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Haicheng Wu 2022-04-28 10:50:11 -04:00 committed by GitHub
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@ -45,7 +45,14 @@ CUTLASS 2.9 is an update to CUTLASS adding:
- [SYMM](/test/unit/gemm/device/symm_f32n_f32n_tensor_op_fast_f32_ls_sm80.cu), [HEMM](/test/unit/gemm/device/hemm_cf32h_cf32n_tensor_op_fast_f32_ls_sm80.cu)
- [CUTLASS Python](/examples/40_cutlass_py) demonstrating JIT compilation of CUTLASS kernels and a Python-based runtime using [CUDA Python](https://developer.nvidia.com/cuda-python)
- [GEMM + Softmax example](/examples/35_gemm_softmax)
- [Gather and Scatter Fusion with GEMM](/examples/36_gather_scatter_fusion) can gather inputs and scatters outputs based on indices vectors in the same GEMM kernel.
- [Back-to-back GEMM/CONV](examples/13_two_tensor_op_fusion) fully supports buffering the previous GEMM/CONV results in the shared memory for the latter one to use.
- [Transposed Convolution](/examples/34_transposed_conv2d) (a.k.a Deconvolution) support which reuses Dgrad implementation.
- [Utility functions](/tools/util/include/cutlass/util) that can pad NHWC and convert between NCHW and NHWC.
- [Small alignment implicit gemm](https://github.com/NVIDIA/cutlass/issues/242) support for Fprop/Dgrad/Wgrad so that padding is no longer mandated to use tensor cores.
- Epilogue enhancement with performance improvement, more activation functions, and more fusion patterns.
- Optimal performance using [CUDA 11.6u2](https://developer.nvidia.com/cuda-downloads)
- [Parallel GEMM splitk](https://github.com/NVIDIA/cutlass/pull/277) support in the CUTLASS profiler.
- Updates and bugfixes from the community (thanks!)
- **Deprecation announcement:** CUTLASS plans to deprecate the following:
- Maxwell and Pascal GPU architectures