TaiChi: A Hybrid Compression Format for Binary Sparse Matrix-Vector Multiplication on GPU
Author:
Affiliation:
1. School of Computer Science and Technology, Beijing Institute of Technology, Beijing, China
Funder
National Natural Science Foundation of China
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Computational Theory and Mathematics,Hardware and Architecture,Signal Processing
Link
http://xplorestaging.ieee.org/ielx7/71/9790018/09763312.pdf?arnumber=9763312
Reference67 articles.
1. BestSF
2. Towards Efficient SpMV on Sunway Manycore Architectures
3. Bridging the gap between deep learning and sparse matrix format selection
4. Sparse Matrix Format Selection with Multiclass SVM for SpMV on GPU
5. Automatic Selection of Sparse Matrix Representation on GPUs
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3. Large-Scale Simulation of Structural Dynamics Computing on GPU Clusters;Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis;2023-11-11
4. HASpMV: Heterogeneity-Aware Sparse Matrix-Vector Multiplication on Modern Asymmetric Multicore Processors;2023 IEEE International Conference on Cluster Computing (CLUSTER);2023-10-31
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