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Chenggang Zhao 2025-04-09 11:17:47 +08:00
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@ -8,7 +8,7 @@ Despite its lightweight design, DeepGEMM's performance matches or exceeds expert
## News
- 2025.04.09: DeepGEMM now achieves up to **1520 TFLOPS** on H800! See #74, #78, and #81 for details.
- 2025.04.09: DeepGEMM now achieves up to **1520 TFLOPS** on H800! See [#74](https://github.com/deepseek-ai/DeepGEMM/pull/74), [#78](https://github.com/deepseek-ai/DeepGEMM/pull/78), and [#81](https://github.com/deepseek-ai/DeepGEMM/pull/81) for details.
## Performance
@ -164,6 +164,8 @@ The [Tensor Memory Accelerator](https://docs.nvidia.com/cuda/hopper-tuning-guide
- Utilization of the [`stmatrix`](https://docs.nvidia.com/cuda/parallel-thread-execution/index.html#warp-level-matrix-store-instruction-stmatrix) PTX instruction
- [Register count control](https://docs.nvidia.com/cuda/parallel-thread-execution/index.html#miscellaneous-instructions-setmaxnreg) tailored for different warpgroups
- Larger block sizes
- Less bank conflicts via 3D TMA 🐳
- Overlapping as much as possible, e.g. overlapping TMA store and non-TMA RHS scaling factor load 🐳
#### A unified and optimized block scheduler