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ExM: High Level Dataﬂow Programming for Extreme-Scale Systems
Timothy G. Armstrong, University of Chicago; Justin M. Wozniak, Michael Wilde, and Ketan Maheshwari, Argonne National Laboratory; Daniel S. Katz, University of Chicago and Argonne National Laboratory; Matei Ripeanu, University of British Columbia; Ewing L. Lusk, Argonne National Laboratory; Ian T. Foster, University of Chicago and Argonne National Laboratory
We present here the ExM (extreme-scale many-task) programming and execution model as a practical solution to the challenges of programing the higher-level logic of complex parallel applications on current petascale and future exascale computing systems. ExM provides an expressive, high-level functional programming model that yields massive concurrency through implicit, automated parallelism. It comprises a judicious integration of dataﬂow constructs, highly parallel function evaluation, and extremely scalable task generation. It directly addresses the intertwined programmability and scalability requirements of systems with massive concurrency, while providing a programming model that may be attractive and feasible for systems of much lower scale. We describe here the beneﬁts of the ExM programming and execution model, its potential applications, and the performance of its current implementation.
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