An Approach to Performance Prediction for Parallel Applications
An Approach to Performance Prediction for Parallel Applications
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DOI：
10.1007/11549468_24
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发表时间：
2005-08
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影响因子：
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通讯作者：
Engin Ipek;B. Supinski;M. Schulz;S. Mckee
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作者：
Engin Ipek;B. Supinski;M. Schulz;S. Mckee
随着系统复杂性急剧增加，准确建模和预测大规模应用程序的性能变得越来越困难。分析预测模型很有用，但难以构建，通常范围有限，并且常常无法捕获架构和软件之间的微妙交互。相比之下，我们采用多层神经网络，根据目标平台上执行的输入数据进行训练。这种方法对于预测性能的许多方面都很有用，并且它捕获了完整的系统复杂性。我们的模型是根据训练输入集自动开发的，避免了开发分析模型所需的困难且可能容易出错的过程。本研究重点关注高性能并行应用程序 SMG2000，这是一种经过深入研究的代码，但其执行时间的变化仍未得到很好的理解。我们的模型在大型多维参数空间中预测两个大型并行平台上的性能，误差在 5%-7% 之内。
Accurately modeling and predicting performance for large-scale applications becomes increasingly difficult as system complexity scales dramatically. Analytic predictive models are useful, but are difficult to construct, usually limited in scope, and often fail to capture subtle interactions between architecture and software. In contrast, we employ multilayer neural networks trained on input data from executions on the target platform. This approach is useful for predicting many aspects of performance, and it captures full system complexity. Our models are developed automatically from the training input set, avoiding the difficult and potentially error-prone process required to develop analytic models. This study focuses on the high-performance, parallel application SMG2000, a much studied code whose variations in execution times are still not well understood. Our model predicts performance on two large-scale parallel platforms within 5%-7% error across a large, multi-dimensional parameter space.