Abstract

Many of the scientific applications that execute on large scale parallel computing platforms run in a suboptimal fashion. Frequently, modest changes or optimizations to the internal calculation of the applications can significantly reduce the time-tosolution and improves the code quality. While i... Show moreMany of the scientific applications that execute on large scale parallel computing platforms run in a suboptimal fashion. Frequently, modest changes or optimizations to the internal calculation of the applications can significantly reduce the time-tosolution and improves the code quality. While it is frequently easy to make these code modifications, it is non-trivial to know exactly which modification should be made. This optimization process requires lot of analysis and human efforts. Using simulation or manual techniques to measure the performance and identify the part of code need to be optimized of the whole source code is often too slow. Our idea is to decrease the human efforts and reduce the time for identification of code optimization by utilizing machine learning techniques to identify which code changes should be applied to certain sections of code based on a detailed performance analysis of an application Show less