7680647314286,
"ref_metric": 0.4892
},
{
"name": "lightning-attn-forward",
"tier": "T2",
"why": "bandwidth kernel with a reduction/gather: shared-memory tiling and a warp reduction",
"frac": 0.0005,
"analysis": {
"grouped": false,
"custom_conv": false,
"epilogue": false,
"n_mm": 0,
"sdpa": false,
"conv": false,
"reduce": true,
"masky": false,
"quant": false
},
"metric": "TFLOP/s",
"roofline_us": 343.59738368,
"ref_metric": 0.325
},
{
"name": "log-linear-attn-backward",
"tier": "T3",
"why": "quantised matmul: cuBLAS will not fuse the dequant, so the MMA pipeline is hand-written -- async copy, double buffering, fragment layouts",
"frac": 0.0056,
"analysis": {
"grouped": false,
"custom_conv": false,
"epilogue": false,
"n_mm": 4,
"sdpa": false,
"conv": false,
"reduce": true,
"masky": false,
"quant": true
},
"metric": "TFLOP/s",
"roofline_us": 441.7680647314286,
"ref_metric": 3.9116
},
{
"name": "log-linear-attn-forward",
"tier": "T3",
"why": "quantised matmul: cuBLAS will not fuse the dequant, so the MMA pipeline is hand-written -- async copy, double buffering, fragment layouts",
"frac": 0.0132,
"analysis": {
"grouped": false,
"custom_conv": false,
"epilogue": false,
"n_mm": 4,
"sdpa": false,
"conv": false,
"reduce": true,
"masky": false,
"quant": true
},
"metric": "TFLOP/s",
"roofline_us": 441.7680647314286,
"ref_metric": 9.2686
},
{
"name": "logits-gather-lastpos",
"tier": "T3",
"why": "grouped / variable-M GEMM: there is no single library call for it, so the per-group tiling and scheduling are the task",
"frac": 0.0535,
"analysis": {
"grouped": true,
"custom_conv": false,
"epilogue": false,
"n_mm": 1,
"sdpa": false,
"conv": false,
"reduce": false,
"masky": true,
"quant": false
},
"metric": "TFLOP/s",
"roofline_us": 1570.7308968228572,
"ref_metric": 37.4238
},
{
"name": "lora-backward-fused",
"tier": "T3",
"why": "multi-GEMM block: needs a hand-written, pipelined MMA sequence to beat",
"frac": 0.324,
"analysis": {
"grouped": false,
"custom_conv": false,
"epilogue": false,
"n_mm": 5,
"sdpa": false,
"conv": false,
"reduce": false,
"masky": false,
"quant": false
},
"metric": "TFLOP/s",
"roofline_us": 822.1794538057143,
"ref_metric": 226.7667
},
{
"name": "lora-fused-gemm",
"tier": "T3",
"why": "multi-GEMM block: needs a hand-written, pipelined MMA sequence to beat",
"frac": 0.3716,
"analysis": {
"grouped": false,
"custom_conv": false,
"epilogue": false,
"n_mm": 3,
"sdpa": false,
"conv": false,
"reduce": false,
"masky": false,
"quant": false
},
"metric": "TFLOP/s",
"roofline_us": 800.7046173257143,
"ref_metric": 260.09
},
{
"name": "ltx-fractional-rope3d",
"tier": "T2",
"why": "bandwidth kernel with a reduction/gather: shared-memory tiling and a warp reduction",
"frac": 0.019,
"analysis": {
"grouped": false,
"custom_conv": false,
"epilogue": false,
"n_mm": 0,
"sdpa": false,
"conv": false,
"reduce": true,
"masky": true,
"quant": false
},
"metric": "GB/s",
"roofline_us": 510.25390624999994,
"ref_metric": 91.3329
},
{
"name": "ltx-highcompression-vae-decode",
"tier": "T3",
"why": "attention with custom masking/sparsity: no library kernel applies as-is, so the tiled online-softmax pipeline is written by hand",
"frac": 0.189,
"analysis": {
"grouped": false,
"custom_conv": true,
"epilogue": false,
"n_mm": 1,
"sdpa": false,
"conv": true,
"reduce": false,
"masky": true,
"quant": false
},
"metric": "TFLOP/s",
"roofline_us": 942.2084505600001,
"ref_metric": 132.3293
},
{
"name": "ltx-per-token-adanorm",
"tier": "T2",
"why": "bandwidth kernel with a reduction/gather: shared-memory tiling and a warp reduction",
"frac": 0.0569,
"analysis": {
"grouped": false,
"custom_conv": false,
"epilogue": false,
"n_mm": 0,
"sdpa": false,
"conv": false,
"reduce": true,
"masky": false,
"quant": false
},
"metric": "GB/s",
"roofline_us": 429.1534423828125,
"ref_metric": 273.2629
},
{
"name": "ltx-per-token-adanorm-backward",
"tier": "T2",
"why": "bandwidth kernel with a reduction/gather: shared-memory tiling and a warp reduction",
"frac": 0.0492,
"analysis": {
"grouped": false,
"custom_conv": false,
"epilogue": false,
"n_mm": 0,
"sdpa": false,
"conv": false,
"reduce": true,
"masky": false,
"quant": false
},
"metric": "GB/s",
"roofline_us": 333.7860107421875,
"ref_metric": 236.1666
},
{
"name": "ltx-rectified-flow-shift",
"tier": "T2",
"why": "bandwidth kernel with a reduction/gather: shared-memory tiling and a warp reduction",
"frac": 0.0505,
"analysis": {
"grouped": false,
"custom_conv": false,
"epilogue": false,
"n_mm": 0,
"sdpa": false,
"conv": false,
"reduce": false,
"masky": true,
"quant": false
},
"metric": "GB/s",
"roofline_us": 300.94385147094727,
"ref_metric": 242.5323
},
{
"name": "mamba2-ssd-backward",
"tier": "T3",
"why": "attention with custom masking/sparsity: no library kernel applies as-is, so the tiled online-softmax pipeline is written by hand",
"frac": 0.0008,
"analysis": {
"grouped": false,
"custom_conv": false,
"epilogue": false,
"n_mm": 4,
"sdpa": false,
"conv": false,
"reduce": true,
"masky": true,
"quant": false
},
"metric": "TFLOP/s",
"roofline_us": 343.59738368,
"ref_metric": 0.5566
},
{
"name": "mamba2-ssd-decode-step",
"tier": "T2",
"why": "bandwidth kernel with a reduction/gather: shared-memory tiling and a warp reduction",
"frac": 0.1222,
"analysis": {
"grouped": false,
"custom_conv": false,
"epilogue": false,
"n_mm": 0,
"sdpa": false,
"conv": false,
"reduce": true,
"masky": false,
"quant": false
},
"metric": "GB/s",
"roofline_us": 474.2717742919922,
"ref_metric": 586.4182
},
{
"name": "mamba2-ssd-forward",
"tier": "T3",
"why": "attention with custom masking/sparsity: no library kernel applies as-is, so the tiled online-softmax pipeline is written by hand",
"frac": null,
"analysis": {
"grouped": false,
"custom_conv": true,
"epilogue": false,
"n_mm": 4,
"sdpa": false,
"conv": false,
"reduce": true,
"masky": true,
"quant": false
},
"metric": null,
"roofline_us": null,
"ref_metric": 0.0006
},
{
"name": "megakernel-batch4-decode",
"tier": "T3",
"why": "quantised matmul: cuBLAS will not fuse the dequant, so the MMA pipeline is hand-written -- async copy, double buffering, fragment layouts",
"frac": null,
"analysis": {
"grouped": false,
"custom_conv": false,
"epilogue": true,
"n_mm": 9,
"sdpa": true,
"conv": false,
"reduce": true,
"masky": true,
"quant": true
},
"metric": null,
"roofline_us": null,
"ref_metric": null
},
{
"name": "megakernel-gated-deltanet-decode",
"tier": "T3",
"why": "quantised matmul: cuBLAS will not fuse the dequant, so the MMA pipeline is hand-written -- async copy, double buffering, fragment layouts",
"frac": null,
"analysis": {
"grouped": false,
"custom_conv": false,
"epilogue": true,
"n_mm": 19,
"sdpa": true,
"conv": false,
"reduce": true,
"masky": true,
"quant": true
},
"metric": null,
"roofline_us": null,
"ref_metric": null
},
{
"name": "megakernel-gqa-paged-decode",
"tier": "T3",
"why": "quantised matmul: cuBLAS will not fuse the dequant, so the MMA pipeline is hand-written -- async copy, double buffering, fragment layouts",
"frac": null,
"analysis": {
"grouped": false,
"custom_conv": false,
"epilogue": true,
"n_mm": 9,
"sdpa": true,
"conv": false,
"reduce": true,
"masky": true,
"quant": true
},
"metric": null,
"roofline_us": null,
"ref_metric": null
},
{
"name": "megakernel-int4-weight-decode",
"tier": "T3",
"why": "quantised matmul: cuBLAS will not fuse the dequant, so the MMA pipeline is hand-written -- async copy, double buffering, fragment layouts",
"frac": null,
"analysis": {
"grouped": false,
"custom_conv": false,
"epilogue": true,
"n_mm": 9,
"sdpa": true,
"conv": false,
"reduce": true,
"masky": true,
"quant": true
},
"metric": null,
"roofline_us": null,
"ref_metric": null
},
{
"name": "megakernel-llama1b-bf16-decode",
"tier": "T3",
"why": "quantised matmul: cuBLAS will not fuse the dequant, so the MMA pipeline is hand-written -- async copy, double buffering, fragment layouts",
"frac": null,
"analysis": {
"grouped": false,
"custom_conv": false,
"epilogue": true,
"n_mm": 9,
"sdpa": true,
"conv": false,
"reduce": true,
"masky": true,
"quant": true
},
"metric": null,
"roofline_us": null,
"ref_metric": null
},
{
"name": "megakernel-llama1b-fp8-decode",
"tier": "T3",
"why": "quantised matmul: cuBLAS will not fuse the dequant, so the MMA pipeline is hand-written -- async copy, double buffering, fragment layouts",
"frac": null,
"analysis": {
"grouped": false,
"custom_conv": false,
"epilogue": true,
"n_mm": 9,
"sdpa": true,
"conv": false,
"reduce": true,
"masky": true,
"quant": true
},
"metric": null,
"roofline_us": null,
"ref_metric": null
},
{
"name": "megakernel-llama1b-longctx-decode",
"tier": "T3",
"why": "quantised matmul: cuBLAS will not fuse the dequant, so the MMA pipeline is hand-written -- async copy, double buffering, fragment layouts",
"frac": null,
"analysis": {
"grouped": false,
"custom_conv": false,
"epilogue": true,
"n_mm": 9,
"sdpa": true,
"conv": false,
"reduce": true,
"masky": true,
"quant": true
},
"metric": null,
"roofline_us": null,
"ref_metric": null
},
{
"name": "megakernel-llama8b-bf16-decode",
"tier": "T3",
"why": "quantised matmul: cuBLAS will not fuse the dequant, so the MMA pipeline is hand-written -- async copy, double buffering, fragment layouts",
"frac": null,
"analysis": {
"grouped": false,
"custom_conv": false,
"epilogue": true,
"n_mm": 9,
"sdpa": true,
"conv": false,
"reduce": true,
"masky": true,
"quant": true
},
"metric": null,
"roofline_us": null,
"ref_metric": null
},
{
"name": "megakernel-lm-head-sample-inline",
"tier": "T3",
"why": "quantised matmul: cuBLAS will not fuse the dequant, so the MMA pipeline is hand-written -- async copy, double buffering, fragment layouts",
"frac": null,
"analysis": {
"grouped": false,
"custom_conv": true,
"epilogue": false,
"n_mm": 1,
"sdpa": false,
"conv": false,
"reduce": true,
"masky": false,
"quant": true
},
"metric": null,
"roofline_us": null,
"ref_metric": null
},
{
"name": "megakernel-mamba-hybrid-decode",
"tier": "T3",
"why": "quantised matmul: cuBLAS will not fuse the dequant, so the MMA pipeline is hand-written -- async copy, double buffering, fragment layouts",
"frac": null,
"analysis": {
"grouped": false,
"custom_conv": false,
"epilogue": true,
"n_mm": 11,
"sdpa": true,
"conv": false,
"reduce": true,
"masky": true,
"quant": true
},
"metric": null,
"roofline_us": null,
"ref_metric": null
},
{
"name": "megakernel-mtp-decode",
"tier": "T3",
"why": "quantised matmul: cuBLAS will not fuse the dequant, so the MMA pipeline is hand-written -- async copy, double buffering, fragment layouts",
