Exploiting historical data: Pruning autotuning spaces and estimating the number of tuning steps. - Jaroslav Olha; Jana Hozzová; Jan Fousek; Jiri Filipovic (Concurr. Comput. Pract. Exp., 2020)

A benchmark set of highly-efficient CUDA and OpenCL kernels and its dynamic autotuning with Kernel Tuning Toolkit. - Filip Petrovic; David Strelák; Jana Hozzová; Jaroslav Olha; Richard Trembecký; Siegfried Benkner; Jiri Filipovic (Future Gener. Comput. Syst., 2020)

Searching CUDA code autotuning spaces with hardware performance counters: data from benchmarks running on various GPU architectures. - Jiri Filipovic; Jana Hozzová; Amin Nezarat; Jaroslav Olha; Filip Petrovic (CoRR, 2021)

Improving ligand transport trajectory within flexible receptor in CaverDock. - Petra Nemcová; Jana Hozzová; Jiri Filipovic (SAC, 2022)

Property Map Collective Variable as a Useful Tool for a Force Field Correction. - Dalibor Trapl; Martin Krupicka; Vladimír Visnovský; Jana Hozzová; Jaroslav Olha; Ales Krenek; Vojtech Spiwok (J. Chem. Inf. Model., 2022)

Using hardware performance counters to speed up autotuning convergence on GPUs. - Jiri Filipovic; Jana Hozzová; Amin Nezarat; Jaroslav Olha; Filip Petrovic (J. Parallel Distributed Comput., 2022)

FAIR Sharing of Data in Autotuning Research (Vision Paper). - Jana Hozzová; Jacob O. Tørring; Ben van Werkhoven; David Strelák; Richard W. Vuduc (ICPE (Companion), 2024)

Estimating resource budgets to ensure autotuning efficiency. - Jaroslav Olha; Jana Hozzová; Matej Antol; Jiri Filipovic (Parallel Comput., 2025)