nvidia-cusparselt-cu12 0.6.2

NVIDIA cuSPARSELt

NVIDIA cuSPARSELt is a high-performance CUDA library dedicated to general matrix-matrix operations in which at least one operand is a sparse matrix:

\begin{equation*} D = Activation(\alpha op(A) \cdot op(B) + \beta op(C) + bias) \cdot scale \end{equation*}

where \(op(A)/op(B)\) refers to in-place operations such as transpose/non-transpose, and \(alpha, beta, scale\) are scalars.

The cuSPARSELt APIs allow flexibility in the algorithm/operation selection, epilogue, and matrix characteristics, including memory layout, alignment, and data types.

Download: developer.nvidia.com/cusparselt/downloads

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Examples: cuSPARSELt Example 1, cuSPARSELt Example 2

Blog post:

• Exploiting NVIDIA Ampere Structured Sparsity with cuSPARSELt

• Structured Sparsity in the NVIDIA Ampere Architecture and Applications in Search Engines

• Making the Most of Structured Sparsity in the NVIDIA Ampere Architecture

Key Features

• NVIDIA Sparse MMA tensor core support

• Mixed-precision computation support:

  Input A/B	Input C	Output D	Compute
  FP32	FP32	FP32	FP32
  FP16	FP16	FP16	FP32
  			FP16
  BF16	BF16	BF16	FP32
  INT8	INT8	INT8	INT32
  	INT32	INT32
  	FP16	FP16
  	BF16	BF16
  E4M3	FP16	E4M3	FP32
  	BF16	E4M3
  	FP16	FP16
  	BF16	BF16
  	FP32	FP32
  E5M2	FP16	E5M2	FP32
  	BF16	E5M2
  	FP16	FP16
  	BF16	BF16
  	FP32	FP32

• Matrix pruning and compression functionalities

• Activation functions, bias vector, and output scaling

• Batched computation (multiple matrices in a single run)

• GEMM Split-K mode

• Auto-tuning functionality (see cusparseLtMatmulSearch())

• NVTX ranging and Logging functionalities

Support

• Supported SM Architectures: SM 8.0, SM 8.6, SM 8.9, SM 9.0

• Supported CPU architectures and operating systems:

OS	CPU archs
Windows	x86_64
Linux	x86_64, Arm64

Documentation

Please refer to https://docs.nvidia.com/cuda/cusparselt/index.html for the cuSPARSELt documentation.

Installation

The cuSPARSELt wheel can be installed as follows:

pip install cusparselt-cuXX

where XX is the CUDA major version (currently CUDA 12 only is supported).