Performance and Power Analysis of HPC Workloads on Heterogenous Multi-Node Clusters
Visualitza/Obre
Cita com:
hdl:2117/117020
Tipus de documentArticle
Data publicació2018-05-04
EditorMDPI
Condicions d'accésAccés obert
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Reconeixement-NoComercial-SenseObraDerivada 4.0 Espanya
ProjecteMONT-BLANC - Mont-Blanc, European scalable and power efficient HPC platform based on low-power embedded technology (EC-FP7-288777)
MONT-BLANC 2 - Mont-Blanc 2, European scalable and power efficient HPC platform based on low-power embedded technology (EC-FP7-610402)
Mont-Blanc 3 - Mont-Blanc 3, European scalable and power efficient HPC platform based on low-power embedded technology (EC-H2020-671697)
MONT-BLANC 2 - Mont-Blanc 2, European scalable and power efficient HPC platform based on low-power embedded technology (EC-FP7-610402)
Mont-Blanc 3 - Mont-Blanc 3, European scalable and power efficient HPC platform based on low-power embedded technology (EC-H2020-671697)
Abstract
Performance analysis tools allow application developers to identify and characterize the inefficiencies that cause performance degradation in their codes, allowing for application optimizations. Due to the increasing interest in the High Performance Computing (HPC) community towards energy-efficiency issues, it is of paramount importance to be able to correlate performance and power figures within the same profiling and analysis tools. For this reason, we present a performance and energy-efficiency study aimed at demonstrating how a single tool can be used to collect most of the relevant metrics. In particular, we show how the same analysis techniques can be applicable on different architectures, analyzing the same HPC application on a high-end and a low-power cluster. The former cluster embeds Intel Haswell CPUs and NVIDIA K80 GPUs, while the latter is made up of NVIDIA Jetson TX1 boards, each hosting an Arm Cortex-A57 CPU and an NVIDIA Tegra X1 Maxwell GPU.
CitacióMantovani, F.; Calore, E. Performance and Power Analysis of HPC Workloads on Heterogenous Multi-Node Clusters. "Journal of Low Power Electronics and Applications", 4 Maig 2018, vol. 8, núm. 2, p. 1-14.
ISSN2079-9268
Versió de l'editorhttp://www.mdpi.com/2079-9268/8/2/13
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