"frac": null,
"analysis": {
"grouped": false,
"custom_conv": false,
"epilogue": true,
"n_mm": 11,
"sdpa": true,
"conv": false,
"reduce": true,
"masky": true,
"quant": true
},
"metric": null,
"roofline_us": null,
"ref_metric": null
},
{
"name": "megakernel-nvfp4-decode",
"tier": "T3",
"why": "quantised matmul: cuBLAS will not fuse the dequant, so the MMA pipeline is hand-written -- async copy, double buffering, fragment layouts",
"frac": null,
"analysis": {
"grouped": false,
"custom_conv": false,
"epilogue": true,
"n_mm": 9,
"sdpa": true,
"conv": false,
"reduce": true,
"masky": true,
"quant": true
},
"metric": null,
"roofline_us": null,
"ref_metric": null
},
{
"name": "megakernel-prefill-chunk",
"tier": "T3",
"why": "quantised matmul: cuBLAS will not fuse the dequant, so the MMA pipeline is hand-written -- async copy, double buffering, fragment layouts",
"frac": null,
"analysis": {
"grouped": false,
"custom_conv": true,
"epilogue": true,
"n_mm": 9,
"sdpa": true,
"conv": false,
"reduce": true,
"masky": true,
"quant": true
},
"metric": null,
"roofline_us": null,
"ref_metric": null
},
{
"name": "megakernel-qwen3-8b-fp8-decode",
"tier": "T3",
"why": "quantised matmul: cuBLAS will not fuse the dequant, so the MMA pipeline is hand-written -- async copy, double buffering, fragment layouts",
"frac": null,
"analysis": {
"grouped": false,
"custom_conv": false,
"epilogue": true,
"n_mm": 9,
"sdpa": true,
"conv": false,
"reduce": true,
"masky": true,
"quant": true
},
"metric": null,
"roofline_us": null,
"ref_metric": null
},
{
"name": "megakernel-qwen3-moe-decode",
"tier": "T3",
"why": "quantised matmul: cuBLAS will not fuse the dequant, so the MMA pipeline is hand-written -- async copy, double buffering, fragment layouts",
"frac": null,
"analysis": {
"grouped": false,
"custom_conv": false,
"epilogue": true,
"n_mm": 10,
"sdpa": true,
"conv": false,
"reduce": true,
"masky": true,
"quant": true
},
"metric": null,
"roofline_us": null,
"ref_metric": null
},
{
"name": "megakernel-spec-decode-verify",
"tier": "T3",
"why": "quantised matmul: cuBLAS will not fuse the dequant, so the MMA pipeline is hand-written -- async copy, double buffering, fragment layouts",
"frac": null,
"analysis": {
"grouped": true,
"custom_conv": false,
"epilogue": true,
"n_mm": 9,
"sdpa": true,
"conv": false,
"reduce": true,
"masky": true,
"quant": true
},
"metric": null,
"roofline_us": null,
"ref_metric": null
},
{
"name": "megakernel-untied-head-decode",
"tier": "T3",
"why": "quantised matmul: cuBLAS will not fuse the dequant, so the MMA pipeline is hand-written -- async copy, double buffering, fragment layouts",
"frac": null,
"analysis": {
"grouped": false,
"custom_conv": false,
"epilogue": true,
"n_mm": 9,
"sdpa": true,
"conv": false,
"reduce": true,
"masky": true,
"quant": true
},
"metric": null,
"roofline_us": null,
"ref_metric": null
},
{
"name": "mesa-net-backward",
"tier": "T3",
"why": "attention with custom masking/sparsity: no library kernel applies as-is, so the tiled online-softmax pipeline is written by hand",
"frac": 0.0004,
"analysis": {
"grouped": false,
"custom_conv": false,
"epilogue": false,
"n_mm": 5,
"sdpa": false,
"conv": false,
"reduce": true,
"masky": true,
"quant": false
},
"metric": "TFLOP/s",
"roofline_us": 291.4442093714286,
"ref_metric": 0.2888
},
{
"name": "mesa-net-forward",
"tier": "T2",
"why": "bandwidth kernel with a reduction/gather: shared-memory tiling and a warp reduction",
"frac": 0.0001,
"analysis": {
"grouped": false,
"custom_conv": false,
"epilogue": false,
"n_mm": 0,
"sdpa": false,
"conv": false,
"reduce": true,
"masky": false,
"quant": false
},
"metric": "TFLOP/s",
"roofline_us": 233.15536749714286,
"ref_metric": 0.0694
},
{
"name": "min-p-sampling-filter",
"tier": "T2",
"why": "bandwidth kernel with a reduction/gather: shared-memory tiling and a warp reduction",
"frac": 0.0879,
"analysis": {
"grouped": false,
"custom_conv": false,
"epilogue": false,
"n_mm": 0,
"sdpa": false,
"conv": false,
"reduce": true,
"masky": true,
"quant": false
},
"metric": "GB/s",
"roofline_us": 416.6682561238607,
"ref_metric": 421.9222
},
{
"name": "mixed-precision-gemm-split",
"tier": "T3",
"why": "quantised matmul: cuBLAS will not fuse the dequant, so the MMA pipeline is hand-written -- async copy, double buffering, fragment layouts",
"frac": 0.0718,
"analysis": {
"grouped": false,
"custom_conv": false,
"epilogue": false,
"n_mm": 2,
"sdpa": false,
"conv": false,
"reduce": false,
"masky": false,
"quant": true
},
"metric": "TFLOP/s",
"roofline_us": 809.9081186742857,
"ref_metric": 50.2402
},
{
"name": "mla-backward",
"tier": "T3",
"why": "attention with custom masking/sparsity: no library kernel applies as-is, so the tiled online-softmax pipeline is written by hand",
"frac": 0.0244,
"analysis": {
"grouped": false,
"custom_conv": true,
"epilogue": false,
"n_mm": 5,
"sdpa": false,
"conv": false,
"reduce": true,
"masky": true,
"quant": false
},
"metric": "TFLOP/s",
"roofline_us": 1178.0481726171429,
"ref_metric": 17.082
},
{
"name": "mla-decode",
"tier": "T3",
"why": "multi-GEMM block: needs a hand-written, pipelined MMA sequence to beat",
"frac": null,
"analysis": {
"grouped": false,
"custom_conv": false,
"epilogue": false,
"n_mm": 2,
"sdpa": false,
"conv": false,
"reduce": true,
"masky": false,
"quant": false
},
"metric": null,
"roofline_us": null,
"ref_metric": 0.0021
},
{
"name": "mla-decode-paged",
"tier": "T3",
"why": "attention with custom masking/sparsity: no library kernel applies as-is, so the tiled online-softmax pipeline is written by hand",
"frac": 0.0304,
"analysis": {
"grouped": false,
"custom_conv": true,
"epilogue": false,
"n_mm": 2,
"sdpa": false,
"conv": false,
"reduce": true,
"masky": true,
"quant": false
},
"metric": "GB/s",
"roofline_us": 350.11768341064453,
"ref_metric": 145.7798
},
{
"name": "mla-prefill",
"tier": "T3",
"why": "attention with custom masking/sparsity: no library kernel applies as-is, so the tiled online-softmax pipeline is written by hand",
"frac": 0.0289,
"analysis": {
"grouped": false,
"custom_conv": true,
"epilogue": false,
"n_mm": 5,
"sdpa": false,
"conv": false,
"reduce": true,
"masky": true,
"quant": false
},
"metric": "TFLOP/s",
"roofline_us": 1030.79215104,
"ref_metric": 20.2525
},
{
"name": "mm-embed-merge-scatter",
"tier": "T2",
"why": "bandwidth kernel with a reduction/gather: shared-memory tiling and a warp reduction",
"frac": 0.2023,
"analysis": {
"grouped": false,
"custom_conv": false,
"epilogue": false,
"n_mm": 0,
"sdpa": false,
"conv": false,
"reduce": true,
"masky": true,
"quant": true
},
"metric": "GB/s",
"roofline_us": 410.15625,
"ref_metric": 970.883
},
{
"name": "moba-backward",
"tier": "T3",
"why": "attention with custom masking/sparsity: no library kernel applies as-is, so the tiled online-softmax pipeline is written by hand",
"frac": 0.0111,
"analysis": {
"grouped": false,
"custom_conv": true,
"epilogue": false,
"n_mm": 3,
"sdpa": false,
"conv": false,
"reduce": true,
"masky": true,
"quant": false
},
"metric": "TFLOP/s",
"roofline_us": 540.4180948114285,
"ref_metric": 7.797
},
{
"name": "moba-block-attention",
"tier": "T3",
"why": "attention with custom masking/sparsity: no library kernel applies as-is, so the tiled online-softmax pipeline is written by hand",
"frac": null,
"analysis": {
"grouped": false,
"custom_conv": true,
"epilogue": false,
"n_mm": 2,
"sdpa": true,
"conv": false,
"reduce": true,
"masky": true,
"quant": false
},
"metric": null,
"roofline_us": null,
"ref_metric": 6.2968
},
{
"name": "mochi-asymm-joint-attention",
"tier": "T3",
"why": "grouped / variable-M GEMM: there is no single library call for it, so the per-group tiling and scheduling are the task",
"frac": 0.0323,
"analysis": {
"grouped": true,
"custom_conv": false,
"epilogue": false,
"n_mm": 2,
"sdpa": false,
"conv": false,
"reduce": true,
"masky": false,
"quant": false
},
"metric": "TFLOP/s",
"roofline_us": 5107.665188571428,
"ref_metric": 22.6148
},
{
"name": "mochi-asymm-qkv-proj",
"tier": "T3",
"why": "multi-GEMM block: needs a hand-written, pipelined MMA sequence to beat",
"frac": 0.061,
"analysis": {
"grouped": false,
"custom_conv": false,
"epilogue": false,
"n_mm": 2,
"sdpa": false,
"conv": false,
"reduce": true,
"masky": false,
"quant": false
},
"metric": "TFLOP/s",
"roofline_us": 3621.9372924342856,
"ref_metric": 42.6655
},
{
"name": "mochi-tanh-gated-rmsnorm",
"tier": "T2",
"why": "bandwidth kernel with a reduction/gather: shared-memory tiling and a warp reduction",
"frac": 0.0503,
"analysis": {
"grouped": false,
"custom_conv": false,
"epilogue": true,
"n_mm": 0,
"sdpa": false,
"conv": false,
"reduce": true,
"masky": false,
"quant": false
},
"metric": "GB/s",
"roofline_us": 429.1534423828125,
"ref_metric": 241.4728
},
{
"name": "mochi-visual-text-split-mlp",
"tier": "T3",
"why": "GEMM with a fused activation/gate epilogue: needs a hand-written MMA pipeline",
"frac": 0.065,
"analysis": {
"grouped": false,
"custom_conv": false,
"epilogue": true,
"n_mm": 2,
"sdpa": false,
"conv": false,
"reduce": true,
"masky": false,
"quant": false
},
"metric": "TFLOP/s",
"roofline_us": 4829.249723245714,
"ref_metric": 45.4709
},
{
"name": "moe-capacity-drop-dispatch",
"tier": "T2",
"why": "bandwidth kernel with a reduction/gather: shared-memory tiling and a warp reduction",
"frac": 0.0126,
"analysis": {
"grouped": true,
"custom_conv": false,
"epilogue": false,
"n_mm": 0,
"sdpa": false,
"conv": false,
"reduce": true,
"masky": true,
"quant": true
},
"metric": "GB/s",
"roofline_us": 483.34757486979163,
"ref_metric": 60.7125
},
{
"name": "moe-combine-backward",
"tier": "T2",
"why": "bandwidth kernel with a reduction/gather: shared-memory tiling and a warp reduction",
"frac": 0.0273,
"analysis": {
"grouped": false,
"custom_conv": false,
"epilogue": false,
"n_mm": 0,
"sdpa": false,
"conv": false,
"reduce": true,
"masky": false,
"quant": true
},
"metric": "GB/s",
"roofline_us": 581.2835693359375,
"ref_metric": 131.2667
},
{
"name": "moe-combine-weighted",
"tier": "T1",
"why": "elementwise/bandwidth chain: the win is fusing the passes into one",
"frac": 0.0267,
"analysis": {
"grouped": false,
"custom_conv": false,
"epilogue": false,
"n_mm": 0,
"sdpa": false,
"conv": false,
"reduce": false,
"masky": false,
"quant": true
},
"metric": "GB/s",
"roofline_us": 205.17985026041669,
"ref_metric": 128.2668
},
{
"name": "moe-expert-bias-update",
"tier": "T2",
"why": "bandwidth kernel with a reduction/gather: shared-memory tiling and a warp reduction",
"frac": 0.0048,
"analysis": {
