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cutlass/examples/82_blackwell_distributed_gemm/README.md
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Co-authored-by: yuzhai <yuzhai@nvidia.com>
2025-04-03 02:10:16 -04:00

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# Blackwell Distributed GEMM
This example implements Tensor Parallel GEMMs for the Hopper architecture with the experimental
[Distributed GEMM](../../include/cutlass/experimental/distributed) API in CUTLASS.
This example requires Blackwell GPUs with an any-to-any NVLink network.
Please refer to [REQUIREMENTS.md](REQUIREMENTS.md) for more information.
By default, the example assumes 8 GPUs (TP=8) and runs an All Gather + GEMM operation, which rotates
operand A. To run with a different number of GPUs or schedule, please refer to
[82_blackwell_distributed_gemm.cu](82_blackwell_distributed_gemm.cu).
## Getting started
Command line arguments are mostly similar to other examples:
```
--m=<int> Sets the M extent of the GEMM
--n=<int> Sets the N extent of the GEMM
--k=<int> Sets the K extent of the GEMM
--l=<int> Sets the L extent (batch) of the GEMM (default: 1)
--alpha=<f32> Epilogue scalar alpha (default: 1.0)
--beta=<f32> Epilogue scalar beta (default: 0.0)
--iterations=<int> Number of profiling iterations to perform (default: 100)
--warmup-iterations=<int> Number of warmup iterations prior to profiling (default: 10)
--eps=<f32> Threshold for error compared to reference GEMM (default: 0.0)
```
Sample run command:
```bash
./82_blackwell_distributed_gemm --m=16384 --n=106496 --k=16384 --warmup-iterations=10 --iterations=100
```
This example follows the [Hopper example](../65_distributed_gemm/) very closely, and only differs in the base GEMM kernel. For
more information you can refer to [that example](../65_distributed_gemm/README.md).