Redesigning Triangular Dense Matrix Computations on GPUs-Reference-Cited by-同舟云学术

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Redesigning Triangular Dense Matrix Computations on GPUs

Published:2016 Issue: Volume: Page:477-489
ISSN:0302-9743
Container-title:Euro-Par 2016: Parallel Processing
language:
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Author:

Charara Ali,Ltaief Hatem,Keyes David

Publisher

Springer International Publishing

Link

http://link.springer.com/content/pdf/10.1007/978-3-319-43659-3_35

Reference19 articles.

1. BLAS: Basic Linear Algebra Subprograms. http://www.netlib.org/blas

2. Engineering and Scientific Subroutine Library (ESSL) and Parallel ESSL. http://www-03.ibm.com/systems/power/software/essl/

3. Intel MKL Library. http://software.intel.com/en-us/articles/intel-mkl

4. NVIDIA CUDA Toolkit. http://developer.nvidia.com/cuda-toolkit

5. The NVIDIA CUDA Basic Linear Algebra Subroutines (CUBLAS). http://developer.nvidia.com/cublas

Cited by 9 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. Addressing Irregular Patterns of Matrix Computations on GPUs and Their Impact on Applications Powered by Sparse Direct Solvers;SC22: International Conference for High Performance Computing, Networking, Storage and Analysis;2022-11

2. Efficiently Parallelizable Strassen-Based Multiplication of a Matrix by its Transpose;50th International Conference on Parallel Processing;2021-08-09

3. Design, Optimization, and Benchmarking of Dense Linear Algebra Algorithms on AMD GPUs;2020 IEEE High Performance Extreme Computing Conference (HPEC);2020-09-22

4. Efficient Block Algorithms for Parallel Sparse Triangular Solve;49th International Conference on Parallel Processing - ICPP;2020-08-17

5. Hierarchical algorithms on hierarchical architectures;Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences;2020-01-20

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