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2016 International Conference on Parallel Architecture and Compilation Techniques (PACT) (2016)
Haifa, Israel
Sept. 11, 2016 to Sept. 15, 2016
ISBN: 978-1-5090-5308-7
pp: 247-259
Hongbo Rong , Parallel Computing Lab, Intel Corporation, United States
Jongsoo Park , Parallel Computing Lab, Intel Corporation, United States
Lingxiang Xiang , Parallel Computing Lab, Intel Corporation, United States
Todd A. Anderson , Parallel Computing Lab, Intel Corporation, United States
Mikhail Smelyanskiy , Parallel Computing Lab, Intel Corporation, United States
ABSTRACT
The sparse matrix is a key data structure in various domains such as high-performance computing, machine learning, and graph analytics. To maximize performance of sparse matrix operations, it is especially important to optimize across the operations and not just within individual operations. While a straightforward per-operation mapping to library routines misses optimization opportunities, manually optimizing across the boundary of library routines is time-consuming and error-prone, sacrificing productivity.
INDEX TERMS
Sparse matrices, Optimization, Context, Libraries, Linear algebra, Symmetric matrices, Productivity
CITATION
Hongbo Rong, Jongsoo Park, Lingxiang Xiang, Todd A. Anderson, Mikhail Smelyanskiy, "Sparso: Context-driven optimizations of sparse linear algebra", 2016 International Conference on Parallel Architecture and Compilation Techniques (PACT), vol. 00, no. , pp. 247-259, 2016, doi:10.1145/2967938.2967943
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