Sciweavers

Share
EUROPAR
2005
Springer

An Approach to Performance Prediction for Parallel Applications

9 years 4 months ago
An Approach to Performance Prediction for Parallel Applications
Abstract. Accurately modeling and predicting performance for largescale 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, multidimension...
Engin Ipek, Bronis R. de Supinski, Martin Schulz,
Added 27 Jun 2010
Updated 27 Jun 2010
Type Conference
Year 2005
Where EUROPAR
Authors Engin Ipek, Bronis R. de Supinski, Martin Schulz, Sally A. McKee
Comments (0)
books