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Simulation-Based Performance Analysis and Tuning for a Two-Level Directly Connected System

机译:二级直接连接系统的基于仿真的性能分析和调整

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Hardware and software co-design is becoming increasingly important due to complexities in supercomputing architectures. Simulating applications before there is access to the real hardware can assist machine architects in making better design decisions that can optimize application performance. At the same time, the application and runtime can be optimized and tuned beforehand. BigSim is a simulation-based performance prediction framework designed for these purposes. It can be used to perform packet-level network simulations of parallel applications using existing parallel machines. In this paper, we demonstrate the utility of BigSim in analyzing and optimizing parallel application performance for future systems based on the PERCS network. We present simulation studies using benchmarks and real applications expected to run on future supercomputers. Future petascale systems will have more than 100,000 cores, and we present simulations at that scale.
机译:由于超级计算架构的复杂性,软硬件协同设计变得越来越重要。在可以访问真实硬件之前对应用程序进行仿真可以帮助机器设计师做出更好的设计决策,从而优化应用程序性能。同时,可以预先优化和调整应用程序和运行时。 BigSim是为此目的而设计的基于仿真的性能预测框架。它可用于使用现有并行机执行并行应用程序的数据包级网络仿真。在本文中,我们演示了BigSim在分析和优化基于PERCS网络的未来系统的并行应用程序性能方面的实用性。我们使用基准测试和预期在未来超级计算机上运行的实际应用程序提供模拟研究。未来的petascale系统将拥有超过100,000个内核,我们将以该规模提供仿真。

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