Modeling Performance and Energy for Applications Offloaded to Intel Xeon Phi

Document Type

Conference Paper

Publication Date




Publication Title

Proceedings of the 2nd International Workshop on Hardware-Software Co-Design for High Performance Computing


7 (1-8)

Conference Name

2nd International Workshop on Hardware-Software Co-Design for High Performance Computing, 15 November 2015, Austin, Texas


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.


© 2015 Association for Computing Machinery

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Original Publication Citation

Lawson, G., Sundriyal, V., Sosonkina, M., & Shen, Y. (2015). Modeling performance and energy for applications offloaded to Intel Xeon Phi. In Proceedings of the 2nd International Workshop on Hardware-Software Co-Design for High Performance Computing (7). Association for Computing Machinery.