GPU Performance Prediction Through Parallel Discrete Event Simulation and Common Sense

Guillaume Chapuis, S. Eidenbenz, N. Santhi
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引用次数: 7

Abstract

We present the GPU Module of a Performance Prediction Toolkit developed at Los Alamos National Laboratory, which enables code developers to efficiently test novel algorithmic ideas particularly for large-scale computational physics codes. The GPU Module is a heavily-parameterized model of the GPU hardware that takes as input a sequence of abstracted instructions that the user provides as a representation of the application or can also be read in from the GPU intermediate representation PTX format. These instructions are then executed in a discrete event simulation framework of the entire computing infrastructure that can include multi-GPU and also multi-node components as typically found in high performance computing applications. Our GPU Module aims at a trade-off between the cycle-accuracy of GPU simulators and the fast execution times of analytical models. This trade-off is achieved by simulating at cycle level only a portion of the computations and using this partial runtime to analytically predict the total execution of the modeled application. We present GPU models that we validate against three different benchmark applications that cover the range from bandwidth- to cycle-limited. Our runtime predictions are within an error of 20%. We then predict performance of a next-generation GPU (Nvidia’s Pascal) for the same benchmark applications.
基于并行离散事件仿真和常识的GPU性能预测
我们展示了在洛斯阿拉莫斯国家实验室开发的性能预测工具包的GPU模块,它使代码开发人员能够有效地测试新颖的算法思想,特别是针对大规模计算物理代码。GPU模块是一个高度参数化的GPU硬件模型,它将用户提供的抽象指令序列作为应用程序的表示,或者也可以从GPU中间表示PTX格式中读取。然后,这些指令在整个计算基础设施的离散事件模拟框架中执行,该框架可以包括高性能计算应用程序中常见的多gpu和多节点组件。我们的GPU模块旨在权衡GPU模拟器的周期精度和分析模型的快速执行时间。这种权衡是通过在周期级别上只模拟一部分计算并使用这个部分运行时来分析地预测建模应用程序的总执行来实现的。我们提出了GPU模型,我们针对三种不同的基准应用程序进行了验证,这些应用程序涵盖了从带宽到周期限制的范围。我们的运行时预测误差在20%以内。然后,我们预测了下一代GPU (Nvidia的Pascal)在相同基准应用程序中的性能。
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