Estimation of Execution times on Heterogeneous Supercomputer Architectures

For managing tasks efficiently in a Distributed Het erogeneous Supercomputing System (DHSS) , we re quire a thorough understanding of applications and their intelligent scheduling within the system. For this purpose, an accurate estimation of the execu tion time of applications on various architectures is needed. In this paper we present a framework to ad dress this issue. We propose two techniques, called augmented code profiling and augmented analytical benchmarking, to characterize applications and archi tectures in a DHSS, respectively. These techniques are based on code profiling and analytical benchmarking, respectively and provide a detailed architectural-dependent characterization of DHSS applications.