"grouped": true,
"custom_conv": false,
"epilogue": false,
"n_mm": 0,
"sdpa": false,
"conv": false,
"reduce": true,
"masky": false,
"quant": true
},
"metric": "GB/s",
"roofline_us": 260.467529296875,
"ref_metric": 23.0509
},
{
"name": "moe-expert-weight-gather",
"tier": "T2",
"why": "bandwidth kernel with a reduction/gather: shared-memory tiling and a warp reduction",
"frac": 0.0873,
"analysis": {
"grouped": false,
"custom_conv": false,
"epilogue": false,
"n_mm": 0,
"sdpa": false,
"conv": false,
"reduce": false,
"masky": true,
"quant": true
},
"metric": "GB/s",
"roofline_us": 729.1671137015024,
"ref_metric": 419.1689
},
{
"name": "moe-group-limited-routing",
"tier": "T2",
"why": "bandwidth kernel with a reduction/gather: shared-memory tiling and a warp reduction",
"frac": 0.0048,
"analysis": {
"grouped": false,
"custom_conv": false,
"epilogue": true,
"n_mm": 0,
"sdpa": false,
"conv": false,
"reduce": true,
"masky": true,
"quant": true
},
"metric": "GB/s",
"roofline_us": 221.35436534881592,
"ref_metric": 22.9193
},
{
"name": "moe-grouped-gemm-contiguous",
"tier": "T3",
"why": "grouped GEMM over a contiguous expert layout: no single library call covers it",
"frac": 0.0564,
"analysis": {
"grouped": false,
"custom_conv": false,
"epilogue": false,
"n_mm": 1,
"sdpa": false,
"conv": false,
"reduce": false,
"masky": false,
"quant": false
},
"metric": "TFLOP/s",
"roofline_us": 1374.38953472,
"ref_metric": 39.4584
},
{
"name": "moe-grouped-gemm-varm",
"tier": "T3",
"why": "variable-M grouped GEMM: cuBLAS has no such call",
"frac": 0.0373,
"analysis": {
"grouped": true,
"custom_conv": false,
"epilogue": false,
"n_mm": 1,
"sdpa": false,
"conv": false,
"reduce": false,
"masky": false,
"quant": false
},
"metric": "TFLOP/s",
"roofline_us": 1570.7308968228572,
"ref_metric": 26.1395
},
{
"name": "moe-grouped-swiglu",
"tier": "T3",
"why": "grouped GEMM with a fused SwiGLU epilogue",
"frac": 0.0539,
"analysis": {
"grouped": false,
"custom_conv": false,
"epilogue": true,
"n_mm": 1,
"sdpa": false,
"conv": false,
"reduce": false,
"masky": false,
"quant": false
},
"metric": "TFLOP/s",
"roofline_us": 1570.7308968228572,
"ref_metric": 37.7116
},
{
"name": "moe-router-backward",
"tier": "T3",
"why": "attention with custom masking/sparsity: no library kernel applies as-is, so the tiled online-softmax pipeline is written by hand",
"frac": 0.0425,
"analysis": {
"grouped": false,
"custom_conv": false,
"epilogue": false,
"n_mm": 2,
"sdpa": false,
"conv": false,
"reduce": true,
"masky": true,
"quant": false
},
"metric": "TFLOP/s",
"roofline_us": 429.49672960000004,
"ref_metric": 29.7736
},
{
"name": "moe-shared-expert-fused",
"tier": "T3",
"why": "quantised matmul: cuBLAS will not fuse the dequant, so the MMA pipeline is hand-written -- async copy, double buffering, fragment layouts",
"frac": 0.0481,
"analysis": {
"grouped": false,
"custom_conv": false,
"epilogue": true,
"n_mm": 2,
"sdpa": false,
"conv": false,
"reduce": false,
"masky": false,
"quant": true
},
"metric": "TFLOP/s",
"roofline_us": 1032.13432832,
"ref_metric": 33.6427
},
{
"name": "moe-token-permute",
"tier": "T2",
"why": "bandwidth kernel with a reduction/gather: shared-memory tiling and a warp reduction",
"frac": 0.1298,
"analysis": {
"grouped": true,
"custom_conv": false,
"epilogue": false,
"n_mm": 0,
"sdpa": false,
"conv": false,
"reduce": true,
"masky": false,
"quant": true
},
"metric": "GB/s",
"roofline_us": 205.18004971866807,
"ref_metric": 622.945
},
{
"name": "moe-topk-softmax-fused",
"tier": "T2",
"why": "bandwidth kernel with a reduction/gather: shared-memory tiling and a warp reduction",
"frac": 0.0155,
"analysis": {
"grouped": false,
"custom_conv": false,
"epilogue": false,
"n_mm": 0,
"sdpa": false,
"conv": false,
"reduce": true,
"masky": false,
"quant": true
},
"metric": "GB/s",
"roofline_us": 276.6927083333333,
"ref_metric": 74.1755
},
{
"name": "mrope-multimodal-apply",
"tier": "T1",
"why": "elementwise/bandwidth chain: the win is fusing the passes into one",
"frac": 0.0346,
"analysis": {
"grouped": false,
"custom_conv": false,
"epilogue": false,
"n_mm": 0,
"sdpa": false,
"conv": false,
"reduce": false,
"masky": false,
"quant": false
},
"metric": "GB/s",
"roofline_us": 325.5208333333333,
"ref_metric": 165.9735
},
{
"name": "mtp-head-forward",
"tier": "T3",
"why": "multi-GEMM block: needs a hand-written, pipelined MMA sequence to beat",
"frac": 0.0607,
"analysis": {
"grouped": false,
"custom_conv": false,
"epilogue": false,
"n_mm": 2,
"sdpa": false,
"conv": false,
"reduce": true,
"masky": false,
"quant": false
},
"metric": "TFLOP/s",
"roofline_us": 3337.8031557485715,
"ref_metric": 42.5029
},
{
"name": "mtp-multi-head-loss",
"tier": "T2",
"why": "bandwidth kernel with a reduction/gather: shared-memory tiling and a warp reduction",
"frac": 0.0366,
"analysis": {
"grouped": true,
"custom_conv": true,
"epilogue": false,
"n_mm": 0,
"sdpa": false,
"conv": false,
"reduce": true,
"masky": true,
"quant": false
},
"metric": "GB/s",
"roofline_us": 1666.6666666666667,
"ref_metric": 175.4855
},
{
"name": "multi-lora-batched-decode",
"tier": "T2",
"why": "bandwidth kernel with a reduction/gather: shared-memory tiling and a warp reduction",
"frac": 0.0283,
"analysis": {
"grouped": false,
"custom_conv": false,
"epilogue": false,
"n_mm": 0,
"sdpa": false,
"conv": false,
"reduce": true,
"masky": true,
"quant": false
},
"metric": "GB/s",
"roofline_us": 477.7331999503076,
"ref_metric": 135.7824
},
{
"name": "muon-newton-schulz",
"tier": "T3",
"why": "multi-GEMM block: needs a hand-written, pipelined MMA sequence to beat",
"frac": null,
"analysis": {
"grouped": false,
"custom_conv": false,
"epilogue": false,
"n_mm": 3,
"sdpa": false,
"conv": false,
"reduce": true,
"masky": false,
"quant": false
},
"metric": null,
"roofline_us": null,
"ref_metric": 0.0
},
{
"name": "mxfp4-dequant-gemm",
"tier": "T3",
"why": "quantised matmul: cuBLAS will not fuse the dequant, so the MMA pipeline is hand-written -- async copy, double buffering, fragment layouts",
"frac": 0.0472,
"analysis": {
"grouped": false,
"custom_conv": false,
"epilogue": false,
"n_mm": 1,
"sdpa": false,
"conv": false,
"reduce": false,
"masky": true,
"quant": true
},
"metric": "TFLOP/s",
"roofline_us": 785.3654484114286,
"ref_metric": 33.0729
},
{
"name": "mxfp4-training-backward",
"tier": "T3",
"why": "quantised matmul: cuBLAS will not fuse the dequant, so the MMA pipeline is hand-written -- async copy, double buffering, fragment layouts",
"frac": 0.0607,
"analysis": {
"grouped": false,
"custom_conv": false,
"epilogue": false,
"n_mm": 1,
"sdpa": false,
"conv": false,
"reduce": true,
"masky": true,
"quant": true
},
"metric": "TFLOP/s",
"roofline_us": 785.3654484114286,
"ref_metric": 42.4581
},
{
"name": "mxfp8-blockwise-gemm",
"tier": "T3",
"why": "quantised matmul: cuBLAS will not fuse the dequant, so the MMA pipeline is hand-written -- async copy, double buffering, fragment layouts",
"frac": 0.0595,
"analysis": {
"grouped": false,
"custom_conv": false,
"epilogue": false,
"n_mm": 1,
"sdpa": false,
"conv": false,
"reduce": false,
"masky": false,
"quant": true
},
"metric": "TFLOP/s",
"roofline_us": 785.3654484114286,
"ref_metric": 41.668
},
{
"name": "mxfp8-dual-axis-quantize",
"tier": "T2",
"why": "bandwidth kernel with a reduction/gather: shared-memory tiling and a warp reduction",
"frac": 0.0253,
"analysis": {
"grouped": false,
"custom_conv": false,
"epilogue": false,
"n_mm": 0,
"sdpa": false,
"conv": false,
"reduce": true,
"masky": true,
"quant": true
},
"metric": "GB/s",
"roofline_us": 423.17708333333337,
"ref_metric": 121.4634
},
{
"name": "ngpt-l2norm-residual",
"tier": "T2",
"why": "bandwidth kernel with a reduction/gather: shared-memory tiling and a warp reduction",
"frac": 0.039,
"analysis": {
"grouped": false,
"custom_conv": false,
"epilogue": false,
"n_mm": 0,
"sdpa": false,
"conv": false,
"reduce": true,
"masky": false,
"quant": false
},
"metric": "GB/s",
"roofline_us": 703.129768371582,
"ref_metric": 187.4287
},
{
"name": "ngram-lookahead-match",
"tier": "T2",
"why": "bandwidth kernel with a reduction/gather: shared-memory tiling and a warp reduction",
"frac": 0.0163,
"analysis": {
"grouped": false,
"custom_conv": false,
"epilogue": false,
"n_mm": 0,
"sdpa": false,
"conv": false,
"reduce": true,
"masky": true,
"quant": true
},
"metric": "GB/s",
"roofline_us": 300.535907347997,
"ref_metric": 78.4312
},
{
"name": "noise-add-scaled",
"tier": "T1",
"why": "elementwise/bandwidth chain: the win is fusing the passes into one",
"frac": 0.068,
"analysis": {
"grouped": false,
"custom_conv": false,
"epilogue": false,
"n_mm": 0,
"sdpa": false,
"conv": false,
"reduce": false,
"masky": false,
"quant": false
},
"metric": "GB/s",
"roofline_us": 494.4801330566406,
"ref_metric": 326.581
},
{
"name": "nsa-compression-branch",
"tier": "T3",
"why": "attention with custom masking/sparsity: no library kernel applies as-is, so the tiled online-softmax pipeline is written by hand",
"frac": 0.0109,
"analysis": {
"grouped": false,
"custom_conv": false,
"epilogue": false,
"n_mm": 2,
"sdpa": false,
"conv": false,
"reduce": true,
"masky": true,
"quant": false
},
"metric": "TFLOP/s",
"roofline_us": 393.0654544457143,
"ref_metric": 7.6478
},
{
"name": "nsa-selected-attention",
"tier": "T3",
"why": "attention with custom masking/sparsity: no library kernel applies as-is, so the tiled online-softmax pipeline is written by hand",
"frac": null,
"analysis": {
"grouped": false,
"custom_conv": true,
"epilogue": false,
"n_mm": 2,
"sdpa": false,
"conv": false,
"reduce": true,
"masky": true,
"quant": false
},
"metric": null,
"roofline_us": null,
"ref_metric": 0.0001
},
{
"name": "nsa-selected-backward",
"tier": "T3",
"why": "attention with custom masking/sparsity: no library kernel applies as-is, so the tiled online-softmax pipeline is written by hand",
"frac": 0.0073,
"analysis": {
"grouped": false,
"custom_conv": true,
"epilogue": false,
"n_mm": 2,
"sdpa": false,
"conv": false,
"reduce": true,
"masky": true,
"quant": false
},
"metric": "TFLOP/s",
"roofline_us": 392.6827242057143,
"ref_metric": 5.094
},
{
"name": "nvfp4-dequant-gemm",
"tier": "T3",
"why": "quantised matmul: cuBLAS will not fuse the dequant, so the MMA pipeline is hand-written -- async copy, double buffering, fragment layouts",
"frac": 0.0465,
"analysis": {
"grouped": false,
"custom_conv": false,
"epilogue": false,
"n_mm": 1,
"sdpa": false,
"conv": false,
"reduce": false,
"masky": true,
"quant": true
},
"metric": "TFLOP/s",
"roofline_us": 785.3654484114286,
