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Título:
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Gem5-x: a gem5-based system level simulation framework to optimize many-core platforms
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Autores:
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Mahmood Qureshi, Yasir ;
Simon, William Andrew ;
Zapater, Marina ;
Atienza, David ;
Olcoz Herrero, Katzalin
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Tipo de documento:
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texto impreso
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Editorial:
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IEEE, 2019
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Dimensiones:
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application/pdf
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Nota general:
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info:eu-repo/semantics/openAccess
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Idiomas:
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Palabras clave:
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Estado = Publicado
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Materia = Ciencias: Informática: Inteligencia artificial
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Tipo = Sección de libro
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Resumen:
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The rapid expansion of online-based services requires novel energy and performance efficient architectures to meet power and latency constraints. Fast architectural exploration has become a key enabler in the proposal of architectural innovation. In this paper, we present gem5-X, a gem5-based system level simulation framework, and a methodology to optimize many-core systems for performance and power. As real-life case studies of many-core server workloads, we use real-time video transcoding and image classification using convolutional neural networks (CNNs). Gem5-X allows us to identify bottlenecks and evaluate the potential benefits of architectural extensions such as in-cache computing and 3D stacked High Bandwidth Memory. For real-time video transcoding, we achieve 15% speed-up using in-order cores with in-cache computing when compared to a baseline in-order system and 76% energy savings when compared to an Out-of-Order system. When using HBM, we further accelerate real-time transcoding and CNNs by up to 7% and 8% respectively.
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En línea:
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https://eprints.ucm.es/58594/1/olcoz21%20preprint.pdf
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