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Conference Papers Year : 2017

Modeling Large Compute Nodes with Heterogeneous Memories with Cache-Aware Roofline Model

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Abstract

In order to fulfill modern applications needs, computing systems become more powerful, heterogeneous and complex. NUMA platforms and emerging high bandwidth memories offer new opportunities for performance improvements. However they also increase hardware and software complexity, thus making application performance analysis and optimization an even harder task. The Cache-Aware Roofline Model (CARM) is an insightful, yet simple model designed to address this issue. It provides feedback on potential applications bottlenecks and shows how far is the application performance from the achievable hardware upper-bounds. However, it does not encompass NUMA systems and next generation processors with heterogeneous memories. Yet, some application bottlenecks belong to those memory subsystems, and would benefit from the CARM insights. In this paper, we fill the missing requirements to scope recent large shared memory systems with the CARM. We provide the methodology to instantiate, and validate the model on a NUMA system as well as on the latest Xeon Phi processor equiped with configurable hybrid memory. Finally, we show the model ability to exhibits several bottlenecks of such systems, which were not supported by CARM.
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Dates and versions

hal-01622582 , version 1 (24-10-2017)

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Nicolas Denoyelle, Brice Goglin, Aleksandar Ilic, Emmanuel Jeannot, Leonel Sousa. Modeling Large Compute Nodes with Heterogeneous Memories with Cache-Aware Roofline Model. High Performance Computing systems - Performance Modeling, Benchmarking, and Simulation - 8th International Workshop, PMBS 2017, Nov 2017, Denver (CO), United States. pp.91-113, ⟨10.1007/978-3-319-72971-8_5⟩. ⟨hal-01622582⟩
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