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Optimization principles and application performance evaluation of a multithreaded GPU using CUDA.

, , , , , and . PPoPP, page 73-82. ACM, (2008)

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Efficient performance evaluation of memory hierarchy for highly multithreaded graphics processors., , , and . PPoPP, page 23-34. ACM, (2012)C3-Flow: Compute Compression Co-Design Flow for Deep Neural Networks., and . DAC, page 86. ACM, (2019)Performance Analysis and Tuning for General Purpose Graphics Processing Units (GPGPU), , , , and . Synthesis Lectures on Computer Architecture Morgan & Claypool Publishers, (2012)DeepThin: A Self-Compressing Library for Deep Neural Networks., and . CoRR, (2018)Program optimization space pruning for a multithreaded gpu., , , , , , and . CGO, page 195-204. ACM, (2008)Optimization principles and application performance evaluation of a multithreaded GPU using CUDA., , , , , and . PPoPP, page 73-82. ACM, (2008)Implicitly Parallel Programming Models for Thousand-Core Microprocessors., , , , , , , , , and 4 other author(s). DAC, page 754-759. IEEE, (2007)CUDA-Lite: Reducing GPU Programming Complexity., , , and . LCPC, volume 5335 of Lecture Notes in Computer Science, page 1-15. Springer, (2008)Program optimization carving for GPU computing., , , , , , and . J. Parallel Distributed Comput., 68 (10): 1389-1401 (2008)SAVE: Sparsity-Aware Vector Engine for Accelerating DNN Training and Inference on CPUs., , , , , and . MICRO, page 796-810. IEEE, (2020)