An Approach to Performance Prediction for Parallel Applications

Conference · Tue May 17 00:00:00 EDT 2005

DOI:https://doi.org/10.1007/11549468_24· OSTI ID:878233

Ipek, E; de Supinski, B R; Schulz, M; McKee, S A

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.

• Research Organization: Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)
• Sponsoring Organization: USDOE
• DOE Contract Number: W-7405-ENG-48
• OSTI ID: 878233
• Report Number(s): UCRL-CONF-212365; TRN: US200611%%86
• Resource Relation: Journal Volume: 3648; Conference: Presented at: Euro-Par 2005, Lisboa, Portugal, Portugal, Aug 30 - Sep 02, 2005
• Country of Publication: United States
• Language: English

Related Subjects

99 GENERAL AND MISCELLANEOUS//MATHEMATICS, COMPUTING, AND INFORMATION SCIENCE
ARCHITECTURE
FORECASTING
NEURAL NETWORKS
PERFORMANCE
SIMULATION
TARGETS
TRAINING