Quantitative Performance Analysis of BLAS Libraries on GPU Architectures

Yıl 2024, Cilt: 26 Sayı: 76, 40 - 48, 23.01.2024

Işıl Öz

https://doi.org/10.21205/deufmd.2024267606

Öz

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.

Anahtar Kelimeler

Basic linear algebra subprograms, Graphics processing units, Performance analysis

BLAS Kütüphanelerinin GPU Mimarilerindeki Nicel Performans Analizi

Öz

Temel Lineer Cebir Alt Programları (BLAS), makine öğrenmesi ve bilimsel hesaplama tarafından yaygın olarak kullanılan lineer cebir rutinleri içermektedir. BLAS rutinlerinin optimize edilmiş uygulamalarına sahip BLAS kütüphaneleri, bilgisayar sistemlerindeki paralel yürütme birimlerinden yararlanarak yüksek performans sunmaktadır. Çok sayıda çekirdeğe sahip olan grafik işlemci birimleri, hesaplama açısından ağır iş yükleri için yüksek performans sergilemektedir. Modern BLAS kütüphaneleri, veri paralelliğini kullanarak GPU mimarilerini verimli bir şekilde kullanmaktadır. Bu çalışmada, iki BLAS kütüphanesi (cuBLAS ve MAGMA) fonksiyonları analiz edilmiş, mimari özellikleri ve sınırlamaları göz önünde bulundurularak NVIDIA GPU mimarileri üzerindeki performansları değerlendirilmiştir. Performans metrikleri toplanmış ve kaynak kullanım özellikleri tespit edilmiştir. Çalışmamız, araştırmacıların ve programcıların BLAS rutinlerinin performans davranışını ve GPU kaynak kullanımını anlamalarına yardımcı olmayı amaçlamaktadır.

Anahtar Kelimeler

Temel lineer cebir alt programları, Grafik işlemci birimleri, Performans analizi

Kaynakça

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