Vedaprakash Subramanian, Hongyi Ma, Liqiang Wang, En-Jui Lee, Po Chen
{"title":"使用Windows Azure和Amazon EC2快速三维震源反演","authors":"Vedaprakash Subramanian, Hongyi Ma, Liqiang Wang, En-Jui Lee, Po Chen","doi":"10.1109/SERVICES.2011.90","DOIUrl":null,"url":null,"abstract":"With its rapid development, cloud computing has been increasingly adopted by scientists for large-scale scientific computation. Compared to the traditional computing platforms such as cluster and supercomputer, cloud computing is more elastic in the support of real-time computation and more powerful in the management of large-scale datasets. This paper presents our experience on designing and implementing seismic source inversion on both cluster (specifically, MPI-based) and cloud computing (specifically, Amazon EC2 and Microsoft Windows Azure). Our experiment shows that applying cloud computing to seismic source inversion is feasible and has its advantages. In addition, we notice that both cluster and Amazon EC2 have obviously better performance than Windows Azure. Cloud computing is suited for real-time processing scientific applications but it (especially Azure) does not work well for tightly-coupled applications.","PeriodicalId":429726,"journal":{"name":"2011 IEEE World Congress on Services","volume":"5 1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2011-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"25","resultStr":"{\"title\":\"Rapid 3D Seismic Source Inversion Using Windows Azure and Amazon EC2\",\"authors\":\"Vedaprakash Subramanian, Hongyi Ma, Liqiang Wang, En-Jui Lee, Po Chen\",\"doi\":\"10.1109/SERVICES.2011.90\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"With its rapid development, cloud computing has been increasingly adopted by scientists for large-scale scientific computation. Compared to the traditional computing platforms such as cluster and supercomputer, cloud computing is more elastic in the support of real-time computation and more powerful in the management of large-scale datasets. This paper presents our experience on designing and implementing seismic source inversion on both cluster (specifically, MPI-based) and cloud computing (specifically, Amazon EC2 and Microsoft Windows Azure). Our experiment shows that applying cloud computing to seismic source inversion is feasible and has its advantages. In addition, we notice that both cluster and Amazon EC2 have obviously better performance than Windows Azure. Cloud computing is suited for real-time processing scientific applications but it (especially Azure) does not work well for tightly-coupled applications.\",\"PeriodicalId\":429726,\"journal\":{\"name\":\"2011 IEEE World Congress on Services\",\"volume\":\"5 1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2011-07-04\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"25\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2011 IEEE World Congress on Services\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/SERVICES.2011.90\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2011 IEEE World Congress on Services","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SERVICES.2011.90","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 25
摘要
随着云计算的快速发展,越来越多的科学家采用云计算进行大规模的科学计算。与集群、超级计算机等传统计算平台相比,云计算在支持实时计算方面更具弹性,在管理大规模数据集方面更强大。本文介绍了我们在集群(特别是基于mpi的)和云计算(特别是Amazon EC2和Microsoft Windows Azure)上设计和实现震源反演的经验。实验表明,将云计算应用于震源反演是可行的,具有一定的优势。此外,我们注意到集群和Amazon EC2的性能都明显优于Windows Azure。云计算适合于实时处理科学应用程序,但它(尤其是Azure)不适用于紧密耦合的应用程序。
Rapid 3D Seismic Source Inversion Using Windows Azure and Amazon EC2
With its rapid development, cloud computing has been increasingly adopted by scientists for large-scale scientific computation. Compared to the traditional computing platforms such as cluster and supercomputer, cloud computing is more elastic in the support of real-time computation and more powerful in the management of large-scale datasets. This paper presents our experience on designing and implementing seismic source inversion on both cluster (specifically, MPI-based) and cloud computing (specifically, Amazon EC2 and Microsoft Windows Azure). Our experiment shows that applying cloud computing to seismic source inversion is feasible and has its advantages. In addition, we notice that both cluster and Amazon EC2 have obviously better performance than Windows Azure. Cloud computing is suited for real-time processing scientific applications but it (especially Azure) does not work well for tightly-coupled applications.