BiELL: A bisection ELLPACK-based storage format for optimizing SpMV on GPUs

Author:

Zheng Cong,Gu Shuo,Gu Tong-Xiang,Yang Bing,Liu Xing-Ping

Publisher

Elsevier BV

Subject

Artificial Intelligence,Computer Networks and Communications,Hardware and Architecture,Theoretical Computer Science,Software

Reference19 articles.

1. Optimizing Sparse Matrix–Vector Multiplication on GPUs, Tech. Rep. Research Report RC24704;Baskaran,2009

2. Efficient Sparse Matrix–Vector Multiplication on CUDA, NVIDIA Technical Report NVR-2008-004;Bell,2008

3. R. Boisvert, R. Pozo, K. Remington, B. Miller, R. Lipman, The matrix market, 2000. http://math.nist.gov/MatrixMarket/.

4. Fast conjugate gradients with multiple GPUs;Cevahir,2009

5. J.W. Choi, A. Singh, R.W. Vuduc, Model-driven autotuning of sparse matrix–vector multiply on GPUs, in: Proc. PPoPP10, India, 2010.

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1. Efficient SpMM with Kernel Switching on GPUs for Graph Neural Networks;Proceedings of the 2023 7th International Conference on High Performance Compilation, Computing and Communications;2023-06-17

2. HIRAC: A Hierarchical Accelerator with Sorting-based Packing for SpGEMMs in DNN Applications;2023 IEEE International Symposium on High-Performance Computer Architecture (HPCA);2023-02

3. AlphaSparse: Generating High Performance SpMV Codes Directly from Sparse Matrices;SC22: International Conference for High Performance Computing, Networking, Storage and Analysis;2022-11

4. AAQAL: A Machine Learning-Based Tool for Performance Optimization of Parallel SPMV Computations Using Block CSR;Applied Sciences;2022-07-13

5. An asynchronous and parallel row-wise compressed SpMV kernel on heterogeneous CPU-GPU architectures;International Journal of Embedded Systems;2022

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