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Optimization of Shared High-Performance Reconfigurable Computing Resources

机译:高性能共享可重构计算资源的优化

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摘要

In the field of high-performance computing, systems harboring reconfigurable devices, such as field-programmable gate arrays (FPGAs), are gaining more widespread interest. Such systems range from supercomputers with tightly coupled reconfigurable hardware to clusters with reconfigurable devices at each node. The use of these architectures for scientific computing provides an alternative for computationally demanding problems and has advantages in metrics, such as operating cost/performance and power/performance. However, performance optimization of these systems can be challenging even with knowledge of the system's characteristics. Our analytic performance model includes parameters representing the reconfigurable hardware, application load imbalance across the nodes, background user load, basic message-passing communication, and processor heterogeneity. In this article, we provide an overview of the analytical model and demonstrate its application for optimization and scheduling of high-performance reconfigurable computing (HPRC) resources. We examine cost functions for minimum runtime and other optimization problems commonly found in shared computing resources. Finally, we discuss additional scheduling issues and other potential applications of the model.
机译:在高性能计算领域,包含可重配置设备(例如现场可编程门阵列(FPGA))的系统越来越引起人们的关注。此类系统的范围从具有紧密耦合的可重新配置硬件的超级计算机到每个节点处具有可重新配置设备的群集。这些体系结构在科学计算中的使用为计算要求苛刻的问题提供了替代方法,并且在度量标准(例如运营成本/性能和功率/性能)方面具有优势。但是,即使具有系统特性的知识,这些系统的性能优化也可能具有挑战性。我们的分析性能模型包括代表可重配置硬件,跨节点的应用程序负载不平衡,后台用户负载,基本消息传递通信和处理器异构性的参数。在本文中,我们提供了分析模型的概述,并演示了该模型在优化和调度高性能可重构计算(HPRC)资源中的应用。我们检查成本函数,以最小化运行时以及共享计算资源中常见的其他优化问题。最后,我们讨论了其他调度问题和模型的其他潜在应用。

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