Badi' Abdul-Wahid, Li Yu, Dinesh Rajan, Haoyun Feng, Eric Darve, Douglas Thain, Jesús A Izaguirre
{"title":"用工作队列以500纳秒/小时的速度折叠蛋白质。","authors":"Badi' Abdul-Wahid, Li Yu, Dinesh Rajan, Haoyun Feng, Eric Darve, Douglas Thain, Jesús A Izaguirre","doi":"10.1109/eScience.2012.6404429","DOIUrl":null,"url":null,"abstract":"<p><p>Molecular modeling is a field that traditionally has large computational costs. Until recently, most simulation techniques relied on long trajectories, which inherently have poor scalability. A new class of methods is proposed that requires only a large number of short calculations, and for which minimal communication between computer nodes is required. We considered one of the more accurate variants called Accelerated Weighted Ensemble Dynamics (AWE) and for which distributed computing can be made efficient. We implemented AWE using the Work Queue framework for task management and applied it to an all atom protein model (Fip35 WW domain). We can run with excellent scalability by simultaneously utilizing heterogeneous resources from multiple computing platforms such as clouds (Amazon EC2, Microsoft Azure), dedicated clusters, grids, on multiple architectures (CPU/GPU, 32/64bit), and in a dynamic environment in which processes are regularly added or removed from the pool. This has allowed us to achieve an aggregate sampling rate of over 500 ns/hour. As a comparison, a single process typically achieves 0.1 ns/hour.</p>","PeriodicalId":90293,"journal":{"name":"Proceedings ... IEEE International Conference on eScience. IEEE International Conference on eScience","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2012-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/eScience.2012.6404429","citationCount":"13","resultStr":"{\"title\":\"Folding Proteins at 500 ns/hour with Work Queue.\",\"authors\":\"Badi' Abdul-Wahid, Li Yu, Dinesh Rajan, Haoyun Feng, Eric Darve, Douglas Thain, Jesús A Izaguirre\",\"doi\":\"10.1109/eScience.2012.6404429\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Molecular modeling is a field that traditionally has large computational costs. Until recently, most simulation techniques relied on long trajectories, which inherently have poor scalability. A new class of methods is proposed that requires only a large number of short calculations, and for which minimal communication between computer nodes is required. We considered one of the more accurate variants called Accelerated Weighted Ensemble Dynamics (AWE) and for which distributed computing can be made efficient. We implemented AWE using the Work Queue framework for task management and applied it to an all atom protein model (Fip35 WW domain). We can run with excellent scalability by simultaneously utilizing heterogeneous resources from multiple computing platforms such as clouds (Amazon EC2, Microsoft Azure), dedicated clusters, grids, on multiple architectures (CPU/GPU, 32/64bit), and in a dynamic environment in which processes are regularly added or removed from the pool. This has allowed us to achieve an aggregate sampling rate of over 500 ns/hour. As a comparison, a single process typically achieves 0.1 ns/hour.</p>\",\"PeriodicalId\":90293,\"journal\":{\"name\":\"Proceedings ... IEEE International Conference on eScience. IEEE International Conference on eScience\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2012-10-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://sci-hub-pdf.com/10.1109/eScience.2012.6404429\",\"citationCount\":\"13\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings ... IEEE International Conference on eScience. IEEE International Conference on eScience\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/eScience.2012.6404429\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings ... IEEE International Conference on eScience. IEEE International Conference on eScience","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/eScience.2012.6404429","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 13
摘要
分子建模是一个传统上具有大量计算成本的领域。直到最近,大多数仿真技术都依赖于长轨迹,这本身就具有较差的可扩展性。提出了一种新的方法,它只需要大量的短计算,并且在计算机节点之间需要最少的通信。我们考虑了一种更准确的变体,称为加速加权集成动力学(AWE),分布式计算可以变得高效。我们使用工作队列框架实现了AWE任务管理,并将其应用于全原子蛋白质模型(Fip35 WW结构域)。通过同时利用来自多个计算平台的异构资源,例如云(Amazon EC2, Microsoft Azure)、专用集群、网格、多个架构(CPU/GPU, 32/64位),以及定期从池中添加或删除进程的动态环境,我们可以以出色的可扩展性运行。这使我们能够实现超过500纳秒/小时的总采样率。相比之下,单个过程通常达到0.1纳秒/小时。
Molecular modeling is a field that traditionally has large computational costs. Until recently, most simulation techniques relied on long trajectories, which inherently have poor scalability. A new class of methods is proposed that requires only a large number of short calculations, and for which minimal communication between computer nodes is required. We considered one of the more accurate variants called Accelerated Weighted Ensemble Dynamics (AWE) and for which distributed computing can be made efficient. We implemented AWE using the Work Queue framework for task management and applied it to an all atom protein model (Fip35 WW domain). We can run with excellent scalability by simultaneously utilizing heterogeneous resources from multiple computing platforms such as clouds (Amazon EC2, Microsoft Azure), dedicated clusters, grids, on multiple architectures (CPU/GPU, 32/64bit), and in a dynamic environment in which processes are regularly added or removed from the pool. This has allowed us to achieve an aggregate sampling rate of over 500 ns/hour. As a comparison, a single process typically achieves 0.1 ns/hour.