一个针对BDMPI的内存和执行模型进行优化的内存管理系统

J. Iverson, G. Karypis
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引用次数: 0

摘要

在小型系统上执行大型计算的需求越来越大,因为对大型系统的访问并不广泛,并且无法跟上数据的扩展。BDMPI最近被引入,作为用MPI编写的应用程序实现这一目标的一种方式。BDMPI允许在内存总量小于计算所需的系统上有效地执行标准MPI程序,并且显著优于其他方法。在本文中,我们提出了一个虚拟内存子系统,作为BDMPI运行时的一部分实现。我们的新虚拟内存子系统(我们称之为SBMA)绕过操作系统虚拟内存管理器来利用BDMPI的节点级协作多占用。使用合成应用程序进行基准测试表明,对于与BDMPI相关的用例,BDMPI- sbma系统产生的开销被分摊,从而使其执行速度与应用程序开发人员的显式数据移动一样快。此外,我们用三种不同类型的应用程序测试了SBMA,结果表明,在不修改原始MPI程序的情况下,对于所包含的应用程序,可以实现比标准BDMPI实现的速度提高2倍至12倍。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Memory Management System Optimized for BDMPI's Memory and Execution Model
There is a growing need to perform large computations on small systems, as access to large systems is not widely available and cannot keep up with the scaling of data. BDMPI was recently introduced as a way of achieving this for applications written in MPI. BDMPI allows the efficient execution of standard MPI programs on systems whose aggregate amount of memory is smaller than that required by the computations and significantly outperforms other approaches. In this paper we present a virtual memory subsystem which we implemented as part of the BDMPI runtime. Our new virtual memory subsystem, which we call SBMA, bypasses the operating system virtual memory manager to take advantage of BDMPI's node-level cooperative multi-taking. Benchmarking using a synthetic application shows that for the use cases relevant to BDMPI, the overhead incurred by the BDMPI-SBMA system is amortized such that it performs as fast as explicit data movement by the application developer. Furthermore, we tested SBMA with three different classes of applications and our results show that with no modification to the original MPI program, speedups from 2×--12× over a standard BDMPI implementation can be achieved for the included applications.
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