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Modeling synthetic aperture radar computation with Aspen.

, , , and . Int. J. High Perform. Comput. Appl., 27 (3): 255-262 (2013)

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DRAGON: breaking GPU memory capacity limits with direct NVM access., , , , and . SC, page 32:1-32:13. IEEE / ACM, (2018)A Dynamic Tracing Mechanism for Performance Analysis of OpenMP Applications., , , , and . WOMPAT, volume 2104 of Lecture Notes in Computer Science, page 53-67. Springer, (2001)Runtime Concurrency Control and Operation Scheduling for High Performance Neural Network Training., , , and . CoRR, (2018)Runtime Concurrency Control and Operation Scheduling for High Performance Neural Network Training., , , and . IPDPS, page 188-199. IEEE, (2019)A Survey of CPU-GPU Heterogeneous Computing Techniques., and . ACM Comput. Surv., 47 (4): 69:1-69:35 (2015)Kernel-level single system image for petascale computing., , , , , and . ACM SIGOPS Oper. Syst. Rev., 40 (2): 50-54 (2006)EqualWrites: Reducing Intra-Set Write Variations for Enhancing Lifetime of Non-Volatile Caches., and . IEEE Trans. Very Large Scale Integr. Syst., 24 (1): 103-114 (2016)Contemporary High Performance Computing - From Petascale toward Exascale.. Chapman and Hall / CRC computational science series CRC Press, (2013)Enabling a Highly-Scalable Global Address Space Model for Petascale Computing, , , and . CF '10: Proceedings of the 7th ACM international conference on Computing frontiers, page 207--216. New York, NY, USA, ACM, (2010)GA-GPU: extending a library-based global address spaceprogramming model for scalable heterogeneouscomputing systems., and . Conf. Computing Frontiers, page 53-64. ACM, (2012)