Convolutional neural nets for estimating the run time and energy consumption of the sparse matrix-vector product. (May 2021)

• Record Type: Journal Article
• Authors: Barreda, Maria; Dolz, Manuel F; Castaño, M Asunción
• Other Names: Benner Peter guest-editor.
• Is Part Of: International journal of high performance computing applications. Volume 35:Number 3(2021)
• Page Start: 268
• Page End: 281
• Publication Date: 2021-05
• Subjects: Sparse matrix-vector multiplication (SpMV) -- performance modeling -- energy modeling -- supervised learning -- convolutional neural networks (CNN) -- PageRank algorithm; High performance computing -- Periodicals; Supercomputers -- Periodicals; 004.1105
• DOI: 10.1177/1094342020953196
• Languages: English
• ISSNs: 1094-3420

Abstract

Modeling the performance and energy consumption of the sparse matrix-vector product (Sp MV) is essential to perform off-line analysis and, for example, choose a target computer architecture that delivers the best performance-energy consumption ratio. However, this task is especially complex given the memory-bounded nature and irregular memory accesses of the Sp MV, mainly dictated by the input sparse matrix. In this paper, we propose a Machine Learning (ML)-driven approach that leverages Convolutional Neural Networks (CNNs) to provide accurate estimations of the performance and energy consumption of the Sp MV kernel. The proposed CNN-based models use a blockwise approach to make the CNN architecture independent of the matrix size. These models are trained to estimate execution time as well as total, package, and DRAM energy consumption at different processor frequencies. The experimental results reveal that the overall relative error ranges between 0.5% and 14%, while at matrix level is not superior to 10%. To demonstrate the applicability and accuracy of the Sp MV CNN-based models, this study is complemented with an ad-hoc time-energy model for the PageRank algorithm, a popular algorithm for web information retrieval used by search engines, which internally realizes the Sp MV kernel.