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Mixed-parallelism, the combination of data-and task-parallelism, is a powerful way of increasing the scalability of entire classes of parallel applications on platforms comprising multiple compute clusters. While multi-cluster platforms are predominantly heterogeneous, previous work on mixed-parallel application scheduling targets only homogeneous(More)
Mixed-parallel applications can take advantage of large-scale computing platforms but scheduling them efficiently on such platforms is challenging. In this paper we compare the two main proposed approaches for solving this scheduling problem on a heterogeneous set of homogeneous clusters. We first modify previously proposed algorithms for both approaches(More)
The study of parallel and distributed applications and platforms, whether in the cluster, grid, peer-to-peer, volunteer , or cloud computing domain, often mandates empirical evaluation of proposed algorithmic and system solutions via simulation. Unlike direct experimentation via an application deployment on a real-world testbed, simulation enables fully(More)
—Simulation is a popular approach for predicting the performance of MPI applications for platforms that are not at one's disposal. It is also a way to teach the principles of parallel programming and high-performance computing to students without access to a parallel computer. In this work we present SMPI, a simulator for MPI applications that uses on-line(More)