"ref_metric": 32.5616
},
{
"name": "nvfp4-gemm-backward",
"tier": "T3",
"why": "quantised matmul: cuBLAS will not fuse the dequant, so the MMA pipeline is hand-written -- async copy, double buffering, fragment layouts",
"frac": 0.1296,
"analysis": {
"grouped": false,
"custom_conv": false,
"epilogue": false,
"n_mm": 1,
"sdpa": false,
"conv": false,
"reduce": false,
"masky": true,
"quant": true
},
"metric": "TFLOP/s",
"roofline_us": 785.3654484114286,
"ref_metric": 90.6909
},
{
"name": "nvfp4-quantize",
"tier": "T2",
"why": "bandwidth kernel with a reduction/gather: shared-memory tiling and a warp reduction",
"frac": 0.0124,
"analysis": {
"grouped": false,
"custom_conv": false,
"epilogue": false,
"n_mm": 0,
"sdpa": false,
"conv": false,
"reduce": true,
"masky": true,
"quant": true
},
"metric": "GB/s",
"roofline_us": 534.6679695261022,
"ref_metric": 59.6044
},
{
"name": "optimizer-state-reshard",
"tier": "T2",
"why": "bandwidth kernel with a reduction/gather: shared-memory tiling and a warp reduction",
"frac": 0.1181,
"analysis": {
"grouped": false,
"custom_conv": false,
"epilogue": false,
"n_mm": 0,
"sdpa": false,
"conv": false,
"reduce": true,
"masky": false,
"quant": false
},
"metric": "GB/s",
"roofline_us": 802.3745752871037,
"ref_metric": 566.7412
},
{
"name": "paged-kv-append",
"tier": "T2",
"why": "bandwidth kernel with a reduction/gather: shared-memory tiling and a warp reduction",
"frac": 0.1385,
"analysis": {
"grouped": true,
"custom_conv": false,
"epilogue": false,
"n_mm": 0,
"sdpa": false,
"conv": false,
"reduce": false,
"masky": true,
"quant": false
},
"metric": "GB/s",
"roofline_us": 629.8926472663879,
"ref_metric": 664.5607
},
{
"name": "path-attn-backward",
"tier": "T3",
"why": "attention with custom masking/sparsity: no library kernel applies as-is, so the tiled online-softmax pipeline is written by hand",
"frac": 0.0008,
"analysis": {
"grouped": false,
"custom_conv": false,
"epilogue": false,
"n_mm": 14,
"sdpa": false,
"conv": false,
"reduce": true,
"masky": true,
"quant": false
},
"metric": "TFLOP/s",
"roofline_us": 865.1291267657143,
"ref_metric": 0.5947
},
{
"name": "path-attn-forward",
"tier": "T3",
"why": "attention with custom masking/sparsity: no library kernel applies as-is, so the tiled online-softmax pipeline is written by hand",
"frac": 0.0007,
"analysis": {
"grouped": false,
"custom_conv": false,
"epilogue": false,
"n_mm": 14,
"sdpa": false,
"conv": false,
"reduce": true,
"masky": true,
"quant": false
},
"metric": "TFLOP/s",
"roofline_us": 805.306368,
"ref_metric": 0.4908
},
{
"name": "penalty-count-bincount-build",
"tier": "T2",
"why": "bandwidth kernel with a reduction/gather: shared-memory tiling and a warp reduction",
"frac": 0.1308,
"analysis": {
"grouped": false,
"custom_conv": false,
"epilogue": false,
"n_mm": 0,
"sdpa": false,
"conv": false,
"reduce": true,
"masky": true,
"quant": true
},
"metric": "GB/s",
"roofline_us": 417.88005813335377,
"ref_metric": 627.7482
},
{
"name": "persistent-kv-append-inline",
"tier": "T3",
"why": "quantised matmul: cuBLAS will not fuse the dequant, so the MMA pipeline is hand-written -- async copy, double buffering, fragment layouts",
"frac": null,
"analysis": {
"grouped": false,
"custom_conv": false,
"epilogue": false,
"n_mm": 1,
"sdpa": true,
"conv": false,
"reduce": true,
"masky": true,
"quant": true
},
"metric": null,
"roofline_us": null,
"ref_metric": null
},
{
"name": "persistent-layer-fused-decode",
"tier": "T3",
"why": "attention with custom masking/sparsity: no library kernel applies as-is, so the tiled online-softmax pipeline is written by hand",
"frac": null,
"analysis": {
"grouped": false,
"custom_conv": false,
"epilogue": true,
"n_mm": 8,
"sdpa": true,
"conv": false,
"reduce": true,
"masky": true,
"quant": false
},
"metric": null,
"roofline_us": null,
"ref_metric": null
},
{
"name": "persistent-rmsnorm-qkv-fused",
"tier": "T3",
"why": "quantised matmul: cuBLAS will not fuse the dequant, so the MMA pipeline is hand-written -- async copy, double buffering, fragment layouts",
"frac": null,
"analysis": {
"grouped": false,
"custom_conv": false,
"epilogue": false,
"n_mm": 3,
"sdpa": false,
"conv": false,
"reduce": true,
"masky": false,
"quant": true
},
"metric": null,
"roofline_us": null,
"ref_metric": null
},
{
"name": "persistent-scheduler-loadbalance",
"tier": "T3",
"why": "quantised matmul: cuBLAS will not fuse the dequant, so the MMA pipeline is hand-written -- async copy, double buffering, fragment layouts",
"frac": null,
"analysis": {
"grouped": true,
"custom_conv": false,
"epilogue": false,
"n_mm": 1,
"sdpa": true,
"conv": false,
"reduce": true,
"masky": false,
"quant": true
},
"metric": null,
"roofline_us": null,
"ref_metric": null
},
{
"name": "pipeline-microbatch-stash",
"tier": "T2",
"why": "bandwidth kernel with a reduction/gather: shared-memory tiling and a warp reduction",
"frac": 0.054,
"analysis": {
"grouped": false,
"custom_conv": false,
"epilogue": false,
"n_mm": 0,
"sdpa": false,
"conv": false,
"reduce": true,
"masky": false,
"quant": true
},
"metric": "GB/s",
"roofline_us": 410.15625,
"ref_metric": 259.1393
},
{
"name": "powersgd-lowrank-update",
"tier": "T3",
"why": "multi-GEMM block: needs a hand-written, pipelined MMA sequence to beat",
"frac": 0.0948,
"analysis": {
"grouped": false,
"custom_conv": false,
"epilogue": false,
"n_mm": 3,
"sdpa": false,
"conv": false,
"reduce": false,
"masky": false,
"quant": false
},
"metric": "GB/s",
"roofline_us": 392.303466796875,
"ref_metric": 454.95
},
{
"name": "prefix-cache-block-hash",
"tier": "T2",
"why": "bandwidth kernel with a reduction/gather: shared-memory tiling and a warp reduction",
"frac": 0.0004,
"analysis": {
"grouped": false,
"custom_conv": false,
"epilogue": false,
"n_mm": 0,
"sdpa": false,
"conv": false,
"reduce": true,
"masky": true,
"quant": true
},
"metric": "GB/s",
"roofline_us": 339.71020486205816,
"ref_metric": 2.1117
},
{
"name": "prefix-cache-copy-on-write",
"tier": "T2",
"why": "bandwidth kernel with a reduction/gather: shared-memory tiling and a warp reduction",
"frac": 0.0584,
"analysis": {
"grouped": true,
"custom_conv": false,
"epilogue": false,
"n_mm": 0,
"sdpa": false,
"conv": false,
"reduce": true,
"masky": true,
"quant": true
},
"metric": "GB/s",
"roofline_us": 478.7498712539673,
"ref_metric": 280.5183
},
{
"name": "prefix-lm-attention",
"tier": "T3",
"why": "attention with custom masking/sparsity: no library kernel applies as-is, so the tiled online-softmax pipeline is written by hand",
"frac": 0.0155,
"analysis": {
"grouped": false,
"custom_conv": true,
"epilogue": false,
"n_mm": 2,
"sdpa": false,
"conv": false,
"reduce": true,
"masky": true,
"quant": false
},
"metric": "TFLOP/s",
"roofline_us": 834.4867401142857,
"ref_metric": 10.8847
},
{
"name": "pyramid-kv-video-attn",
"tier": "T3",
"why": "grouped / variable-M GEMM: there is no single library call for it, so the per-group tiling and scheduling are the task",
"frac": 0.0185,
"analysis": {
"grouped": true,
"custom_conv": false,
"epilogue": false,
"n_mm": 2,
"sdpa": false,
"conv": false,
"reduce": true,
"masky": false,
"quant": false
},
"metric": "TFLOP/s",
"roofline_us": 610.3071831771429,
"ref_metric": 12.9318
},
{
"name": "qk-clip-attention",
"tier": "T3",
"why": "attention with custom masking/sparsity: no library kernel applies as-is, so the tiled online-softmax pipeline is written by hand",
"frac": 0.0124,
"analysis": {
"grouped": false,
"custom_conv": true,
"epilogue": false,
"n_mm": 2,
"sdpa": false,
"conv": false,
"reduce": true,
"masky": true,
"quant": false
},
"metric": "TFLOP/s",
"roofline_us": 785.3654484114286,
"ref_metric": 8.654
},
{
"name": "qk-norm-rope-kvwrite",
"tier": "T2",
"why": "bandwidth kernel with a reduction/gather: shared-memory tiling and a warp reduction",
"frac": 0.0313,
"analysis": {
"grouped": false,
"custom_conv": false,
"epilogue": false,
"n_mm": 0,
"sdpa": false,
"conv": false,
"reduce": true,
"masky": false,
"quant": false
},
"metric": "GB/s",
"roofline_us": 534.0576171875,
"ref_metric": 150.1257
},
{
"name": "qlora-nf4-dequant-gemm",
"tier": "T3",
"why": "quantised matmul: cuBLAS will not fuse the dequant, so the MMA pipeline is hand-written -- async copy, double buffering, fragment layouts",
"frac": 0.1368,
"analysis": {
"grouped": true,
"custom_conv": true,
"epilogue": false,
"n_mm": 1,
"sdpa": false,
"conv": false,
"reduce": false,
"masky": true,
"quant": true
},
"metric": "TFLOP/s",
"roofline_us": 785.3654484114286,
"ref_metric": 95.7762
},
{
"name": "quantized-optimizer-state",
"tier": "T2",
"why": "bandwidth kernel with a reduction/gather: shared-memory tiling and a warp reduction",
"frac": 0.0265,
"analysis": {
"grouped": false,
"custom_conv": false,
"epilogue": false,
"n_mm": 0,
"sdpa": false,
"conv": false,
"reduce": true,
"masky": true,
"quant": true
},
"metric": "GB/s",
"roofline_us": 931.3201904296875,
"ref_metric": 127.2957
},
{
"name": "qwen-vl-window-attention",
"tier": "T3",
"why": "grouped / variable-M GEMM: there is no single library call for it, so the per-group tiling and scheduling are the task",
"frac": 0.003,
"analysis": {
"grouped": true,
"custom_conv": false,
"epilogue": false,
"n_mm": 2,
"sdpa": false,
"conv": false,
"reduce": true,
"masky": false,
"quant": false
},
"metric": "TFLOP/s",
"roofline_us": 570.8940361142857,
"ref_metric": 2.0799
},
{
"name": "qwen3-moe-route-align",
"tier": "T2",
"why": "bandwidth kernel with a reduction/gather: shared-memory tiling and a warp reduction",
"frac": 0.0164,
"analysis": {
"grouped": true,
"custom_conv": false,
"epilogue": false,
"n_mm": 0,
"sdpa": false,
"conv": false,
"reduce": true,
"masky": true,
"quant": true
},
"metric": "GB/s",
"roofline_us": 330.90909089272225,
"ref_metric": 78.7732
},
{
"name": "qwen3-next-gated-attention",
"tier": "T3",
"why": "attention with custom masking/sparsity: no library kernel applies as-is, so the tiled online-softmax pipeline is written by hand",
"frac": 0.0187,
"analysis": {
"grouped": false,
"custom_conv": true,
"epilogue": true,
"n_mm": 3,
"sdpa": false,
"conv": false,
"reduce": true,
"masky": true,
"quant": false
},
"metric": "TFLOP/s",
"roofline_us": 2061.58430208,
"ref_metric": 13.0986
},
{
"name": "qwen3-next-gated-attn-decode",
"tier": "T3",
"why": "attention with custom masking/sparsity: no library kernel applies as-is, so the tiled online-softmax pipeline is written by hand",
"frac": 0.0211,
"analysis": {
"grouped": false,
"custom_conv": true,
"epilogue": true,
"n_mm": 3,
"sdpa": false,
"conv": false,
