A Methodology for Developing High Fidelity Communication Models for Large-Scale Applications Targeted on Multicore Systems

Resource sharing and implementation of software stack for emerging multicore processors introduce performance and scaling challenges for large-scale scientific applications, particularly on systems with thousands of processing elements. Traditional performance optimization, tuning and modeling techniques that rely on uniform representation of computation and communication requirements are only partially useful due to the complexity of applications and underlying systems and software architecture. In this paper, the authors propose a workload modeling methodology that allows application developers to capture and represent hierarchical decomposition and distribution of their applications thereby allowing them to explore and identify optimal mapping of a workload on a target system.