A benchmark set of highly-efficient CUDA and OpenCL kernels and its dynamic autotuning with Kernel Tuning Toolkit F Petrovič, D Střelák, J Hozzová, J Ol’ha, R Trembecký, S Benkner, ... Future Generation Computer Systems 108, 161-177, 2020 | 40 | 2020 |
Learned metric index—proposition of learned indexing for unstructured data M Antol, J Ol’ha, T Slanináková, V Dohnal Information Systems 100, 101774, 2021 | 24 | 2021 |
Data-driven learned metric index: an unsupervised approach T Slanináková, M Antol, J OǏha, V Kaňa, V Dohnal Similarity Search and Applications: 14th International Conference, SISAP …, 2021 | 14 | 2021 |
Using hardware performance counters to speed up autotuning convergence on GPUs J Filipovič, J Hozzová, A Nezarat, J Ol'ha, F Petrovič Journal of Parallel and Distributed Computing 160, 16-35, 2022 | 13 | 2022 |
Exploiting historical data: Pruning autotuning spaces and estimating the number of tuning steps J Oľha, J Hozzová, J Fousek, J Filipovič Concurrency and Computation: Practice and Experience 32 (21), e5962, 2020 | 9 | 2020 |
Learned indexing in proteins: substituting complex distance calculations with embedding and clustering techniques J Olha, T Slanináková, M Gendiar, M Antol, V Dohnal International Conference on Similarity Search and Applications, 274-282, 2022 | 5 | 2022 |
Umpalumpa: a framework for efficient execution of complex image processing workloads on heterogeneous nodes D Střelák, D Myška, F Petrovič, J Polák, J Ol’ha, J Filipovič Computing 105 (11), 2389-2417, 2023 | 3 | 2023 |
Reproducible experiments with learned metric index framework T Slanináková, M Antol, J Ol’ha, V Dohnal, S Ladra, MA Martínez-Prieto Information Systems 118, 102255, 2023 | 2 | 2023 |
Property Map Collective Variable as a Useful Tool for a Force Field Correction D Trapl, M Krupicka, V Visnovsky, J Hozzová, J Ol’ha, A Krenek, V Spiwok Journal of Chemical Information and Modeling 62 (3), 567-576, 2022 | 2 | 2022 |
Searching CUDA code autotuning spaces with hardware performance counters: data from benchmarks running on various GPU architectures J Hozzová, J Filipovič, A Nezarat, J Ol’ha, F Petrovič Data in Brief 39, 107631, 2021 | 1 | 2021 |
Searching CUDA code autotuning spaces with hardware performance counters: data from benchmarks running on various GPU architectures J Filipovič, J Hozzová, A Nezarat, J Oľha, F Petrovič arXiv preprint arXiv:2102.05299, 2021 | 1 | 2021 |
AlphaFind: Discover structure similarity across the entire known proteome D Prochazka, T Slaninakova, J Olha, A Rosinec, K Gresova, M Janosova, ... bioRxiv, 2024.02. 15.580465, 2024 | | 2024 |
Check for updates SISAP 2023 Indexing Challenge-Learned Metric Index T Slanináková, D Procházka, M Antol, J Olha, V DohnaliD Similarity Search and Applications: 16th International Conference, SISAP …, 2023 | | 2023 |
SISAP 2023 Indexing Challenge–Learned Metric Index T Slanináková, D Procházka, M Antol, J Olha, V Dohnal International Conference on Similarity Search and Applications, 282-290, 2023 | | 2023 |
Learned Indexing in Proteins: Extended Work on Substituting Complex Distance Calculations with Embedding and Clustering Techniques J Oľha, T Slanináková, M Gendiar, M Antol, V Dohnal arXiv preprint arXiv:2208.08910, 2022 | | 2022 |
Complex simulation workflows in containerized high-performance environment V Višňovský, V Spišaková, J Hozzová, J Oľha, D Trapl, V Spiwok, ... EUROSIS, 2021 | | 2021 |
Exploring Protein Folding Space with Neural Network Guided Simulations A Křenek, J Hozzová, J Oľha, D Trapl, V Spiwok EUROSIS-ETI, 2020 | | 2020 |
Acceleration of Mean Square Distance Calculations with Floating Close Structure in Metadynamics Simulations J Pazúriková, J Oľha, A Křenek, V Spiwok arXiv preprint arXiv:1801.02362, 2018 | | 2018 |
Acceleration of Mean Square Distance Calculations with Floating Close Structure in Metadynamics Simulations J Hozzová, J Oľha, A Křenek, V Spiwok | | 2018 |
Calculation of dissociation constants using triangulation methods J Oľha Masaryk University, Faculty of Informatics, 2012 | | 2012 |