Revisiting thread configuration of SpMV kernels on GPU: A machine learning based approach

Author:

Gao Jianhua,Ji WeixingORCID,Liu Jie,Wang Yizhuo,Shi Feng

Funder

National Natural Science Foundation of China

Publisher

Elsevier BV

Subject

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

Reference42 articles.

1. AAQAL: a machine learning-based tool for performance optimization of parallel SpMV computations using block CSR;Ahmed;Appl. Sci.,2022

2. Load-balancing sparse matrix vector product kernels on GPUs;Anzt;ACM Trans. Parallel Comput.,2020

3. Ginkgo: a modern linear operator algebra framework for high performance computing;Anzt;ACM Trans. Math. Softw.,2022

4. Reinforcement learning for automated performance tuning: initial evaluation for sparse matrix format selection;Armstrong,2008

5. Fast sparse matrix-vector multiplication on GPUs for graph applications;Ashari,2014

Cited by 1 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Optimization of Large-Scale Sparse Matrix-Vector Multiplication on Multi-GPU Systems;ACM Transactions on Architecture and Code Optimization;2024-07-08

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