{"title":"MPI-IO在平行粒子输运蒙特卡罗模拟中的应用","authors":"Mo Ze-yao, Huang Zhengfeng","doi":"10.1080/10637190412331295166","DOIUrl":null,"url":null,"abstract":"Parallel computers are increasingly being used to run large-scale applications that also have huge input/output (I/O) requirements. However, many applications usually obtain poor I/O performance on parallel machines. In this paper, we will address the parallel I/O of a parallel particle transport Monte-Carlo simulation code (PTMC) on a parallel computer. This paper shows that, without careful treatments, the I/O overheads will ultimately dominate the elapsed simulation time. Fortunately, we have successfully designed the parallel MPI I/O methods for it. In particular, for a benchmark application MAP6 with 105 steps of 100,000 samples, we have elevated the speedup from 10 with 64 processors to 56 with 90 processors. Moreover, our method is scalable for a larger number of CPUs and a larger number of samples.","PeriodicalId":406098,"journal":{"name":"Parallel Algorithms and Applications","volume":"67 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2004-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Application of MPI-IO in Parallel Particle Transport Monte-Carlo Simulation\",\"authors\":\"Mo Ze-yao, Huang Zhengfeng\",\"doi\":\"10.1080/10637190412331295166\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Parallel computers are increasingly being used to run large-scale applications that also have huge input/output (I/O) requirements. However, many applications usually obtain poor I/O performance on parallel machines. In this paper, we will address the parallel I/O of a parallel particle transport Monte-Carlo simulation code (PTMC) on a parallel computer. This paper shows that, without careful treatments, the I/O overheads will ultimately dominate the elapsed simulation time. Fortunately, we have successfully designed the parallel MPI I/O methods for it. In particular, for a benchmark application MAP6 with 105 steps of 100,000 samples, we have elevated the speedup from 10 with 64 processors to 56 with 90 processors. Moreover, our method is scalable for a larger number of CPUs and a larger number of samples.\",\"PeriodicalId\":406098,\"journal\":{\"name\":\"Parallel Algorithms and Applications\",\"volume\":\"67 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2004-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Parallel Algorithms and Applications\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1080/10637190412331295166\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Parallel Algorithms and Applications","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1080/10637190412331295166","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Application of MPI-IO in Parallel Particle Transport Monte-Carlo Simulation
Parallel computers are increasingly being used to run large-scale applications that also have huge input/output (I/O) requirements. However, many applications usually obtain poor I/O performance on parallel machines. In this paper, we will address the parallel I/O of a parallel particle transport Monte-Carlo simulation code (PTMC) on a parallel computer. This paper shows that, without careful treatments, the I/O overheads will ultimately dominate the elapsed simulation time. Fortunately, we have successfully designed the parallel MPI I/O methods for it. In particular, for a benchmark application MAP6 with 105 steps of 100,000 samples, we have elevated the speedup from 10 with 64 processors to 56 with 90 processors. Moreover, our method is scalable for a larger number of CPUs and a larger number of samples.