--- _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' ...