Evaluating Active-learning Based Performance Prediction of Parallel Applications

Many scientific applications use Message Passing Interface for distributed-memory parallelism. These applications simulate complex physical phenomena and are routinely executed on supercomputers and high performance compute clusters. The applications may run for several hours or days and there may be long queue waiting times on the supercomputers. Often the application developers are unable to correctly estimate the runtime of their application for a new configuration and thus may overestimate the runtime requirements. This may increase the queue wait times along with sub-optimal job scheduling decisions. Thus accurate prediction of the performance of an application (performance modeling) is helpful. We have developed statistical models for performance prediction of MPI applications based on active learning algorithms and are able to achieve low Median Absolute Percentage Error (MdAPE) in many cases. We used five scientific applications and benchmarks – HPCG, LULESH, miniAMR, miniMD, and miniFE. We predict the execution times of these applications using various data sizes on up to 512 cores of the PARAM Sanganak supercomputer at IIT Kanpur. The statistical models gave MdAPE of 10.6, 3.5, 8.21, 12.62, and 32.25% for HPCG, LULESH, miniAMR, miniMD, and miniFE respectively.