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This repo contains the code to perform the SpMV product with the CSR, K1, AXC, and AXT formats. Using CUDA instructions and the CUSPARSE library.
This repo contains the code to perform the SpMV for the CSR, AXC, K1, and AXT formats using intrinsic instructions and the MKL library.
CSR-based SpMV on Heterogeneous Processors (Intel Broadwell, AMD Kaveri and nVidia Tegra K1)
a software library containing Sparse functions written in OpenCL
A New Format for SIMD-accelerated SpMV
CSR5-based SpMV on CPUs, GPUs and Xeon Phi
CUDA Sparse-Matrix Vector Multiplication, using Sliced Coordinate format
The CUDA Multiple Precision Arithmetic Library
Parallelized and vectorized SpMV on Intel Xeon Phi (Knights Landing, AVX512, KNL)
This is a series of GPU optimization topics. Here we will introduce how to optimize the CUDA kernel in detail. I will introduce several basic kernel optimizations, including: elementwise, reduce, sgemv, sgemm, etc. The performance of these kernels is basically at or near the theoretical limit.
Mixed and Multi-Precision SpMV for GPUs with Row-wise Precision Selection.
This package includes the implementation for four sparse linear algebra kernels: Sparse-Matrix-Vector-Multiplication (SpMV), Sparse-Triangular-Solve (SpTRSV), Sparse-Matrix-Transposition (SpTrans) and Sparse-Matrix-Matrix-Multiplication (SpMM) for Single-node Multi-GPU (scale-up) platforms such as NVIDIA DGX-1 and DGX-2.
SpMV using CUDA ACSR
SparseP is the first open-source Sparse Matrix Vector Multiplication (SpMV) software package for real-world Processing-In-Memory (PIM) architectures. SparseP is developed to evaluate and characterize the first publicly-available real-world PIM architecture, the UPMEM PIM architecture. Described by C. Giannoula et al. [https://arxiv.org/abs/2201.05072]
The SparseX sparse kernel optimization library
spGPU library for sparse linear algebra on GPUs
sparse matrix pre-processing library
This is a tuned sparse matrix dense vector multiplication(SpMV) library
Command line tool for working with matrices from the SuiteSparse Matrix Collection (sparse.tamu.edu)
A searchable Python interface to the SuiteSparse Matrix Collection
Source code of the PPoPP '22 paper: "TileSpGEMM: A Tiled Algorithm for Parallel Sparse General Matrix-Matrix Multiplication on GPUs" by Yuyao Niu, Zhengyang Lu, Haonan Ji, Shuhui Song, Zhou Jin, and Weifeng Liu.
Source code of the IPDPS '21 paper: "TileSpMV: A Tiled Algorithm for Sparse Matrix-Vector Multiplication on GPUs" by Yuyao Niu, Zhengyang Lu, Meichen Dong, Zhou Jin, Weifeng Liu, and Guangming Tan.
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