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Automatic tuning of MPI runtime parameter settings by using machine learning.

, , , and . Conf. Computing Frontiers, page 115-116. ACM, (2010)

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Parallelization for Multiprocessors with Memory Hierarchies., and . ACPC, volume 591 of Lecture Notes in Computer Science, page 89-101. Springer, (1991)Using a Distributed Active Tree in Java for the Parallel and Distributed Implementation of a Nested Optimization Algorithm., and . ICPP Workshops, page 244-. IEEE Computer Society, (2003)Topology-Aware OpenMP Process Scheduling., , and . IWOMP, volume 6132 of Lecture Notes in Computer Science, page 96-108. Springer, (2010)Evaluation of P3T+: A Performance Estimator for Distributed and Parallel Applications., , , and . IPDPS, page 229-234. IEEE Computer Society, (2000)On Using SPiDER to Examine and Debug Real-World Data-Parallel Applications., , , and . PaCT, volume 2127 of Lecture Notes in Computer Science, page 211-225. Springer, (2001)Automatic tuning of MPI runtime parameter settings by using machine learning., , , and . Conf. Computing Frontiers, page 115-116. ACM, (2010)Dynamic data distributions in Vienna Fortran., , , and . SC, page 284-293. ACM, (1993)A Programming Model for Tree Structured Parallel and Distributed Algorithms and its Implementation in a Java Environment.. PARCO, volume 13 of Advances in Parallel Computing, page 71-78. Elsevier, (2003)A multi-objective auto-tuning framework for parallel codes., , , , , , and . SC, page 10. IEEE/ACM, (2012)Optimizing MPI Runtime Parameter Settings by Using Machine Learning., , , and . PVM/MPI, volume 5759 of Lecture Notes in Computer Science, page 196-206. Springer, (2009)