Journal of Parallel and Distributed Computing
Early experiences with the intel many integrated cores accelerated computing technology
Proceedings of the 2011 TeraGrid Conference: Extreme Digital Discovery
Computer Science - Research and Development
Design space exploration towards a realtime and energy-aware GPGPU-based analysis of biosensor data
Computer Science - Research and Development
Energy-Aware real-time face recognition system on mobile CPU-GPU platform
ECCV'10 Proceedings of the 11th European conference on Trends and Topics in Computer Vision - Volume Part II
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Power efficiency is one of the most important issues in high performance computing (HPC) interrelated to both software and hardware. Power dissipation of a program lies on algorithm design and power features of the computer components on which the program runs. In this work, we measure and model the power consumption of large matrices multiplication on multi-core CPU and GPU platform. By incorporating major physical power constrains of hardware components with the analysis of program execution behaviors, we approach to save the overall power consumption by using multithreading CPU to control two GPU devices computing in parallel synchronously. By implementing above method on real system, we show that it can save 22% of energy and speedup the kernel execution time by 71%, compare with solving the same large matrices multiplication using single CPU and GPU combination.