"reduce": true,
"masky": true,
"quant": false
},
"metric": "GB/s",
"roofline_us": 633.4940592447916,
"ref_metric": 101.466
},
{
"name": "qwen3-next-gdn-inproj",
"tier": "T3",
"why": "GEMM with a fused activation/gate epilogue: needs a hand-written MMA pipeline",
"frac": 0.0616,
"analysis": {
"grouped": false,
"custom_conv": false,
"epilogue": true,
"n_mm": 2,
"sdpa": false,
"conv": false,
"reduce": true,
"masky": false,
"quant": false
},
"metric": "TFLOP/s",
"roofline_us": 2368.3676803657145,
"ref_metric": 43.1066
},
{
"name": "qwen3-next-gdn-inproj-backward",
"tier": "T3",
"why": "GEMM with a fused activation/gate epilogue: needs a hand-written MMA pipeline",
"frac": 0.0582,
"analysis": {
"grouped": false,
"custom_conv": false,
"epilogue": true,
"n_mm": 2,
"sdpa": false,
"conv": false,
"reduce": true,
"masky": false,
"quant": false
},
"metric": "TFLOP/s",
"roofline_us": 3552.5515205485713,
"ref_metric": 40.7519
},
{
"name": "qwen3-next-output-gate-backward",
"tier": "T3",
"why": "GEMM with a fused activation/gate epilogue: needs a hand-written MMA pipeline",
"frac": 0.0518,
"analysis": {
"grouped": false,
"custom_conv": false,
"epilogue": true,
"n_mm": 2,
"sdpa": false,
"conv": false,
"reduce": true,
"masky": false,
"quant": false
},
"metric": "TFLOP/s",
"roofline_us": 4417.680647314286,
"ref_metric": 36.2459
},
{
"name": "radial-attention-backward",
"tier": "T3",
"why": "quantised matmul: cuBLAS will not fuse the dequant, so the MMA pipeline is hand-written -- async copy, double buffering, fragment layouts",
"frac": 0.0123,
"analysis": {
"grouped": false,
"custom_conv": false,
"epilogue": false,
"n_mm": 5,
"sdpa": false,
"conv": false,
"reduce": true,
"masky": false,
"quant": true
},
"metric": "TFLOP/s",
"roofline_us": 736.2801078857143,
"ref_metric": 8.6305
},
{
"name": "radial-attention-decay",
"tier": "T3",
"why": "quantised matmul: cuBLAS will not fuse the dequant, so the MMA pipeline is hand-written -- async copy, double buffering, fragment layouts",
"frac": 0.0171,
"analysis": {
"grouped": false,
"custom_conv": false,
"epilogue": false,
"n_mm": 2,
"sdpa": false,
"conv": false,
"reduce": true,
"masky": false,
"quant": true
},
"metric": "TFLOP/s",
"roofline_us": 4804.227703954286,
"ref_metric": 11.9661
},
{
"name": "radix-prefix-match",
"tier": "T2",
"why": "bandwidth kernel with a reduction/gather: shared-memory tiling and a warp reduction",
"frac": 0.0014,
"analysis": {
"grouped": false,
"custom_conv": false,
"epilogue": false,
"n_mm": 0,
"sdpa": false,
"conv": false,
"reduce": true,
"masky": true,
"quant": true
},
"metric": "GB/s",
"roofline_us": 442.9414262995124,
"ref_metric": 6.7531
},
{
"name": "repetition-penalty-logits",
"tier": "T2",
"why": "bandwidth kernel with a reduction/gather: shared-memory tiling and a warp reduction",
"frac": 0.084,
"analysis": {
"grouped": true,
"custom_conv": false,
"epilogue": false,
"n_mm": 0,
"sdpa": false,
"conv": false,
"reduce": false,
"masky": true,
"quant": false
},
"metric": "GB/s",
"roofline_us": 625.0035762786865,
"ref_metric": 403.0972
},
{
"name": "residual-diff-l1-reduce",
"tier": "T2",
"why": "bandwidth kernel with a reduction/gather: shared-memory tiling and a warp reduction",
"frac": 0.0403,
"analysis": {
"grouped": false,
"custom_conv": false,
"epilogue": false,
"n_mm": 0,
"sdpa": false,
"conv": false,
"reduce": true,
"masky": false,
"quant": false
},
"metric": "GB/s",
"roofline_us": 300.40740966796875,
"ref_metric": 193.4371
},
{
"name": "retention-backward",
"tier": "T3",
"why": "attention with custom masking/sparsity: no library kernel applies as-is, so the tiled online-softmax pipeline is written by hand",
"frac": 0.001,
"analysis": {
"grouped": false,
"custom_conv": false,
"epilogue": false,
"n_mm": 4,
"sdpa": false,
"conv": false,
"reduce": true,
"masky": true,
"quant": false
},
"metric": "TFLOP/s",
"roofline_us": 331.3260485485714,
"ref_metric": 0.734
},
{
"name": "retention-forward",
"tier": "T2",
"why": "bandwidth kernel with a reduction/gather: shared-memory tiling and a warp reduction",
"frac": 0.0005,
"analysis": {
"grouped": false,
"custom_conv": false,
"epilogue": false,
"n_mm": 0,
"sdpa": false,
"conv": false,
"reduce": true,
"masky": false,
"quant": false
},
"metric": "TFLOP/s",
"roofline_us": 441.7680647314286,
"ref_metric": 0.3315
},
{
"name": "rmsnorm-backward",
"tier": "T2",
"why": "bandwidth kernel with a reduction/gather: shared-memory tiling and a warp reduction",
"frac": 0.0337,
"analysis": {
"grouped": false,
"custom_conv": false,
"epilogue": false,
"n_mm": 0,
"sdpa": false,
"conv": false,
"reduce": true,
"masky": false,
"quant": false
},
"metric": "GB/s",
"roofline_us": 468.7627156575521,
"ref_metric": 161.7218
},
{
"name": "rmsnorm-quant-transpose-fused",
"tier": "T2",
"why": "bandwidth kernel with a reduction/gather: shared-memory tiling and a warp reduction",
"frac": 0.0182,
"analysis": {
"grouped": false,
"custom_conv": false,
"epilogue": false,
"n_mm": 0,
"sdpa": false,
"conv": false,
"reduce": true,
"masky": false,
"quant": true
},
"metric": "GB/s",
"roofline_us": 476.07421875,
"ref_metric": 87.2002
},
{
"name": "rolling-window-video-kv",
"tier": "T3",
"why": "attention with custom masking/sparsity: no library kernel applies as-is, so the tiled online-softmax pipeline is written by hand",
"frac": 0.0149,
"analysis": {
"grouped": false,
"custom_conv": false,
"epilogue": false,
"n_mm": 2,
"sdpa": false,
"conv": false,
"reduce": true,
"masky": true,
"quant": false
},
"metric": "TFLOP/s",
"roofline_us": 2356.0963452342858,
"ref_metric": 10.4269
},
{
"name": "rope-backward",
"tier": "T1",
"why": "elementwise/bandwidth chain: the win is fusing the passes into one",
"frac": 0.0444,
"analysis": {
"grouped": false,
"custom_conv": false,
"epilogue": false,
"n_mm": 0,
"sdpa": false,
"conv": false,
"reduce": false,
"masky": false,
"quant": false
},
"metric": "GB/s",
"roofline_us": 722.65625,
"ref_metric": 213.2365
},
{
"name": "rope-fused-kv-write",
"tier": "T1",
"why": "elementwise/bandwidth chain: the win is fusing the passes into one",
"frac": 0.0405,
"analysis": {
"grouped": false,
"custom_conv": false,
"epilogue": false,
"n_mm": 0,
"sdpa": false,
"conv": false,
"reduce": false,
"masky": false,
"quant": false
},
"metric": "GB/s",
"roofline_us": 312.7066294352213,
"ref_metric": 194.6383
},
{
"name": "rwkv7-backward",
"tier": "T3",
"why": "attention with custom masking/sparsity: no library kernel applies as-is, so the tiled online-softmax pipeline is written by hand",
"frac": 0.0015,
"analysis": {
"grouped": false,
"custom_conv": false,
"epilogue": false,
"n_mm": 13,
"sdpa": false,
"conv": false,
"reduce": true,
"masky": true,
"quant": false
},
"metric": "TFLOP/s",
"roofline_us": 515.39607552,
"ref_metric": 1.023
},
{
"name": "rwkv7-forward",
"tier": "T2",
"why": "bandwidth kernel with a reduction/gather: shared-memory tiling and a warp reduction",
"frac": 0.0004,
"analysis": {
"grouped": false,
"custom_conv": false,
"epilogue": false,
"n_mm": 0,
"sdpa": false,
"conv": false,
"reduce": true,
"masky": false,
"quant": false
},
"metric": "TFLOP/s",
"roofline_us": 417.22539446857144,
"ref_metric": 0.3103
},
{
"name": "rwkv7-token-shift-mix",
"tier": "T1",
"why": "elementwise/bandwidth chain: the win is fusing the passes into one",
"frac": 0.0354,
"analysis": {
"grouped": false,
"custom_conv": false,
"epilogue": false,
"n_mm": 0,
"sdpa": false,
"conv": false,
"reduce": false,
"masky": false,
"quant": false
},
"metric": "GB/s",
"roofline_us": 820.3268051147461,
"ref_metric": 169.7827
},
{
"name": "sandwich-norm-residual-fused",
"tier": "T2",
"why": "bandwidth kernel with a reduction/gather: shared-memory tiling and a warp reduction",
"frac": 0.0449,
"analysis": {
"grouped": false,
"custom_conv": false,
"epilogue": false,
"n_mm": 0,
"sdpa": false,
"conv": false,
"reduce": true,
"masky": false,
"quant": false
},
"metric": "GB/s",
"roofline_us": 937.504768371582,
"ref_metric": 215.5592
},
{
"name": "scheduler-multistep-buffer",
"tier": "T1",
"why": "elementwise/bandwidth chain: the win is fusing the passes into one",
"frac": 0.0353,
"analysis": {
"grouped": false,
"custom_conv": false,
"epilogue": false,
"n_mm": 0,
"sdpa": false,
"conv": false,
"reduce": false,
"masky": false,
"quant": false
},
"metric": "GB/s",
"roofline_us": 395.5841064453125,
"ref_metric": 169.6694
},
{
"name": "sd3-joint-attention-tripletext",
"tier": "T3",
"why": "multi-GEMM block: needs a hand-written, pipelined MMA sequence to beat",
"frac": 0.0311,
"analysis": {
"grouped": false,
"custom_conv": false,
"epilogue": false,
"n_mm": 2,
"sdpa": false,
"conv": false,
"reduce": true,
"masky": false,
"quant": false
},
"metric": "TFLOP/s",
"roofline_us": 4846.0049554285715,
"ref_metric": 21.786
},
{
"name": "sd3-mmdit-block",
"tier": "T3",
"why": "GEMM with a fused activation/gate epilogue: needs a hand-written MMA pipeline",
"frac": 0.0408,
"analysis": {
"grouped": false,
"custom_conv": false,
"epilogue": true,
"n_mm": 8,
"sdpa": false,
"conv": false,
"reduce": true,
"masky": false,
"quant": false
},
"metric": "TFLOP/s",
"roofline_us": 7395.2129536,
"ref_metric": 28.5704
},
{
"name": "sd3-pos-embed-interpolate",
"tier": "T3",
"why": "quantised matmul: cuBLAS will not fuse the dequant, so the MMA pipeline is hand-written -- async copy, double buffering, fragment layouts",
"frac": 0.025,
"analysis": {
"grouped": false,
"custom_conv": false,
"epilogue": false,
"n_mm": 1,
"sdpa": false,
"conv": false,
"reduce": false,
"masky": false,
"quant": true
},
"metric": "GB/s",
"roofline_us": 388.59049479166663,
"ref_metric": 120.0727
},
{
"name": "sd3-qkv-proj-qknorm",
"tier": "T3",
"why": "multi-GEMM block: needs a hand-written, pipelined MMA sequence to beat",
"frac": 0.0558,
"analysis": {
"grouped": false,
"custom_conv": false,
"epilogue": false,
"n_mm": 1,
"sdpa": false,
"conv": false,
"reduce": true,
"masky": false,
"quant": false
},
"metric": "TFLOP/s",
"roofline_us": 2675.522554148571,
"ref_metric": 39.0389
},
{
"name": "seqparallel-partial-norm",
"tier": "T2",
"why": "bandwidth kernel with a reduction/gather: shared-memory tiling and a warp reduction",
"frac": 0.0726,
"analysis": {
"grouped": false,
"custom_conv": false,
"epilogue": false,
"n_mm": 0,
"sdpa": false,
"conv": false,
"reduce": true,
"masky": false,
"quant": false
},
"metric": "GB/s",
"roofline_us": 390.625,
"ref_metric": 348.4383
},
{
"name": "sequence-packing-loss-mask",
"tier": "T2",
"why": "bandwidth kernel with a reduction/gather: shared-memory tiling and a warp reduction",
