NDLI: Modeling performance and energy for applications offloaded to Intel Xeon Phi

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Modeling performance and energy for applications offloaded to Intel Xeon Phi

Content Provider	ACM Digital Library
Author	Sosonkina, Masha Shen, Yuzhong Lawson, Gary Sundriyal, Vaibhav
Abstract	Accelerators are adopted to increase performance, reduce time-to-solution, and minimize energy-to-solution. However, employing them efficiently, given system and application characteristics, is often a daunting task. A goal of this work is to propose a general model that predicts performance and power requirements for an application, computational portions of which are offloaded to an accelerator. Intel Xeon Phi is the only accelerator type investigated here, and only in offload execution mode. This mode is also employed by other accelerator types, such as GPU; thus the proposed model is applicable directly. The predictive capabilities of the model are demonstrated by determining the best hardware-software configuration instances with respect to the minimum energy consumption for the CoMD proxy application executed on single or multiple nodes. For the CoMD problem sizes investigated here, the best modeled configuration was relatively close to the best measured configuration with relative error under 5% of the energy consumed for most configurations. Initial model validation also confirmed the model accuracy for a variety of model parameters, such as host computation time and power consumption on the host and accelerator. The model also provides estimates of the total data movement and computational throughput as well as of some key metrics, such as FLOPs-per-joule and bytes-per-joule, which are commonly used to study the energy-performance trade-offs.
Starting Page	1
Ending Page	8
Page Count	8
File Format	PDF
ISBN	9781450339926
DOI	10.1145/2834899.2834903
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2015-11-15
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Energy Xeon phi Heterogeneous architecture modeling Performance Hpc cluster Offload
Content Type	Text
Resource Type	Article

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