TSM2X: High-performance tall-and-skinny matrix–matrix multiplication on GPUs

Author:

Rivera CodyORCID,Chen Jieyang,Xiong Nan,Zhang Jing,Song Shuaiwen Leon,Tao DingwenORCID

Funder

National Science Foundation Directorate for Computer and Information Science and Engineering

Publisher

Elsevier BV

Subject

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

Reference38 articles.

1. Kblas: An optimized library for dense matrix-vector multiplication on gpu accelerators;Abdelfattah;ACM Trans. Math. Softw. (TOMS),2016

2. Basic Linear Algebra on NVIDIA GPUs, https://developer.nvidia.com/cublas.

3. Fault Tolerant and Energy Efficient One-Sided Matrix Decompositions on Heterogeneous Systems with GPUs;Chen,2019

4. J. Chen, S. Li, Z. Chen, GPU-ABFT: Optimizing algorithm-based fault tolerance for heterogeneous systems with GPUs, in: 2016 IEEE International Conference on Networking, Architecture and Storage (NAS).

5. Fault tolerant one-sided matrix decompositions on heterogeneous systems with GPUs;Chen,2018

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