sponsored byIEEEACMThe International Conference for High Performance 
Computing, Networking, Storage and Analysis
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SCHEDULE: NOV 16-21, 2014

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New Parallelization Model of Sequential Monte Carlo Analysis with Prediction-Correction Computing

SESSION: Poster Reception


TIME: 5:15PM - 7:00PM

AUTHOR(S):Eiji Tomiyama, Hiroshi Koyama, Katsumi Hagita

ROOM:New Orleans Theater Lobby


Monte Carlo (MC) search with Metropolis judgment has been used widely to solve inverse problems; in particular, reverse Monte Carlo (RMC) analysis has been shown to make a possible configration of particles in terms of the structure factor S(q) obtained by X-ray scattering experiments. In general, MC search proceeds sequentially, because each judgement of the MC trial depends on its previous state. In the present study, we have developed a new “SimpleRMC” parallel code for RMC analysis for a system with millions of particles. In this code, two hotspots are identified and optimized for the histogram h(r) and difference of histogram Δh(r) calculations. For the histogram kernel, we achieved 339 Tflops performance (32.3% of the peak) on 8,192 nodes of the K computer using a 33,554,432-particle system. This newly developed “prediction–correction” method improves both the parallel performance of the Δh(r) calculation and its elapse time.

Chair/Author Details:

Eiji Tomiyama - Research Organization for Information Science and Technology

Hiroshi Koyama - Research Organization for Information Science and Technology

Katsumi Hagita - National Defense Academy

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