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CUTLASS 2.10 - August 2022
CUTLASS is a collection of CUDA C++ template abstractions for implementing high-performance matrix-multiplication (GEMM) and related computations at all levels and scales within CUDA. It incorporates strategies for hierarchical decomposition and data movement similar to those used to implement cuBLAS and cuDNN. CUTLASS decomposes these "moving parts" into reusable, modular software components abstracted by C++ template classes. These thread-wide, warp-wide, block-wide, and device-wide primitives can be specialized and tuned via custom tiling sizes, data types, and other algorithmic policy. The resulting flexibility simplifies their use as building blocks within custom kernels and applications.
To support a wide variety of applications, CUTLASS provides extensive support for mixed-precision computations, providing specialized data-movement and multiply-accumulate abstractions for half-precision floating point (FP16), BFloat16 (BF16), Tensor Float 32 (TF32), single-precision floating point (FP32), FP32 emulation via tensor core instruction, double-precision floating point (FP64) types, integer data types (4b and 8b), and binary data types (1b). CUTLASS demonstrates warp-synchronous matrix multiply operations targeting the programmable, high-throughput Tensor Cores implemented by NVIDIA's Volta, Turing, and Ampere architectures.
CUTLASS implements high-performance Convolution via the implicit GEMM algorithm. Implicit GEMM is the formulation of a convolution operation as a GEMM thereby taking advantage of CUTLASS's modular GEMM pipeline. This allows CUTLASS to build convolutions by reusing highly optimized warp-wide GEMM components and below.
See the Quick Start Guide to get started quickly.
See the functionality listing for the list of operations supported at each level of the execution model hierarchy.
CUTLASS 2.10 is an update to CUTLASS adding:
- CUTLASS Python now supports GEMM, Convolution and Grouped GEMM for different data types as well as different epilogue flavors.
- Optimizations for CUTLASS's Grouped GEMM kernel. It can move some scheduling into the host side if applicable.
- Optimizations for GEMM+Softmax.
- Grouped GEMM for Multihead Attention is a general MHA that does not require equal sequence length in every GEMM.
- GEMM + Layer norm fusion for Ampere can fuse the layernorm into GEMMs before and after.
- GEMM Epilogue Permutation Fusion can permute the GEMM output before storing.
- Grouped convolution targeting implicit GEMM introduces the first group convolution implementation to CUTLASS. It is an Analytical implementation, not an Optimized.
- Depthwise separable convolution introduces the first depthwise convolution which is also Analytical for now.
- Standalone Layernorm and Pooling kernels.
- Back-to-back GEMM enhancements.
- Updates and bugfixes from the community (thanks!)
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Deprecation announcement: CUTLASS plans to deprecate the following:
- Maxwell and Pascal GPU architectures
- Ubuntu 16.04
- CUDA 10.2
See the CHANGELOG for a detailed listing of releases and updates.