E-LIB Suche - Ergebnisse für: Hozzová, Jana

1

FAIR Sharing of Data in Autotuning Research (Vision Paper):

, In: Companion of the 15th ACM/SPEC International Conference on Performance Engineering,

Hozzová, Jana ; Tørring, Jacob O. ; van Werkhoven, Ben.. - p. 21-27 , 2024

2

Property Map Collective Variable as a Useful Tool for a For..:

Trapl, Dalibor ; Krupička, Martin ; Višňovský, Vladimír...
Journal of Chemical Information and Modeling. 62 (2022) 3 - p. 567-576 , 2022

Link: https://doi.org/10.1021/..

3

Using hardware performance counters to speed up autotuning ..:

Filipovič, Jiří ; Hozzová, Jana ; Nezarat, Amin..
Journal of Parallel and Distributed Computing. 160 (2022) - p. 16-35 , 2022

Link: https://doi.org/10.1016/..

4

Improving ligand transport trajectory within flexible recep..:

, In: Proceedings of the 37th ACM/SIGAPP Symposium on Applied Computing,

Němcová, Petra ; Hozzová, Jana ; Filipovič, Jiří - p. 619-626 , 2022

Link: https://dl.acm.org/doi/1..

5

Searching CUDA code autotuning spaces with hardware perform..:

Hozzová, Jana ; Filipovič, Jiří ; Nezarat, Amin..
Data in Brief. 39 (2021) - p. 107631 , 2021

Link: https://doi.org/10.1016/..

6

Exploiting Historical Data: Pruning Autotuning Spaces and E..:

, In: Euro-Par 2019: Parallel Processing Workshops; Lecture Notes in Computer Science,

Oľha, Jaroslav ; Hozzová, Jana ; Fousek, Jan. - p. 295-307 , 2020

Link: https://doi.org/10.1007/..

7

A benchmark set of highly-efficient CUDA and OpenCL kernels..:

Petrovič, Filip ; Střelák, David ; Hozzová, Jana...
Future Generation Computer Systems. 108 (2020) - p. 161-177 , 2020

Link: https://doi.org/10.1016/..

8

Exploiting historical data: Pruning autotuning spaces and e..:

Oľha, Jaroslav ; Hozzová, Jana ; Fousek, Jan.
Concurrency and Computation: Practice and Experience. 32 (2020) 21 - p. , 2020

Link: https://doi.org/10.1002/..

9

Searching CUDA code autotuning spaces with hardware perform..:

Hozzová, Jana ; Filipovič, Jiří ; Nezarat, Amin..
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8633859/. , 2021

Link: http://www.ncbi.nlm.nih...

10

Property map collective variable as a usefultool for force ..:

Trapl, Dalibor ; Krupicka, Martin ; Visnovsky, Vladimir...
doi:10.5281/zenodo.4906267. , 2021

Link: https://zenodo.org/recor..

11

Searching CUDA code autotuning spaces with hardware perform..:

Filipovič, Jiří ; Hozzová, Jana ; Nezarat, Amin..
http://arxiv.org/abs/2102.05299. , 2021

Link: http://arxiv.org/abs/210..

12

Using hardware performance counters to speed up autotuning ..:

Filipovič, Jiří ; Hozzová, Jana ; Nezarat, Amin..
http://arxiv.org/abs/2102.05297. , 2021

Link: http://arxiv.org/abs/210..

13

Exploiting historical data: pruning autotuning spaces and e..:

Oľha, Jaroslav ; Hozzová, Jana ; Fousek, Jan.
info:eu-repo/semantics/altIdentifier/doi/10.1002/cpe.5962. , 2020

Link: https://hal.science/hal-..

14

Exploiting historical data: pruning autotuning spaces and e..:

Oľha, Jaroslav ; Hozzová, Jana ; Fousek, Jan.
info:eu-repo/semantics/altIdentifier/doi/10.1002/cpe.5962. , 2020

Link: https://hal.archives-ouv..

15

A benchmark set of highly-efficient CUDA and OpenCL kernels..:

Petrovič, Filip ; Střelák, David ; Hozzová, Jana...
#PLACEHOLDER_PARENT_METADATA_VALUE#. , 2020

Link: http://hdl.handle.net/10..