Performance Issues of SYRK Implementations in Shared Memory Environments for Edge Cases
| Autor: | Ramakrishnan Kannan, Muzakhir S. Amanzholov, Thomas M. Hines, Ryan Marshall, Mosharaf Hossain, Sheikh K. Ghafoor |
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| Rok vydání: | 2018 |
| Předmět: |
Theoretical computer science
Rank (linear algebra) Computer science Dimensionality reduction Approximation algorithm 010103 numerical & computational mathematics 02 engineering and technology 01 natural sciences Matrix decomposition Matrix (mathematics) Shared memory 020204 information systems Linear algebra 0202 electrical engineering electronic engineering information engineering Symmetric matrix 0101 mathematics Cluster analysis |
| Zdroj: | 2018 21st International Conference of Computer and Information Technology (ICCIT). |
| DOI: | 10.1109/iccitechn.2018.8631936 |
| Popis: | The symmetric rank-k update (SYRK) is a level-3 BLAS routine commonly used by many Data Mining/Machine Learning(DM/ML) algorithms such as regression, dimensionality reduction algorithms like PCA, matrix factorization and k-mean clustering. This paper presents a comprehensive analysis of the SYRK routine under popular dense linear algebra libraries such as OpenBLAS, Intel MKL, and BLIS particularly focusing on edge cases of dense matrices (thin or fat shapes) that are common in DM/ML applications. Our work identifies some performance issues of the SYRK routine in multi-threaded shared memory environments for edge cases and discuss matrix dependent modifications for performance improvement. |
| Databáze: | OpenAIRE |
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