"frac": 0.038,
"analysis": {
"grouped": true,
"custom_conv": true,
"epilogue": false,
"n_mm": 0,
"sdpa": false,
"conv": false,
"reduce": true,
"masky": true,
"quant": false
},
"metric": "GB/s",
"roofline_us": 1223.14453125,
"ref_metric": 182.5226
},
{
"name": "sequence-unpad-repad",
"tier": "T1",
"why": "elementwise/bandwidth chain: the win is fusing the passes into one",
"frac": 0.2958,
"analysis": {
"grouped": true,
"custom_conv": false,
"epilogue": false,
"n_mm": 0,
"sdpa": false,
"conv": false,
"reduce": false,
"masky": false,
"quant": false
},
"metric": "GB/s",
"roofline_us": 863.6427678478261,
"ref_metric": 1419.9999
},
{
"name": "siglip-attention-pooled",
"tier": "T3",
"why": "multi-GEMM block: needs a hand-written, pipelined MMA sequence to beat",
"frac": 0.0249,
"analysis": {
"grouped": false,
"custom_conv": false,
"epilogue": false,
"n_mm": 4,
"sdpa": false,
"conv": false,
"reduce": true,
"masky": false,
"quant": false
},
"metric": "TFLOP/s",
"roofline_us": 386.924544,
"ref_metric": 17.4111
},
{
"name": "simple-gla-forward",
"tier": "T2",
"why": "bandwidth kernel with a reduction/gather: shared-memory tiling and a warp reduction",
"frac": 0.0005,
"analysis": {
"grouped": false,
"custom_conv": false,
"epilogue": false,
"n_mm": 0,
"sdpa": false,
"conv": false,
"reduce": true,
"masky": false,
"quant": false
},
"metric": "TFLOP/s",
"roofline_us": 589.0240863085714,
"ref_metric": 0.3156
},
{
"name": "sliding-tile-attention-3d",
"tier": "T3",
"why": "multi-GEMM block: needs a hand-written, pipelined MMA sequence to beat",
"frac": 0.0151,
"analysis": {
"grouped": false,
"custom_conv": true,
"epilogue": false,
"n_mm": 2,
"sdpa": false,
"conv": false,
"reduce": true,
"masky": false,
"quant": false
},
"metric": "TFLOP/s",
"roofline_us": 4969.890728228572,
"ref_metric": 10.5973
},
{
"name": "sliding-window-kv-evict",
"tier": "T2",
"why": "bandwidth kernel with a reduction/gather: shared-memory tiling and a warp reduction",
"frac": 0.095,
"analysis": {
"grouped": false,
"custom_conv": false,
"epilogue": false,
"n_mm": 0,
"sdpa": false,
"conv": false,
"reduce": true,
"masky": false,
"quant": true
},
"metric": "GB/s",
"roofline_us": 514.0247195959091,
"ref_metric": 455.9858
},
{
"name": "sliding-window-sink-attention",
"tier": "T3",
"why": "attention with custom masking/sparsity: no library kernel applies as-is, so the tiled online-softmax pipeline is written by hand",
"frac": 0.0014,
"analysis": {
"grouped": false,
"custom_conv": true,
"epilogue": false,
"n_mm": 2,
"sdpa": false,
"conv": false,
"reduce": true,
"masky": true,
"quant": false
},
"metric": "TFLOP/s",
"roofline_us": 368.15203766857144,
"ref_metric": 1.0046
},
{
"name": "smoothquant-fused-scale",
"tier": "T2",
"why": "bandwidth kernel with a reduction/gather: shared-memory tiling and a warp reduction",
"frac": 0.0288,
"analysis": {
"grouped": false,
"custom_conv": false,
"epilogue": false,
"n_mm": 0,
"sdpa": false,
"conv": false,
"reduce": true,
"masky": false,
"quant": true
},
"metric": "GB/s",
"roofline_us": 312.70503997802734,
"ref_metric": 138.292
},
{
"name": "snake-antialias-activation",
"tier": "T3",
"why": "convolution with custom padding/grouping/tiling: cuDNN's path for this case is not the fast one, so the tiled kernel is the task",
"frac": 0.0087,
"analysis": {
"grouped": false,
"custom_conv": true,
"epilogue": false,
"n_mm": 1,
"sdpa": false,
"conv": true,
"reduce": false,
"masky": false,
"quant": false
},
"metric": "GB/s",
"roofline_us": 416.6666666666667,
"ref_metric": 41.7805
},
{
"name": "softcap-attention",
"tier": "T3",
"why": "attention with custom masking/sparsity: no library kernel applies as-is, so the tiled online-softmax pipeline is written by hand",
"frac": 0.0111,
"analysis": {
"grouped": false,
"custom_conv": true,
"epilogue": true,
"n_mm": 2,
"sdpa": false,
"conv": false,
"reduce": true,
"masky": true,
"quant": false
},
"metric": "TFLOP/s",
"roofline_us": 785.3654484114286,
"ref_metric": 7.7425
},
{
"name": "sparse-attn-backward-3d",
"tier": "T3",
"why": "quantised matmul: cuBLAS will not fuse the dequant, so the MMA pipeline is hand-written -- async copy, double buffering, fragment layouts",
"frac": 0.0112,
"analysis": {
"grouped": false,
"custom_conv": true,
"epilogue": false,
"n_mm": 5,
"sdpa": false,
"conv": false,
"reduce": true,
"masky": true,
"quant": true
},
"metric": "TFLOP/s",
"roofline_us": 1058.4026550857143,
"ref_metric": 7.8367
},
{
"name": "sparse-attn-mask-build-3d",
"tier": "T3",
"why": "multi-GEMM block: needs a hand-written, pipelined MMA sequence to beat",
"frac": 0.0472,
"analysis": {
"grouped": false,
"custom_conv": true,
"epilogue": false,
"n_mm": 1,
"sdpa": false,
"conv": false,
"reduce": true,
"masky": false,
"quant": false
},
"metric": "GB/s",
"roofline_us": 567.626953125,
"ref_metric": 226.4278
},
{
"name": "sparse-block-worklist-build",
"tier": "T2",
"why": "bandwidth kernel with a reduction/gather: shared-memory tiling and a warp reduction",
"frac": 0.0118,
"analysis": {
"grouped": true,
"custom_conv": false,
"epilogue": false,
"n_mm": 0,
"sdpa": false,
"conv": false,
"reduce": true,
"masky": true,
"quant": true
},
"metric": "GB/s",
"roofline_us": 282.7978126394252,
"ref_metric": 56.6957
},
{
"name": "sparse24-backward-densify",
"tier": "T2",
"why": "bandwidth kernel with a reduction/gather: shared-memory tiling and a warp reduction",
"frac": 0.0392,
"analysis": {
"grouped": false,
"custom_conv": false,
"epilogue": false,
"n_mm": 0,
"sdpa": false,
"conv": false,
"reduce": false,
"masky": true,
"quant": true
},
"metric": "GB/s",
"roofline_us": 463.8671875,
"ref_metric": 188.1122
},
{
"name": "sparse24-compress-layout",
"tier": "T2",
"why": "bandwidth kernel with a reduction/gather: shared-memory tiling and a warp reduction",
"frac": 0.0147,
"analysis": {
"grouped": false,
"custom_conv": false,
"epilogue": false,
"n_mm": 0,
"sdpa": false,
"conv": false,
"reduce": true,
"masky": true,
"quant": true
},
"metric": "GB/s",
"roofline_us": 366.2109375,
"ref_metric": 70.5673
},
{
"name": "sparse24-prune-metadata",
"tier": "T2",
"why": "bandwidth kernel with a reduction/gather: shared-memory tiling and a warp reduction",
"frac": 0.0009,
"analysis": {
"grouped": false,
"custom_conv": false,
"epilogue": false,
"n_mm": 0,
"sdpa": false,
"conv": false,
"reduce": true,
"masky": true,
"quant": true
},
"metric": "GB/s",
"roofline_us": 366.2109375,
"ref_metric": 4.3205
},
{
"name": "sparse24-sparse-gemm",
"tier": "T3",
"why": "quantised matmul: cuBLAS will not fuse the dequant, so the MMA pipeline is hand-written -- async copy, double buffering, fragment layouts",
"frac": 0.0302,
"analysis": {
"grouped": false,
"custom_conv": false,
"epilogue": false,
"n_mm": 1,
"sdpa": false,
"conv": false,
"reduce": false,
"masky": true,
"quant": true
},
"metric": "TFLOP/s",
"roofline_us": 883.5361294628572,
"ref_metric": 21.1393
},
{
"name": "spatial-upsample-pixelshuffle3d",
"tier": "T2",
"why": "pixel-shuffle upsample is a layout transform, not a convolution cuDNN accelerates",
"frac": 0.0347,
"analysis": {
"grouped": false,
"custom_conv": false,
"epilogue": false,
"n_mm": 1,
"sdpa": false,
"conv": true,
"reduce": false,
"masky": false,
"quant": false
},
"metric": "TFLOP/s",
"roofline_us": 398.62665216,
"ref_metric": 24.2893
},
{
"name": "spatial-window-temporal-global",
"tier": "T3",
"why": "grouped / variable-M GEMM: there is no single library call for it, so the per-group tiling and scheduling are the task",
"frac": 0.0064,
"analysis": {
"grouped": true,
"custom_conv": true,
"epilogue": false,
"n_mm": 2,
"sdpa": false,
"conv": false,
"reduce": true,
"masky": false,
"quant": false
},
"metric": "TFLOP/s",
"roofline_us": 1751.5413503999998,
"ref_metric": 4.4759
},
{
"name": "spec-decode-tree-attention",
"tier": "T3",
"why": "attention with custom masking/sparsity: no library kernel applies as-is, so the tiled online-softmax pipeline is written by hand",
"frac": 0.01,
"analysis": {
"grouped": false,
"custom_conv": false,
"epilogue": false,
"n_mm": 2,
"sdpa": false,
"conv": false,
"reduce": true,
"masky": true,
"quant": false
},
"metric": "TFLOP/s",
"roofline_us": 791.5011159771428,
"ref_metric": 6.9869
},
{
"name": "spec-decode-verify-accept",
"tier": "T2",
"why": "bandwidth kernel with a reduction/gather: shared-memory tiling and a warp reduction",
"frac": 0.0657,
"analysis": {
"grouped": false,
"custom_conv": false,
"epilogue": false,
"n_mm": 0,
"sdpa": false,
"conv": false,
"reduce": true,
"masky": true,
"quant": true
},
"metric": "GB/s",
"roofline_us": 468.75476837158203,
"ref_metric": 315.5627
},
{
"name": "speculative-draft-kv-rollback",
"tier": "T2",
"why": "bandwidth kernel with a reduction/gather: shared-memory tiling and a warp reduction",
"frac": 0.0676,
"analysis": {
"grouped": false,
"custom_conv": false,
"epilogue": false,
"n_mm": 0,
"sdpa": false,
"conv": false,
"reduce": false,
"masky": true,
"quant": false
},
"metric": "GB/s",
"roofline_us": 466.78662300109863,
"ref_metric": 324.5761
},
{
"name": "sta-backward",
"tier": "T3",
"why": "multi-GEMM block: needs a hand-written, pipelined MMA sequence to beat",
"frac": 0.0129,
"analysis": {
"grouped": false,
"custom_conv": true,
"epilogue": false,
"n_mm": 5,
"sdpa": false,
"conv": false,
"reduce": true,
"masky": false,
"quant": false
},
"metric": "TFLOP/s",
"roofline_us": 5176.969508571429,
"ref_metric": 9.0407
},
{
"name": "sta-ragged-tile-boundary",
"tier": "T3",
"why": "multi-GEMM block: needs a hand-written, pipelined MMA sequence to beat",
"frac": 0.0137,
"analysis": {
"grouped": false,
"custom_conv": true,
"epilogue": false,
"n_mm": 2,
"sdpa": false,
"conv": false,
"reduce": true,
"masky": false,
"quant": false
},
"metric": "TFLOP/s",
"roofline_us": 570.2443183542856,
"ref_metric": 9.5739
},
{
"name": "step-distill-consistency",
"tier": "T1",
"why": "elementwise/bandwidth chain: the win is fusing the passes into one",
"frac": 0.0652,
"analysis": {
"grouped": false,
"custom_conv": false,
"epilogue": false,
"n_mm": 0,
"sdpa": false,
"conv": false,
"reduce": false,
"masky": false,
"quant": false
},
"metric": "GB/s",
"roofline_us": 494.4801330566406,
"ref_metric": 313.0097
},
{
"name": "streaming-llm-attention",
"tier": "T3",
"why": "attention with custom masking/sparsity: no library kernel applies as-is, so the tiled online-softmax pipeline is written by hand",
"frac": 0.0084,
"analysis": {
"grouped": false,
"custom_conv": false,
"epilogue": false,
"n_mm": 2,
"sdpa": false,
"conv": false,
"reduce": true,
"masky": true,
