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Algorithmic Time, Energy, and Power on Candidate HPC Compute Building Blocks.

, , , and . IPDPS, page 447-457. IEEE Computer Society, (2014)

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The Sixth International Workshop on Automatic Performance Tuning (iWAPT2011)., and . ICCS, volume 4 of Procedia Computer Science, page 2124-2125. Elsevier, (2011)Tuned and wildly asynchronous stencil kernels for hybrid CPU/GPU systems., and . ICS, page 244-255. ACM, (2009)An Extensible Open-Source Compiler Infrastructure for Testing., , and . Haifa Verification Conference, volume 3875 of Lecture Notes in Computer Science, page 116-133. Springer, (2005)Efficient Communications in Training Large Scale Neural Networks., , , , , , , and . ACM Multimedia (Thematic Workshops), page 110-116. ACM, (2017)A Distributed Kernel Summation Framework for General-Dimension Machine Learning., , and . SDM, page 391-402. SIAM / Omnipress, (2012)CUP: Cluster Pruning for Compressing Deep Neural Networks., , , and . CoRR, (2019)Autotuning in High-Performance Computing Applications., , , , , , and . Proc. IEEE, 106 (11): 2068-2083 (2018)Max orientation coverage: efficient path planning to avoid collisions in the CNC milling of 3D objects., , , , and . IROS, page 6862-6869. IEEE, (2020)CUP: Cluster Pruning for Compressing Deep Neural Networks., , , , and . IEEE BigData, page 5102-5106. IEEE, (2021)Scalable All-pairs Shortest Paths for Huge Graphs on Multi-GPU Clusters., , , , , and . HPDC, page 121-131. ACM, (2021)