CREST H23 年度 RA 成果報告会

CREST 平成23年度の RA の成果報告会を実施。パターンマッチング、スペクトラルクラスタリング、Random Walk with Restart、協調フィルタリング、ストリーム処理系の5つを5名の RA が実装。いずれも短期間ながら良い成果が出てきたと思います。今年も3名が引き続き、RA を続けていきます。小規模クラスタだけではなく、今年は TSUBAME を使った大規模実験およびそれに向けた最適化を実現したいと思います。

Performance Optimization for X10-based Large-scale Traffic Simulation

We are intensively working on the performance optimization for large-scale traffic simulation based on X10. What we are firstly facing is the synchronization overhead. If computational loads are not sufficient for given compute resources, the performance scalability can not be obtained due the the simulation model in that every synchronization is occurred at each step. The "step" here corresponds to "second". If the target road network is not that large, that synchronization can not be ignored. However, if we need to target larger network, such synchronization needs to be treated more carefully since all the cross points should be synchronized at every "steps". We introduce more relaxed simulation model in that the synchronization is occurred with longer interval. Determining the appropriate interval time is greatly important and the tradeoff between the interval and simulation precision exist. Thus we are currently working what's the most appropriate interval by given two facts, underlying execution environment and the precision threshold level. If we could find such a good balance, we will definitely be able to achieve more performance for given compute resources. The simulation itself had not been out of our research scope, but a lot of graph techniques could be leveraged for the performance optimization. Anyway, let's go on and publish this exciting research soon to international conferences.