降低ib集群MPI连接内存需求:一种消息合并方法

Matthew J. Koop, T. Jones, D. Panda
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引用次数: 40

摘要

高性能计算领域的集群规模一直在以相当快的速度增长。在这些集群中,主要的编程模型是消息传递接口(Message Passing Interface, MPI),因此MPI库在资源使用和性能方面起着关键作用。为了获得最大的性能,许多集群在计算节点之间部署了高速互连。InfiniBand是这样一种互连,由于其各种特性(包括远程数据内存访问(RDMA)和高性能)而越来越受欢迎。因此,它被部署在大量的集群中,并被选为美国能源部Tri-Labs容量集群的标准互连。随着这些集群规模的增长,必须小心确保资源使用不会随着规模的扩大而大幅增加。特别是,MPI库资源的使用不应该以耗尽节点内存或使用户应用程序挨饿的速度增长。在本文中,我们展示了在创建所有连接时的当前内存使用情况,并设计了一种消息合并方法来显着降低内存使用。我们的模型显示,对于8K进程,MVAPICH的默认配置可以增加到每个进程1GB,而我们的增强功能将使用率降低到每个进程120 MB左右,同时保持近乎相同的性能。我们已经在一个575个节点的集群上验证了我们的设计,并显示各种应用程序的性能没有下降。我们还将可实现的消息率提高了150%以上。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Reducing Connection Memory Requirements of MPI for InfiniBand Clusters: A Message Coalescing Approach
Clusters in the area of high-performance computing have been growing in size at a considerable rate. In these clusters, the dominate programming model is the Message Passing Interface (MPI), so the MPI library has a key role in resource usage and performance. To obtain maximal performance, many clusters deploy a high-speed interconnect between compute nodes. One such interconnect, InfiniBand, has been gaining in popularity due to its various features including Remote Data Memory Access (RDMA), and high-performance. As a result, it is being deployed in a significant number of clusters and has been chosen as the standard interconnect for capacity clusters within the DOE Tri-Labs. As these clusters grow in size, care must be taken to ensure the resource usage does not increase too significantly with scale. In particular, the MPI library resource usage should not grow at a rate which will exhaust the node memory or starve user applications. In this paper we present our findings of current memory usage when all connections are created and design a message coalescing method to decrease memory usage significantly. Our models show that the default configuration of MVAPICH can grow to 1GB per process for 8K processes, while our enhancements reduce usage by an order of magnitude to around 120 MB per process while maintaining near-equal performance. We have validated our design on a 575-node cluster and shown no performance degradation for a variety of applications. We also increase the message rate attainable by over 150%.
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