---
_id: '10673'
author:
- first_name: Nam
full_name: Ho, Nam
last_name: Ho
- first_name: Abdullah Fathi
full_name: Ahmed, Abdullah Fathi
last_name: Ahmed
- first_name: Paul
full_name: Kaufmann, Paul
last_name: Kaufmann
- first_name: Marco
full_name: Platzner, Marco
id: '398'
last_name: Platzner
citation:
ama: 'Ho N, Ahmed AF, Kaufmann P, Platzner M. Microarchitectural optimization by
means of reconfigurable and evolvable cache mappings. In: Proc. NASA/ESA Conf.
Adaptive Hardware and Systems (AHS). ; 2015:1-7. doi:10.1109/AHS.2015.7231178'
apa: Ho, N., Ahmed, A. F., Kaufmann, P., & Platzner, M. (2015). Microarchitectural
optimization by means of reconfigurable and evolvable cache mappings. In Proc.
NASA/ESA Conf. Adaptive Hardware and Systems (AHS) (pp. 1–7). https://doi.org/10.1109/AHS.2015.7231178
bibtex: '@inproceedings{Ho_Ahmed_Kaufmann_Platzner_2015, title={Microarchitectural
optimization by means of reconfigurable and evolvable cache mappings}, DOI={10.1109/AHS.2015.7231178},
booktitle={Proc. NASA/ESA Conf. Adaptive Hardware and Systems (AHS)}, author={Ho,
Nam and Ahmed, Abdullah Fathi and Kaufmann, Paul and Platzner, Marco}, year={2015},
pages={1–7} }'
chicago: Ho, Nam, Abdullah Fathi Ahmed, Paul Kaufmann, and Marco Platzner. “Microarchitectural
Optimization by Means of Reconfigurable and Evolvable Cache Mappings.” In Proc.
NASA/ESA Conf. Adaptive Hardware and Systems (AHS), 1–7, 2015. https://doi.org/10.1109/AHS.2015.7231178.
ieee: N. Ho, A. F. Ahmed, P. Kaufmann, and M. Platzner, “Microarchitectural optimization
by means of reconfigurable and evolvable cache mappings,” in Proc. NASA/ESA
Conf. Adaptive Hardware and Systems (AHS), 2015, pp. 1–7.
mla: Ho, Nam, et al. “Microarchitectural Optimization by Means of Reconfigurable
and Evolvable Cache Mappings.” Proc. NASA/ESA Conf. Adaptive Hardware and Systems
(AHS), 2015, pp. 1–7, doi:10.1109/AHS.2015.7231178.
short: 'N. Ho, A.F. Ahmed, P. Kaufmann, M. Platzner, in: Proc. NASA/ESA Conf. Adaptive
Hardware and Systems (AHS), 2015, pp. 1–7.'
date_created: 2019-07-10T11:18:00Z
date_updated: 2022-01-06T06:50:49Z
department:
- _id: '78'
doi: 10.1109/AHS.2015.7231178
keyword:
- cache storage
- field programmable gate arrays
- multiprocessing systems
- parallel architectures
- reconfigurable architectures
- FPGA
- dynamic reconfiguration
- evolvable cache mapping
- many-core architecture
- memory-to-cache address mapping function
- microarchitectural optimization
- multicore architecture
- nature-inspired optimization
- parallelization degrees
- processor
- reconfigurable cache mapping
- reconfigurable computing
- Field programmable gate arrays
- Software
- Tuning
language:
- iso: eng
page: 1-7
project:
- _id: '31'
grant_number: '257906'
name: Engineering Proprioception in Computing Systems
publication: Proc. NASA/ESA Conf. Adaptive Hardware and Systems (AHS)
status: public
title: Microarchitectural optimization by means of reconfigurable and evolvable cache
mappings
type: conference
user_id: '3118'
year: '2015'
...