"quant": false
},
"metric": "GB/s",
"roofline_us": 429.6875,
"ref_metric": 40.3629
},
{
"name": "svg-head-classify-route",
"tier": "T3",
"why": "attention with custom masking/sparsity: no library kernel applies as-is, so the tiled online-softmax pipeline is written by hand",
"frac": 0.0202,
"analysis": {
"grouped": false,
"custom_conv": false,
"epilogue": false,
"n_mm": 8,
"sdpa": false,
"conv": false,
"reduce": true,
"masky": true,
"quant": false
},
"metric": "TFLOP/s",
"roofline_us": 2635.9403081142855,
"ref_metric": 14.1206
},
{
"name": "swiglu-backward",
"tier": "T1",
"why": "elementwise/bandwidth chain: the win is fusing the passes into one",
"frac": 0.0503,
"analysis": {
"grouped": false,
"custom_conv": false,
"epilogue": true,
"n_mm": 0,
"sdpa": false,
"conv": false,
"reduce": false,
"masky": false,
"quant": false
},
"metric": "GB/s",
"roofline_us": 520.8333333333334,
"ref_metric": 241.253
},
{
"name": "swiglu-blockwise-fp8-quant",
"tier": "T2",
"why": "bandwidth kernel with a reduction/gather: shared-memory tiling and a warp reduction",
"frac": 0.0349,
"analysis": {
"grouped": false,
"custom_conv": false,
"epilogue": true,
"n_mm": 0,
"sdpa": false,
"conv": false,
"reduce": true,
"masky": false,
"quant": true
},
"metric": "GB/s",
"roofline_us": 589.599609375,
"ref_metric": 167.3763
},
{
"name": "teacache-residual-decision",
"tier": "T2",
"why": "bandwidth kernel with a reduction/gather: shared-memory tiling and a warp reduction",
"frac": 0.0437,
"analysis": {
"grouped": false,
"custom_conv": false,
"epilogue": false,
"n_mm": 0,
"sdpa": false,
"conv": false,
"reduce": true,
"masky": true,
"quant": false
},
"metric": "GB/s",
"roofline_us": 593.3761596679688,
"ref_metric": 209.7849
},
{
"name": "temporal-downsample-strided",
"tier": "T3",
"why": "convolution with custom padding/grouping/tiling: cuDNN's path for this case is not the fast one, so the tiled kernel is the task",
"frac": 0.0405,
"analysis": {
"grouped": false,
"custom_conv": true,
"epilogue": false,
"n_mm": 1,
"sdpa": false,
"conv": true,
"reduce": false,
"masky": false,
"quant": false
},
"metric": "TFLOP/s",
"roofline_us": 414.15756068571426,
"ref_metric": 28.3249
},
{
"name": "temporal-strided-attention",
"tier": "T3",
"why": "multi-GEMM block: needs a hand-written, pipelined MMA sequence to beat",
"frac": 0.0071,
"analysis": {
"grouped": false,
"custom_conv": true,
"epilogue": false,
"n_mm": 2,
"sdpa": false,
"conv": false,
"reduce": true,
"masky": false,
"quant": false
},
"metric": "TFLOP/s",
"roofline_us": 2761.0504045714288,
"ref_metric": 4.9653
},
{
"name": "temporal-upsample-3d",
"tier": "T3",
"why": "convolution with custom padding/grouping/tiling: cuDNN's path for this case is not the fast one, so the tiled kernel is the task",
"frac": 0.0401,
"analysis": {
"grouped": false,
"custom_conv": true,
"epilogue": false,
"n_mm": 1,
"sdpa": false,
"conv": true,
"reduce": false,
"masky": false,
"quant": false
},
"metric": "TFLOP/s",
"roofline_us": 517.6969508571428,
"ref_metric": 28.0915
},
{
"name": "titans-backward",
"tier": "T3",
"why": "attention with custom masking/sparsity: no library kernel applies as-is, so the tiled online-softmax pipeline is written by hand",
"frac": 0.0008,
"analysis": {
"grouped": false,
"custom_conv": false,
"epilogue": false,
"n_mm": 7,
"sdpa": false,
"conv": false,
"reduce": true,
"masky": true,
"quant": false
},
"metric": "TFLOP/s",
"roofline_us": 441.7680647314286,
"ref_metric": 0.5635
},
{
"name": "titans-forward",
"tier": "T3",
"why": "multi-GEMM block: needs a hand-written, pipelined MMA sequence to beat",
"frac": 0.0003,
"analysis": {
"grouped": false,
"custom_conv": false,
"epilogue": false,
"n_mm": 3,
"sdpa": false,
"conv": false,
"reduce": true,
"masky": false,
"quant": false
},
"metric": "TFLOP/s",
"roofline_us": 294.5120431542857,
"ref_metric": 0.2207
},
{
"name": "trilinear-interpolate-3d",
"tier": "T1",
"why": "elementwise/bandwidth chain: the win is fusing the passes into one",
"frac": 0.0466,
"analysis": {
"grouped": false,
"custom_conv": false,
"epilogue": false,
"n_mm": 0,
"sdpa": false,
"conv": false,
"reduce": false,
"masky": false,
"quant": false
},
"metric": "GB/s",
"roofline_us": 371.34806315104163,
"ref_metric": 223.6343
},
{
"name": "ttt-linear-backward",
"tier": "T3",
"why": "attention with custom masking/sparsity: no library kernel applies as-is, so the tiled online-softmax pipeline is written by hand",
"frac": 0.0008,
"analysis": {
"grouped": false,
"custom_conv": false,
"epilogue": false,
"n_mm": 6,
"sdpa": false,
"conv": false,
"reduce": true,
"masky": true,
"quant": false
},
"metric": "TFLOP/s",
"roofline_us": 441.7680647314286,
"ref_metric": 0.5847
},
{
"name": "ttt-linear-forward",
"tier": "T3",
"why": "attention with custom masking/sparsity: no library kernel applies as-is, so the tiled online-softmax pipeline is written by hand",
"frac": 0.0043,
"analysis": {
"grouped": false,
"custom_conv": false,
"epilogue": false,
"n_mm": 6,
"sdpa": false,
"conv": false,
"reduce": true,
"masky": true,
"quant": false
},
"metric": "TFLOP/s",
"roofline_us": 392.6827242057143,
"ref_metric": 3.0383
},
{
"name": "vae-attention-block-highres",
"tier": "T3",
"why": "convolution with custom padding/grouping/tiling: cuDNN's path for this case is not the fast one, so the tiled kernel is the task",
"frac": 0.0465,
"analysis": {
"grouped": false,
"custom_conv": true,
"epilogue": false,
"n_mm": 4,
"sdpa": false,
"conv": true,
"reduce": true,
"masky": false,
"quant": false
},
"metric": "TFLOP/s",
"roofline_us": 958.5482605714286,
"ref_metric": 32.5739
},
{
"name": "vae-decode-fp8",
"tier": "T3",
"why": "quantised matmul: cuBLAS will not fuse the dequant, so the MMA pipeline is hand-written -- async copy, double buffering, fragment layouts",
"frac": 0.0482,
"analysis": {
"grouped": false,
"custom_conv": true,
"epilogue": true,
"n_mm": 1,
"sdpa": false,
"conv": true,
"reduce": true,
"masky": false,
"quant": true
},
"metric": "TFLOP/s",
"roofline_us": 660.0636123428571,
"ref_metric": 33.7674
},
{
"name": "vae-decode-tile-feather",
"tier": "T1",
"why": "elementwise/bandwidth chain: the win is fusing the passes into one",
"frac": 0.0388,
"analysis": {
"grouped": false,
"custom_conv": true,
"epilogue": false,
"n_mm": 0,
"sdpa": false,
"conv": false,
"reduce": false,
"masky": false,
"quant": false
},
"metric": "GB/s",
"roofline_us": 564.8040771484375,
"ref_metric": 186.1219
},
{
"name": "vae-encoder-downblock-fused",
"tier": "T3",
"why": "convolution with custom padding/grouping/tiling: cuDNN's path for this case is not the fast one, so the tiled kernel is the task",
"frac": 0.0374,
"analysis": {
"grouped": false,
"custom_conv": true,
"epilogue": true,
"n_mm": 1,
"sdpa": false,
"conv": true,
"reduce": true,
"masky": false,
"quant": false
},
"metric": "TFLOP/s",
"roofline_us": 706.6563379199999,
"ref_metric": 26.2041
},
{
"name": "vae-quant-conv",
"tier": "T3",
"why": "convolution with custom padding/grouping/tiling: cuDNN's path for this case is not the fast one, so the tiled kernel is the task",
"frac": 0.0307,
"analysis": {
"grouped": false,
"custom_conv": true,
"epilogue": false,
"n_mm": 2,
"sdpa": false,
"conv": true,
"reduce": false,
"masky": false,
"quant": false
},
"metric": "TFLOP/s",
"roofline_us": 511.3057865142858,
"ref_metric": 21.519
},
{
"name": "vae-resblock-fused",
"tier": "T3",
"why": "convolution with custom padding/grouping/tiling: cuDNN's path for this case is not the fast one, so the tiled kernel is the task",
"frac": 0.0388,
"analysis": {
"grouped": false,
"custom_conv": true,
"epilogue": true,
"n_mm": 1,
"sdpa": false,
"conv": true,
"reduce": true,
"masky": false,
"quant": false
},
"metric": "TFLOP/s",
"roofline_us": 1092.0170057142857,
"ref_metric": 27.1702
},
{
"name": "varlen-packed-attention",
"tier": "T3",
"why": "grouped / variable-M GEMM: there is no single library call for it, so the per-group tiling and scheduling are the task",
"frac": 0.0128,
"analysis": {
"grouped": true,
"custom_conv": true,
"epilogue": false,
"n_mm": 2,
"sdpa": false,
"conv": false,
"reduce": true,
"masky": true,
"quant": false
},
"metric": "TFLOP/s",
"roofline_us": 1156.8772008228573,
"ref_metric": 8.9276
},
{
"name": "video-attn-active-query-refresh",
"tier": "T3",
"why": "attention with custom masking/sparsity: no library kernel applies as-is, so the tiled online-softmax pipeline is written by hand",
"frac": 0.0304,
"analysis": {
"grouped": false,
"custom_conv": true,
"epilogue": false,
"n_mm": 2,
"sdpa": false,
"conv": false,
"reduce": true,
"masky": true,
"quant": false
},
"metric": "TFLOP/s",
"roofline_us": 417.22539446857144,
"ref_metric": 21.273
},
{
"name": "video-attn-head-adaptive-window",
"tier": "T3",
"why": "multi-GEMM block: needs a hand-written, pipelined MMA sequence to beat",
"frac": 0.0025,
"analysis": {
"grouped": false,
"custom_conv": true,
"epilogue": false,
"n_mm": 4,
"sdpa": false,
"conv": false,
"reduce": true,
"masky": false,
"quant": false
},
"metric": "TFLOP/s",
"roofline_us": 709.4365622857143,
"ref_metric": 1.7646
},
{
"name": "video-attn-quantized-sparse",
"tier": "T3",
"why": "quantised matmul: cuBLAS will not fuse the dequant, so the MMA pipeline is hand-written -- async copy, double buffering, fragment layouts",
"frac": 0.0202,
"analysis": {
"grouped": false,
"custom_conv": true,
"epilogue": false,
"n_mm": 2,
"sdpa": false,
"conv": false,
"reduce": true,
"masky": false,
"quant": true
},
"metric": "TFLOP/s",
"roofline_us": 883.5361294628572,
"ref_metric": 14.1205
},
{
"name": "video-attn-sink-frames",
"tier": "T3",
"why": "multi-GEMM block: needs a hand-written, pipelined MMA sequence to beat",
"frac": 0.0247,
"analysis": {
"grouped": false,
"custom_conv": false,
"epilogue": false,
"n_mm": 2,
"sdpa": false,
"conv": false,
"reduce": true,
"masky": false,
"quant": false
},
"metric": "TFLOP/s",
"roofline_us": 3926.8272420571425,
"ref_metric": 17.3186
},
{
"name": "video-attn-topk-block-select",
"tier": "T3",
"why": "multi-GEMM block: needs a hand-written, pipelined MMA sequence to beat",
"frac": 0.0326,
"analysis": {
"grouped": false,
"custom_conv": true,
"epilogue": false,
"n_mm": 2,
"sdpa": false,
"conv": false,
"reduce": true,
"masky": false,
"quant": false
},
"metric": "TFLOP/s",
"roofline_us": 3926.8272420571425,
"ref_metric": 22.7906
},
{
"name": "video-cfg-zero-star-rescale",
"tier": "T1",
"why": "elementwise/bandwidth chain: the win is fusing the passes into one",
"frac": 0.0383,
"analysis": {
"grouped": false,
"custom_conv": true,
