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Exploiting historical data: Pruning autotuning spaces and estimating the number of tuning steps.

, , , and . Concurr. Comput. Pract. Exp., (2020)

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A GPU acceleration of 3-D Fourier reconstruction in cryo-EM., , , and . Int. J. High Perform. Comput. Appl., (2019)Using hardware performance counters to speed up autotuning convergence on GPUs., , , , and . J. Parallel Distributed Comput., (2022)Job Provenance - Insight into Very Large Provenance Datasets., , , , , , , and . IPAW, volume 5272 of Lecture Notes in Computer Science, page 144-151. Springer, (2008)Performance analysis and autotuning setup of the cuFFT library., and . ANDARE@PACT, page 1:1-1:6. ACM, (2018)A Benchmark Set of Highly-efficient CUDA and OpenCL Kernels and its Dynamic Autotuning with Kernel Tuning Toolkit., , , , , , and . CoRR, (2019)pyCaverDock: Python implementation of the popular tool for analysis of ligand transport with advanced caching and batch calculation support., , , , , , , and . Bioinform., (August 2023)Searching CUDA code autotuning spaces with hardware performance counters: data from benchmarks running on various GPU architectures., , , , and . CoRR, (2021)Towards a Benchmarking Suite for Kernel Tuners., , , , , and . IPDPS Workshops, page 724-733. IEEE, (2023)Optimizing CUDA code by kernel fusion: application on BLAS., , , and . J. Supercomput., 71 (10): 3934-3957 (2015)Automatic fusions of CUDA-GPU kernels for parallel map., , and . SIGARCH Comput. Archit. News, 39 (4): 98-99 (2011)