Efficient SDS Simulations on Multi-GPU Nodes of XSEDE High-End Clusters

S. Schlachter, Stephen Herbein, M. Taufer, S. Ou, Sandeep Patel, Jeremy S. Logan
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Abstract

Efficiently studying Sodium Dodecyl Sulfate (SDS) molecules' formations in the presence of different molar concentrations on high-end GPU clusters whose nodes share accelerators exposes us to several challenges, including the need to dynamically adapt the job lengths. Neither virtualization nor lightweight OS solutions can easily support generality, portability, and maintainability in concert. Our solution complements rather than rewrites existing workflow and resource managers with a companion module that complements functions of the workflow manager and a wrapper module that extends functions of the resource managers. Results on the Keene land cluster show how, by using our modules, accelerated SDS simulations more efficiently use the cluster's GPUs while leading to relevant scientific observations.
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基于XSEDE高端集群多gpu节点的高效SDS仿真
在节点共享加速器的高端GPU集群上,高效地研究十二烷基硫酸钠(SDS)分子在不同摩尔浓度下的形成,给我们带来了几个挑战,包括需要动态调整工作长度。虚拟化和轻量级操作系统解决方案都无法轻松地同时支持通用性、可移植性和可维护性。我们的解决方案是对现有工作流和资源管理器的补充,而不是重写,它有一个配套模块来补充工作流管理器的功能,还有一个包装器模块来扩展资源管理器的功能。Keene陆地集群上的结果表明,通过使用我们的模块,加速SDS模拟如何更有效地利用集群的gpu,同时导致相关的科学观测。
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