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Intelligent Optimization of Parallel and Distributed Applications

2007pp. 1–6

Bhupesh Bansal, Ümit V. Çatalyürek, Jacqueline Chame, Chun Chen, Ewa Deelman, Yolanda Gil, Mary Hall, Vijay Kumar, Tahsin Kurç, Kristina Lerman, Aiichiro Nakano, Yoonju Lee Nelson, Joel Saltz, Ashish Sharma, Priya Vashishta

Abstract

This paper describes a new project that systematically addresses the enormous complexity of mapping applications to current and future parallel platforms. By integrating the system layers - domain-specific environment, application program, compiler, run-time environment, performance models and simulation, and workflow manager - and through a systematic strategy for application mapping, our approach exploit the vast machine resources available in such parallel platforms to dramatically increase the productivity of application programmers. This project brings together computer scientists in the areas represented by the system layers (i.e., language extensions, compilers, run-time systems, workflows) together with expertise in knowledge representation and machine learning. With expert domain scientists in molecular dynamics (MD) simulation, we are developing our approach in the context of a specific application class which already targets environments consisting of several hundreds of processors. In this way, we gain valuable insight into a generalizable strategy, while simultaneously producing performance benefits for existing and important applications.

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