---
_id: '48838'
abstract:
- lang: eng
text: 'The majority of algorithms can be controlled or adjusted by parameters. Their
values can substantially affect the algorithms’ performance. Since the manual
exploration of the parameter space is tedious – even for few parameters – several
automatic procedures for parameter tuning have been proposed. Recent approaches
also take into account some characteristic properties of the problem instances,
frequently termed instance features. Our contribution is the proposal of a novel
concept for feature-based algorithm parameter tuning, which applies an approximating
surrogate model for learning the continuous feature-parameter mapping. To accomplish
this, we learn a joint model of the algorithm performance based on both the algorithm
parameters and the instance features. The required data is gathered using a recently
proposed acquisition function for model refinement in surrogate-based optimization:
the profile expected improvement. This function provides an avenue for maximizing
the information required for the feature-parameter mapping, i.e., the mapping
from instance features to the corresponding optimal algorithm parameters. The
approach is validated by applying the tuner to exemplary evolutionary algorithms
and problems, for which theoretically grounded or heuristically determined feature-parameter
mappings are available.'
author:
- first_name: Jakob
full_name: Bossek, Jakob
id: '102979'
last_name: Bossek
orcid: 0000-0002-4121-4668
- first_name: Bernd
full_name: Bischl, Bernd
last_name: Bischl
- first_name: Tobias
full_name: Wagner, Tobias
last_name: Wagner
- first_name: Günter
full_name: Rudolph, Günter
last_name: Rudolph
citation:
ama: 'Bossek J, Bischl B, Wagner T, Rudolph G. Learning Feature-Parameter Mappings
for Parameter Tuning via the Profile Expected Improvement. In: Proceedings
of the Genetic and Evolutionary Computation Conference. GECCO ’15. Association
for Computing Machinery; 2015:1319–1326. doi:10.1145/2739480.2754673'
apa: Bossek, J., Bischl, B., Wagner, T., & Rudolph, G. (2015). Learning Feature-Parameter
Mappings for Parameter Tuning via the Profile Expected Improvement. Proceedings
of the Genetic and Evolutionary Computation Conference, 1319–1326. https://doi.org/10.1145/2739480.2754673
bibtex: '@inproceedings{Bossek_Bischl_Wagner_Rudolph_2015, place={New York, NY,
USA}, series={GECCO ’15}, title={Learning Feature-Parameter Mappings for Parameter
Tuning via the Profile Expected Improvement}, DOI={10.1145/2739480.2754673},
booktitle={Proceedings of the Genetic and Evolutionary Computation Conference},
publisher={Association for Computing Machinery}, author={Bossek, Jakob and Bischl,
Bernd and Wagner, Tobias and Rudolph, Günter}, year={2015}, pages={1319–1326},
collection={GECCO ’15} }'
chicago: 'Bossek, Jakob, Bernd Bischl, Tobias Wagner, and Günter Rudolph. “Learning
Feature-Parameter Mappings for Parameter Tuning via the Profile Expected Improvement.”
In Proceedings of the Genetic and Evolutionary Computation Conference,
1319–1326. GECCO ’15. New York, NY, USA: Association for Computing Machinery,
2015. https://doi.org/10.1145/2739480.2754673.'
ieee: 'J. Bossek, B. Bischl, T. Wagner, and G. Rudolph, “Learning Feature-Parameter
Mappings for Parameter Tuning via the Profile Expected Improvement,” in Proceedings
of the Genetic and Evolutionary Computation Conference, 2015, pp. 1319–1326,
doi: 10.1145/2739480.2754673.'
mla: Bossek, Jakob, et al. “Learning Feature-Parameter Mappings for Parameter Tuning
via the Profile Expected Improvement.” Proceedings of the Genetic and Evolutionary
Computation Conference, Association for Computing Machinery, 2015, pp. 1319–1326,
doi:10.1145/2739480.2754673.
short: 'J. Bossek, B. Bischl, T. Wagner, G. Rudolph, in: Proceedings of the Genetic
and Evolutionary Computation Conference, Association for Computing Machinery,
New York, NY, USA, 2015, pp. 1319–1326.'
date_created: 2023-11-14T15:58:51Z
date_updated: 2023-12-13T10:40:30Z
department:
- _id: '819'
doi: 10.1145/2739480.2754673
extern: '1'
keyword:
- evolutionary algorithms
- model-based optimization
- parameter tuning
language:
- iso: eng
page: 1319–1326
place: New York, NY, USA
publication: Proceedings of the Genetic and Evolutionary Computation Conference
publication_identifier:
isbn:
- 978-1-4503-3472-3
publication_status: published
publisher: Association for Computing Machinery
series_title: GECCO ’15
status: public
title: Learning Feature-Parameter Mappings for Parameter Tuning via the Profile Expected
Improvement
type: conference
user_id: '102979'
year: '2015'
...