"epilogue": false,
"n_mm": 0,
"sdpa": false,
"conv": false,
"reduce": false,
"masky": false,
"quant": false
},
"metric": "GB/s",
"roofline_us": 303.9836883544922,
"ref_metric": 183.8145
},
{
"name": "video-latent-pack-unpack",
"tier": "T1",
"why": "elementwise/bandwidth chain: the win is fusing the passes into one",
"frac": 0.1679,
"analysis": {
"grouped": false,
"custom_conv": false,
"epilogue": false,
"n_mm": 0,
"sdpa": false,
"conv": false,
"reduce": false,
"masky": false,
"quant": false
},
"metric": "GB/s",
"roofline_us": 330.4481506347656,
"ref_metric": 805.9575
},
{
"name": "vision-patch-embed-2d",
"tier": "T4",
"why": "dense GEMM: cuBLAS/CUTLASS are already at the hardware limit",
"frac": 0.0205,
"analysis": {
"grouped": false,
"custom_conv": false,
"epilogue": false,
"n_mm": 1,
"sdpa": false,
"conv": false,
"reduce": false,
"masky": false,
"quant": false
},
"metric": "TFLOP/s",
"roofline_us": 563.7144576,
"ref_metric": 14.3676
},
{
"name": "w4a16-dequant-gemm",
"tier": "T3",
"why": "quantised matmul: cuBLAS will not fuse the dequant, so the MMA pipeline is hand-written -- async copy, double buffering, fragment layouts",
"frac": 0.0453,
"analysis": {
"grouped": false,
"custom_conv": false,
"epilogue": false,
"n_mm": 1,
"sdpa": false,
"conv": false,
"reduce": false,
"masky": true,
"quant": true
},
"metric": "TFLOP/s",
"roofline_us": 392.6827242057143,
"ref_metric": 31.6845
},
{
"name": "wan-adaln-modulation-backward",
"tier": "T2",
"why": "bandwidth kernel with a reduction/gather: shared-memory tiling and a warp reduction",
"frac": 0.027,
"analysis": {
"grouped": false,
"custom_conv": false,
"epilogue": false,
"n_mm": 0,
"sdpa": false,
"conv": false,
"reduce": true,
"masky": false,
"quant": false
},
"metric": "GB/s",
"roofline_us": 901.2222290039062,
"ref_metric": 129.4269
},
{
"name": "wan-adaln-modulation-fused",
"tier": "T2",
"why": "bandwidth kernel with a reduction/gather: shared-memory tiling and a warp reduction",
"frac": 0.0384,
"analysis": {
"grouped": false,
"custom_conv": false,
"epilogue": false,
"n_mm": 0,
"sdpa": false,
"conv": false,
"reduce": true,
"masky": false,
"quant": false
},
"metric": "GB/s",
"roofline_us": 600.8148193359375,
"ref_metric": 184.2033
},
{
"name": "wan-attention-backward-video",
"tier": "T3",
"why": "multi-GEMM block: needs a hand-written, pipelined MMA sequence to beat",
"frac": 0.0111,
"analysis": {
"grouped": false,
"custom_conv": true,
"epilogue": false,
"n_mm": 5,
"sdpa": false,
"conv": false,
"reduce": true,
"masky": false,
"quant": false
},
"metric": "TFLOP/s",
"roofline_us": 8957.952,
"ref_metric": 7.7506
},
{
"name": "wan-cross-attention-backward",
"tier": "T3",
"why": "multi-GEMM block: needs a hand-written, pipelined MMA sequence to beat",
"frac": 0.0337,
"analysis": {
"grouped": false,
"custom_conv": false,
"epilogue": false,
"n_mm": 5,
"sdpa": false,
"conv": false,
"reduce": true,
"masky": false,
"quant": false
},
"metric": "TFLOP/s",
"roofline_us": 5662.3104,
"ref_metric": 23.5932
},
{
"name": "wan-cross-attention-text",
"tier": "T3",
"why": "multi-GEMM block: needs a hand-written, pipelined MMA sequence to beat",
"frac": 0.036,
"analysis": {
"grouped": false,
"custom_conv": false,
"epilogue": false,
"n_mm": 2,
"sdpa": false,
"conv": false,
"reduce": true,
"masky": false,
"quant": false
},
"metric": "TFLOP/s",
"roofline_us": 3401.8099199999997,
"ref_metric": 25.174
},
{
"name": "wan-ffn-gelu-video",
"tier": "T3",
"why": "GEMM with a fused activation/gate epilogue: needs a hand-written MMA pipeline",
"frac": 0.064,
"analysis": {
"grouped": false,
"custom_conv": false,
"epilogue": true,
"n_mm": 2,
"sdpa": false,
"conv": false,
"reduce": true,
"masky": false,
"quant": false
},
"metric": "TFLOP/s",
"roofline_us": 13249.806336,
"ref_metric": 44.7734
},
{
"name": "wan-moe-expert-switch",
"tier": "T3",
"why": "attention with custom masking/sparsity: no library kernel applies as-is, so the tiled online-softmax pipeline is written by hand",
"frac": 0.0624,
"analysis": {
"grouped": false,
"custom_conv": false,
"epilogue": true,
"n_mm": 2,
"sdpa": false,
"conv": false,
"reduce": false,
"masky": true,
"quant": false
},
"metric": "TFLOP/s",
"roofline_us": 11777.605631999999,
"ref_metric": 43.6683
},
{
"name": "wan-patch-embed-3dconv",
"tier": "T4",
"why": "plain convolution: cuDNN applies directly and already runs near roofline",
"frac": 0.0214,
"analysis": {
"grouped": false,
"custom_conv": false,
"epilogue": false,
"n_mm": 1,
"sdpa": false,
"conv": true,
"reduce": false,
"masky": false,
"quant": false
},
"metric": "GB/s",
"roofline_us": 608.45414797465,
"ref_metric": 102.6147
},
{
"name": "wan-qkv-proj-qknorm-rope",
"tier": "T3",
"why": "multi-GEMM block: needs a hand-written, pipelined MMA sequence to beat",
"frac": 0.2955,
"analysis": {
"grouped": false,
"custom_conv": false,
"epilogue": false,
"n_mm": 3,
"sdpa": false,
"conv": false,
"reduce": true,
"masky": false,
"quant": false
},
"metric": "TFLOP/s",
"roofline_us": 16986.9312,
"ref_metric": 206.8575
},
{
"name": "wan-residual-gate-add",
"tier": "T2",
"why": "bandwidth kernel with a reduction/gather: shared-memory tiling and a warp reduction",
"frac": 0.051,
"analysis": {
"grouped": false,
"custom_conv": false,
"epilogue": false,
"n_mm": 0,
"sdpa": false,
"conv": false,
"reduce": true,
"masky": false,
"quant": false
},
"metric": "GB/s",
"roofline_us": 1201.629638671875,
"ref_metric": 245.0307
},
{
"name": "wan-rope3d-apply",
"tier": "T1",
"why": "elementwise/bandwidth chain: the win is fusing the passes into one",
"frac": 0.0367,
"analysis": {
"grouped": false,
"custom_conv": false,
"epilogue": false,
"n_mm": 0,
"sdpa": false,
"conv": false,
"reduce": false,
"masky": false,
"quant": false
},
"metric": "GB/s",
"roofline_us": 1201.629638671875,
"ref_metric": 176.1293
},
{
"name": "wan-sage-qk-int8-quant",
"tier": "T2",
"why": "bandwidth kernel with a reduction/gather: shared-memory tiling and a warp reduction",
"frac": 0.0256,
"analysis": {
"grouped": false,
"custom_conv": false,
"epilogue": false,
"n_mm": 0,
"sdpa": false,
"conv": false,
"reduce": true,
"masky": false,
"quant": true
},
"metric": "GB/s",
"roofline_us": 901.2222290039062,
"ref_metric": 122.8106
},
{
"name": "wan-spatiotemporal-attention",
"tier": "T3",
"why": "multi-GEMM block: needs a hand-written, pipelined MMA sequence to beat",
"frac": 0.0103,
"analysis": {
"grouped": false,
"custom_conv": true,
"epilogue": false,
"n_mm": 2,
"sdpa": false,
"conv": false,
"reduce": true,
"masky": false,
"quant": false
},
"metric": "TFLOP/s",
"roofline_us": 7166.3616,
"ref_metric": 7.1812
},
{
"name": "wan-sta-tile-permute",
"tier": "T1",
"why": "elementwise/bandwidth chain: the win is fusing the passes into one",
"frac": 0.2003,
"analysis": {
"grouped": false,
"custom_conv": true,
"epilogue": false,
"n_mm": 0,
"sdpa": false,
"conv": false,
"reduce": false,
"masky": false,
"quant": false
},
"metric": "GB/s",
"roofline_us": 600.8148193359375,
"ref_metric": 961.5004
},
{
"name": "wan-unpatchify-head",
"tier": "T3",
"why": "grouped / variable-M GEMM: there is no single library call for it, so the per-group tiling and scheduling are the task",
"frac": 0.0172,
"analysis": {
"grouped": true,
"custom_conv": false,
"epilogue": false,
"n_mm": 1,
"sdpa": false,
"conv": false,
"reduce": true,
"masky": false,
"quant": false
},
"metric": "GB/s",
"roofline_us": 608.4521611531576,
"ref_metric": 82.4882
},
{
"name": "warp-specialized-gemv",
"tier": "T3",
"why": "GEMM with a fused activation/gate epilogue: needs a hand-written MMA pipeline",
"frac": null,
"analysis": {
"grouped": false,
"custom_conv": true,
"epilogue": true,
"n_mm": 3,
"sdpa": false,
"conv": false,
"reduce": true,
"masky": false,
"quant": false
},
"metric": null,
"roofline_us": null,
"ref_metric": null
},
{
"name": "wavelet-transform-3d",
"tier": "T1",
"why": "elementwise/bandwidth chain: the win is fusing the passes into one",
"frac": 0.0289,
"analysis": {
"grouped": false,
"custom_conv": false,
"epilogue": false,
"n_mm": 0,
"sdpa": false,
"conv": false,
"reduce": false,
"masky": false,
"quant": false
},
"metric": "GB/s",
"roofline_us": 373.53515625,
"ref_metric": 138.5753
},
{
"name": "whisper-conv-frontend",
"tier": "T4",
"why": "plain convolution: cuDNN applies directly and already runs near roofline",
"frac": 0.1619,
"analysis": {
"grouped": false,
"custom_conv": false,
"epilogue": true,
"n_mm": 2,
"sdpa": false,
"conv": true,
"reduce": false,
"masky": false,
"quant": false
},
"metric": "TFLOP/s",
"roofline_us": 566.23104,
"ref_metric": 113.3154
},
{
"name": "whisper-cross-attn-decode",
"tier": "T3",
"why": "multi-GEMM block: needs a hand-written, pipelined MMA sequence to beat",
"frac": 0.036,
"analysis": {
"grouped": false,
"custom_conv": false,
"epilogue": false,
"n_mm": 2,
"sdpa": false,
"conv": false,
"reduce": true,
"masky": false,
"quant": false
},
"metric": "GB/s",
"roofline_us": 476.837158203125,
"ref_metric": 172.8725
},
{
"name": "yarn-partial-rope",
"tier": "T2",
"why": "bandwidth kernel with a reduction/gather: shared-memory tiling and a warp reduction",
"frac": 0.0411,
"analysis": {
"grouped": false,
"custom_conv": false,
"epilogue": false,
"n_mm": 0,
"sdpa": false,
"conv": false,
"reduce": true,
"masky": false,
"quant": false
},
"metric": "GB/s",
"roofline_us": 390.7012939453125,
"ref_metric": 197.5155
},
{
"name": "zamba-shared-attn-lora-backward",
"tier": "T3",
"why": "multi-GEMM block: needs a hand-written, pipelined MMA sequence to beat",
"frac": 0.0304,
"analysis": {
"grouped": false,
"custom_conv": false,
"epilogue": false,
"n_mm": 3,
"sdpa": false,
"conv": false,
"reduce": false,
"masky": false,
"quant": false
},
"metric": "TFLOP/s",
"roofline_us": 1932.7352832,
"ref_metric": 21.2649
},
{
"name": "zamba-shared-attn-lora-qkv",
"tier": "T3",
"why": "multi-GEMM block: needs a hand-written, pipelined MMA sequence to beat",
"frac": 0.0581,
"analysis": {
"grouped": false,
"custom_conv": false,
"epilogue": false,
"n_mm": 3,
"sdpa": false,
"conv": false,
"reduce": false,
"masky": false,
"quant": false
},
"metric": "TFLOP/s",
"roofline_us": 1879.048192,
"ref_metric": 40.6886
},
{
"name": "zero3-param-regroup",
"tier": "T1",
"why": "elementwise/bandwidth chain: the win is fusing the passes into one",
"frac": 0.2053,
"analysis": {
"grouped": false,
"custom_conv": false,
"epilogue": false,
"n_mm": 0,
"sdpa": false,
"conv": false,
"reduce": false,
"masky": false,
"quant": true
},
"metric": "GB/s",
"roofline_us": 469.21253204345703,
"ref_metric": 985.2554
}
]