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NiMC: Characterizing and Eliminating Network-Induced Memory Contention.

, , and . IPDPS, page 253-262. IEEE Computer Society, (2016)

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In search of sweet-spots in parallel performance monitoring., , , , and . CLUSTER, page 69-78. IEEE Computer Society, (2008)A framework for scalable, parallel performance monitoring., , , , and . Concurr. Comput. Pract. Exp., 22 (6): 720-735 (2010)A checkpoint compression study for high-performance computing systems., , and . Int. J. High Perform. Comput. Appl., 29 (4): 387-402 (2015)Virtual-machine-based emulation of future generation high-performance computing systems., , , , , , , and . Int. J. High Perform. Comput. Appl., 26 (2): 125-135 (2012)(SAI) Stalled, Active and Idle: Characterizing Power and Performance of Large-Scale Dragonfly Networks., , , , , and . CLUSTER, page 50-59. IEEE Computer Society, (2016)Using simulation to explore distributed key-value stores for extreme-scale system services., , , , and . SC, page 9:1-9:12. ACM, (2013)Using Simulation to Evaluate the Performance of Resilience Strategies at Scale., , , , , and . PMBS@SC, volume 8551 of Lecture Notes in Computer Science, page 91-114. Springer, (2013)Exploring the Design Tradeoffs for Extreme-Scale High-Performance Computing System Software., , , , and . IEEE Trans. Parallel Distributed Syst., 27 (4): 1070-1084 (2016)An Optimal Algorithm for Extreme Scale Job Launching., , , , and . TrustCom/ISPA/IUCC, page 1115-1122. IEEE Computer Society, (2013)Developing an Architecture to Support the Implementation and Development of Scientific computing Applications., and . The Architecture of Scientific Software, volume 188 of IFIP Conference Proceedings, page 39-56. Kluwer, (2000)