distributed-multi-gpu
Two-GPU tasks. The agent moves the bytes itself (NVSHMEM, CUDA IPC, a Triton kernel over peer memory); torch's own collectives are blocked inside the graded call.
8 tasks.
| task | metric | roofline | what the kernel does |
|---|---|---|---|
dist-allgather-gemm 2-GPU |
— | — | |
dist-allgather-gemm-overlap 2-GPU |
TFLOP/s | 1571 us | A sequence-parallel block starts with the mirror image of the reduce-scatter that ended the last one: each rank holds only its own slice of … |
dist-allreduce-rmsnorm 2-GPU |
GB/s | 14062 us | Every tensor-parallel transformer block ends the same way: the row-parallel projection leaves a PARTIAL sum on each rank, that partial has t… |
dist-moe-a2a-dispatch 2-GPU |
GB/s | 4688 us | Expert parallelism puts different experts on different GPUs, so after the router has spoken every token has to travel to whichever rank owns… |
dist-oneshot-allreduce 2-GPU |
GB/s | 3750 us | At the message sizes a tensor-parallel decoder actually produces — a few MB to a few tens of MB — NCCL's ring all-reduce is the wrong algori… |
dist-reduce-scatter-rmsnorm 2-GPU |
GB/s | 8438 us | Sequence-parallel tensor parallelism replaces the block-ending all-reduce with a REDUCE-SCATTER: the row-parallel projection leaves a full-s… |
dist-tp-embedding-allreduce 2-GPU |
GB/s | 14062 us | The embedding table is the one weight too large to replicate, so it is sharded over the VOCABULARY: each rank holds a slice of the rows and … |
dist-tp-fused-kernels 2-GPU |
— | — |