- By:
- Hossain, Md Mosharaf; Hines, Thomas; Ghafoor, Sheikh; Marshall, Ryan; Amanzholov, Muzakhir; Kannan, Ramakrishnan
- Page Number:
- 1-7
- Volume:
- 21
- Book Title:
- 2018 21st International Conference of Computer and Information Technology (ICCIT)
- Publication Date:
- August 15, 2019
- Conference Name:
- 2018 21st International Conference of Computer and Information Technology (ICCIT)
- Conference Location:
- Dhaka, Bangladesh
- Conference Sponsor:
- IEEE
- View DOI Listing:
- https://doi.org/10.1109/ICCITECHN.2018.8631936
Abstract
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.
