Please use this identifier to cite or link to this item: https://hdl.handle.net/11147/14441
Title: Quantitative Performance Analysis of BLAS Libraries on GPU Architectures
Authors: Öz, Işıl
Abstract: Basic Linear Algebra Subprograms (BLAS) are a set of linear algebra routines commonly used by machine learning applications and scientific computing. BLAS libraries with optimized implementations of BLAS routines offer high performance by exploiting parallel execution units in target computing systems. With massively large number of cores, graphics processing units (GPUs) exhibit high performance for computationally-heavy workloads. Recent BLAS libraries utilize parallel cores of GPU architectures efficiently by employing inherent data parallelism. In this study, we analyze GPU-targeted functions from two BLAS libraries, cuBLAS and MAGMA, and evaluate their performance on a single-GPU NVIDIA architecture by considering architectural features and limitations. We collect architectural performance metrics and explore resource utilization characteristics. Our work aims to help researchers and programmers to understand the performance behavior and GPU resource utilization of the BLAS routines implemented by the libraries.
URI: https://doi.org/10.21205/deufmd.2024267606
https://search.trdizin.gov.tr/tr/yayin/detay/1223639/quantitative-performance-analysis-of-blas-libraries-on-gpu-architectures
https://hdl.handle.net/11147/14441
ISSN: 1302-9304
2547-958X
Appears in Collections:TR Dizin İndeksli Yayınlar / TR Dizin Indexed Publications Collection

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