Evaluating the Performance and Energy Efficiency of N-Body Codes on Multi-Core CPUs and GPUs

Ivan Zecena, Martin Burtscher, Tongdan Jin, Ziliang Zong
Department of Computer Science, Texas State University
32nd IEEE International Performance Computing and Communications Conference (IPCCC’13), 2013


   title={Evaluating the Performance and Energy Efficiency of N-Body Codes on Multi-Core CPUs and GPUs},

   author={Zecena, Ivan and Burtscher, Martin and Jin, Tongdan and Zong, Ziliang},



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N-body simulations are computation-intensive ap-plications that calculate the motion of a large number of bodies under pair-wise forces. Although different versions of n-body codes have been widely used in many scientific fields, the perfor-mance and energy efficiency of various n-body codes have not been comprehensively studied, especially when they are running on newly released multi-core CPUs and GPUs (e.g., Tesla K20). In this paper, we evaluate the performance and energy efficiency of five parallel n-body implementations on two different multi-core CPU systems and on two different types of GPUs. Our ex-perimental results show that up to 71% of the energy can be saved by using all cores of a Xeon E5620 CPU instead of only one. We find hyper-threading to be able to further reduce the energy usage and runtime, but not by as much as adding more cores does. Finally, our experiments illustrate that GPU-based acceleration using a Tesla K20c can boost the performance and energy efficiency by orders of magnitude.
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