| # Choosing a toolchain |
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|
| Triton is the default reflex and is often **not** the right answer. Pick deliberately. |
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|
| | workload | first choice | why | |
| |---|---|---| |
| | elementwise / reduction fusion | **Triton** | fast to write, autotunes well, this is its sweet spot | |
| | standard GEMM shapes | **cuBLAS / CUTLASS** | already at the hardware limit; do not rewrite it | |
| | GEMM + unusual epilogue or prologue | **CUTLASS / CuTe DSL** | epilogue fusion with full layout control | |
| | quantised GEMM (fp8/int4/mxfp4/nvfp4) | **CUTLASS / CUDA** | the dequant must fuse into the MMA pipeline; no library does it for you | |
| | attention variants (masks, sparsity, sinks) | **CUDA or Triton** | no library kernel applies as-is; Triton is viable if the mask is regular | |
| | persistent kernel / grid-wide sync | **CUDA** | Triton can do it (atomics + spin) but it is awkward and fragile | |
| | whole-model fusion (megakernel) | **CUDA** | needs instruction scheduling, async weight staging, warp specialization | |
| | sub-byte packing, bit tricks | **CUDA** | `lop3`, `prmt`, byte permutes have no Triton expression | |
| | variable-length / ragged batching | **Triton or CUDA** | both fine; Triton's masking is convenient | |
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|
| ## Where Triton specifically loses |
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| - **Fine-grained warp specialization** — producer/consumer warp roles are not directly expressible. |
| - **TMA multicast, cluster/DSMEM (sm_90+), `tcgen05` (sm_100+)** — either unavailable or awkward. |
| - **Custom epilogues with awkward layouts** — you get what the compiler decides. |
| - **Sub-byte dtypes** — 4-bit packing needs bit manipulation Triton does not express well. |
| - **Register pressure control** — no direct control; spills appear and are hard to fix. |
| - **Persistent kernels with device-wide barriers** — possible via `tl.atomic_add` + a spin loop, and it |
| works, but the grid must stay **co-resident** or the spinning blocks deadlock. Verify block count |
| against `multi_processor_count` before relying on it. |
|
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| ## Where Triton wins |
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| Iteration speed, autotuning over `num_warps`/`num_stages`/tiles, automatic masking for ragged shapes, |
| and readable reduction code. For a bandwidth-bound fusion it will usually reach near-peak with far less |
| effort than CUDA. |
|
|
| ## The pragmatic path |
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|
| Prototype in Triton to establish correctness and a baseline number, dump its PTX/SASS to see what it |
| generated, then decide whether hand-written CUDA can beat it. Frequently the Triton version is already |
| at bandwidth and the answer is to stop; for MMA-heavy work it usually is not. |
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