| OĽHA, Jaroslav, Jana HOZZOVÁ, Jan FOUSEK and Jiří FILIPOVIČ. Exploiting historical data: pruning autotuning spaces and estimating the number of tuning steps. In Ulrich Schwardmann, Christian Boehme, Dora B. Heras, Valeria Cardellini, Emmanuel Jeannot, Antonio Salis, Claudio Schifanella, Ravi Reddy Manumachu, Dieter Schwamborn, Laura Ricci, Oh Sangyoon, Thomas Gruber, Laura Antonelli, Stephen L. Scott. Lecture Notes in Computer Science. Cham, Switzerland: Springer, Cham, 2019, p. 295-307. ISBN 978-3-030-48339-5. Available from: https://dx.doi.org/10.1007/978-3-030-48340-1_23. |
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@inproceedings{1551680,
author = {Oľha, Jaroslav and Hozzová, Jana and Fousek, Jan and Filipovič, Jiří},
address = {Cham, Switzerland},
booktitle = {Lecture Notes in Computer Science},
doi = {http://dx.doi.org/10.1007/978-3-030-48340-1_23},
editor = {Ulrich Schwardmann, Christian Boehme, Dora B. Heras, Valeria Cardellini, Emmanuel Jeannot, Antonio Salis, Claudio Schifanella, Ravi Reddy Manumachu, Dieter Schwamborn, Laura Ricci, Oh Sangyoon, Thomas Gruber, Laura Antonelli, Stephen L. Scott},
keywords = {Autotuning; prediction of tuning cost; tuning space pruning; sensitivity analysis},
howpublished = {tištěná verze "print"},
language = {eng},
location = {Cham, Switzerland},
isbn = {978-3-030-48339-5},
pages = {295-307},
publisher = {Springer, Cham},
title = {Exploiting historical data: pruning autotuning spaces and estimating the number of tuning steps},
year = {2019}
}
TY - JOUR
ID - 1551680
AU - Oľha, Jaroslav - Hozzová, Jana - Fousek, Jan - Filipovič, Jiří
PY - 2019
TI - Exploiting historical data: pruning autotuning spaces and estimating the number of tuning steps
PB - Springer, Cham
CY - Cham, Switzerland
SN - 9783030483395
KW - Autotuning
KW - prediction of tuning cost
KW - tuning space pruning
KW - sensitivity analysis
N2 - Autotuning, the practice of automatic tuning of code to provide performance portability, has received increased attention in the research community, especially in high performance computing. Ensuring high performance on a variety of hardware usually means modifications to the code, often via different values of a selected set of parameters, such as tiling size, loop unrolling factor or data layout. However, the search space of all possible combinations of these parameters can be enormous. Traditional search methods often fail to find a well-performing set of parameter values quickly. We have found that certain properties of tuning spaces do not vary much when hardware is changed. In this paper, we demonstrate that it is possible to use historical data to reliably predict the number of tuning steps necessary to find a well-performing configuration, and to reduce the size of the tuning space. We evaluate our hypotheses on a number of GPU-accelerated benchmarks written in CUDA and OpenCL.
ER -
OĽHA, Jaroslav, Jana HOZZOVÁ, Jan FOUSEK and Jiří FILIPOVIČ. Exploiting historical data: pruning autotuning spaces and estimating the number of tuning steps. In Ulrich Schwardmann, Christian Boehme, Dora B. Heras, Valeria Cardellini, Emmanuel Jeannot, Antonio Salis, Claudio Schifanella, Ravi Reddy Manumachu, Dieter Schwamborn, Laura Ricci, Oh Sangyoon, Thomas Gruber, Laura Antonelli, Stephen L. Scott. \textit{Lecture Notes in Computer Science}. Cham, Switzerland: Springer, Cham, 2019, p.~295-307. ISBN~978-3-030-48339-5. Available from: https://dx.doi.org/10.1007/978-3-030-48340-1\_{}23.
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