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Optimizing Data Placement on GPU Memory: A Portable Approach.

, , , and . IEEE Trans. Computers, 66 (3): 473-487 (2017)

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Exploration of the Influence of Program Inputs on CMP Co-scheduling., and . Euro-Par, volume 5168 of Lecture Notes in Computer Science, page 263-273. Springer, (2008)Combining Locality Analysis with Online Proactive Job Co-scheduling in Chip Multiprocessors., , and . HiPEAC, volume 5952 of Lecture Notes in Computer Science, page 201-215. Springer, (2010)TOP: A Framework for Enabling Algorithmic Optimizations for Distance-Related Problems., , , and . Proc. VLDB Endow., 8 (10): 1046-1057 (2015)CoCoPIE: Making Mobile AI Sweet As PIE -Compression-Compilation Co-Design Goes a Long Way., , , and . CoRR, (2020)Deep reuse: streamline CNN inference on the fly via coarse-grained computation reuse., and . ICS, page 438-448. ACM, (2019)Optimizing Data Placement on GPU Memory: A Portable Approach., , , and . IEEE Trans. Computers, 66 (3): 473-487 (2017)Understanding co-run performance on CPU-GPU integrated processors: observations, insights, directions., , , , , and . Frontiers Comput. Sci., 11 (1): 130-146 (2017)Predicting locality phases for dynamic memory optimization., , and . J. Parallel Distributed Comput., 67 (7): 783-796 (2007)Do computer programs have to be as dumb as they are?: input-centric dynamic program optimizations.. VMIL@SPLASH, page 41-42. ACM, (2013)Software-level scheduling to exploit non-uniformly shared data cache on GPGPU., , and . MSPC@PLDI, page 10:1-10:2. ACM, (2013)