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A Benchmark Set of Highly-efficient CUDA and OpenCL Kernels and its Dynamic Autotuning with Kernel Tuning Toolkit., , , , , , and . CoRR, (2019)Acceleration of Mean Square Distance Calculations with Floating Close Structure in Metadynamics Simulations., , , and . CoRR, (2018)Reproducible experiments with Learned Metric Index Framework., , , , , and . Inf. Syst., (September 2023)Searching CUDA code autotuning spaces with hardware performance counters: data from benchmarks running on various GPU architectures., , , , and . CoRR, (2021)Property Map Collective Variable as a Useful Tool for a Force Field Correction., , , , , , and . J. Chem. Inf. Model., 62 (3): 567-576 (2022)A benchmark set of highly-efficient CUDA and OpenCL kernels and its dynamic autotuning with Kernel Tuning Toolkit., , , , , , and . Future Gener. Comput. Syst., (2020)Umpalumpa: a framework for efficient execution of complex image processing workloads on heterogeneous nodes., , , , , and . Computing, 105 (11): 2389-2417 (November 2023)Exploiting Historical Data: Pruning Autotuning Spaces and Estimating the Number of Tuning Steps., , , and . Euro-Par Workshops, volume 11997 of Lecture Notes in Computer Science, page 295-307. Springer, (2019)SISAP 2023 Indexing Challenge - Learned Metric Index., , , , and . SISAP, volume 14289 of Lecture Notes in Computer Science, page 282-290. Springer, (2023)Learned Indexing in Proteins: Substituting Complex Distance Calculations with Embedding and Clustering Techniques., , , , and . SISAP, volume 13590 of Lecture Notes in Computer Science, page 274-282. Springer, (2022)