Convolutional neural nets for estimating the run time and energy consumption of the sparse matrix-vector product
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Otros documentos de la autoría: Barreda Vayá, Maria; Dolz, Manuel F.; Castaño Álvarez, María Asunción
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Mostrar el registro completo del ítemcomunitat-uji-handle:10234/9
comunitat-uji-handle2:10234/7036
comunitat-uji-handle3:10234/8620
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Título
Convolutional neural nets for estimating the run time and energy consumption of the sparse matrix-vector productFecha de publicación
2020-08-26Editor
SageCita bibliográfica
Barreda M, Dolz MF, Castaño MA. Convolutional neural nets for estimating the run time and energy consumption of the sparse matrix-vector product. The International Journal of High Performance Computing Applications. August 2020. doi:10.1177/1094342020953196Tipo de documento
info:eu-repo/semantics/articleVersión de la editorial
https://journals.sagepub.com/doi/10.1177/1094342020953196Versión
info:eu-repo/semantics/acceptedVersionPalabras clave / Materias
Resumen
Modeling the performance and energy consumption of the sparse matrix-vector product (SpMV) is essential to perform off-line analysis and, for example, choose a target computer architecture that delivers the best ... [+]
Modeling the performance and energy consumption of the sparse matrix-vector product (SpMV) 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 SpMV, 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 SpMV 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 SpMV 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 SpMV kernel. [-]
Proyecto de investigación
project TIN2017-82972-R from the MINECO ; Plan GenT project CDEIGENT/2018/014 from the Generalitat Valenciana, Spain ; POSDOC-A/2017/11 project from the Universitat Jaume I.Derechos de acceso
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