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Publications

[1] P. Balaprakash, V. Morozov, S. M. Wild, H. Finkel, V. Vishwanath, and K. Kumaran. End-to-end programming platform for neuromorphic computing systems using leadership-class machines. White Paper, 2016. [ bib ]
[2] A. Roy, P. Balaprakash, P. D. Hovland, and S. M. Wild. Exploiting performance portability in search algorithms for autotuning. In 2016 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW), pages 1535–1544, 2016. [ bib | DOI ]
[3] C. Symons, R. Sukumar, W. Joubert, R. Kannan, C. Steed, A. Ramanathan, P. Balaprakash, V. Vishwanath, S. Wild, F. Xia, J. Brase, B. Chen, and W. Davis. Co-design center supporting scientific discovery through structure-based machine learning. Exascale Computing Project White Paper, 2016. [ bib ]
[4] A. Moawad, P. Balaprakash, A. Rousseau, and S. M. Wild. Novel large scale simulation process to support DOT's CAFE modeling system. International Journal of Automotive Technology, 17(6):1067–1077, 2016. [ bib | DOI ]
[5] P. Balaprakash, A. Tiwari, S. M. Wild, and P. D. Hovland. AutoMOMML: Automatic Multi-objective Modeling with Machine Learning. In M. J. Kunkel, P. Balaji, and J. Dongarra, editors, High Performance Computing: 31st International Conference, ISC High Performance 2016, Frankfurt, Germany, June 19-23, 2016, Proceedings, pages 219–239. Springer International Publishing, 2016. [ bib | DOI ]
[6] P. Balaprakash, V. Morozov, R. Kettimuthu, K. Kumaran, and I. Foster. Improving data transfer throughput with direct search optimization. In 2016 45th International Conference on Parallel Processing (ICPP), pages 248–257, 2016. Acceptance rate 21.10%. [ bib | DOI ]
[7] A. Choudhary, A. Agrawal, W. Liao, P. Balaprakash, R. Ross, and S. Wild. High performance deep machine learning software for exascale applications. Exascale Computing Project White Paper, 2016. [ bib ]
[8] M. Hall, S. Williams, B. van Straalen, P. Balaprakash, and D. Quinlan. Autotuning compiler technology for cross-architecture transformation and code generation. Exascale Computing Project White Paper, 2016. [ bib ]
[9] O. Subasi, S. Di, L. Bautista-Gomez, P. Balaprakash, O. Unsal, J. Labarta, A. Cristal, and F. Cappello. Spatial support vector regression to detect silent errors in the exascale era. In 2016 16th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid), pages 413–424, 2016. Acceptance rate 20.0%. [ bib | DOI ]
[10] M. Berry, T. E. Potok, P. Balaprakash, H. Hoffmann, R. Vatsavai, and Prabhat. Machine learning and understanding for intelligent extreme scale scientific computing and discovery. Technical report, DOE ASCR Workshop Report, 2015. [ bib | .pdf ]
[11] P. Balaprakash, M. Birattari, T. Stützle, and M. Dorigo. Estimation-based metaheuristics for the single vehicle routing problem with stochastic demands and customers. Computational Optimization and Applications, 61(2):463–487, 2015. [ bib | DOI ]
[12] A. Mametjanov, P. Balaprakash, C. Choudary, P. D. Hovland, S. M. Wild, and G. Sabin. Autotuning FPGA design parameters for performance and power. In 2015 IEEE 23rd Annual International Symposium on Field-Programmable Custom Computing Machines (FCCM), pages 84–91, 2015. Acceptance rate 22.10%. [ bib | DOI ]
[13] P. Balaprakash, L. A. B. Gomez, M. S. Bouguerra, S. M. Wild, F. Cappello, and P. D. Hovland. Analysis of the tradeoffs between energy and run time for multilevel checkpointing. In S. A. Jarvis, S. A. Wright, and S. D. Hammond, editors, High Performance Computing Systems. Performance Modeling, Benchmarking, and Simulation – PMBS 2014, volume 8966 of Lecture Notes in Computer Science, pages 249–263. Springer International Publishing, 2015. Acceptance rate 26%. [ bib | DOI ]
[14] T. Nelson, A. Rivera, P. Balaprakash, M. Hall, P. D. Hovland, E. Jessup, and B. Norris. Generating efficient tensor contractions for GPUs. In 2015 44th International Conference on Parallel Processing (ICPP), pages 969–978, 2015. Acceptance rate 32.5%. [ bib | DOI ]
[15] P. Balaprakash, Y. Alexeev, S. A. Mickelson, S. Leyffer, R. Jacob, and A. Craig. Machine-learning-based load balancing for community ice code component in CESM. In M. Daydé, O. Marques, and K. Nakajima, editors, High Performance Computing for Computational Science – VECPAR 2014, Revised Selected Papers, volume 8969 of Lecture Notes in Computer Science, pages 79–91. Springer International Publishing, 2015. [ bib | DOI ]
[16] F. Isaila, P. Balaprakash, S. M. Wild, D. Kimpe, R. Latham, R. Ross, and P. D. Hovland. Collective I/O tuning using analytical and machine learning models. In 2015 IEEE International Conference on Cluster Computing (CLUSTER), pages 128–137. IEEE, 2015. Acceptance rate 24%. [ bib | DOI ]
[17] A. Moawad, P. Balaprakash, A. Rousseau, and S. M. Wild. Novel large scale simulation process to support DOT's CAFE modeling system. In International Electric Vehicle Symposium and Exhibition (EVS28), 2015. [ bib | .pdf ]
[18] P. Balaprakash, A. Tiwari, and S. M. Wild. Framework for optimizing power, energy, and performance. In The SUPER Project Newsletter, 2014. [ bib | .pdf ]
[19] P. Balaprakash, V. Morozov, S. M. Wild, V. Vishwanath, P. D. Hovland, K. Kumaran, and B. Allcock. Machine learning for self-adaptive leadership-class machines. White Paper, 2014. [ bib ]
[20] A. Moawad, S. Halbach, S. Pagerit, A. Rousseau, P. Balaprakash, and S. Wild. Novel process to use vehicle simulations directly as inputs to DOTs CAFE modeling system. Technical Report ANL/ESD-13/13, Report to Department of Transportation, 2014. [ bib ]
[21] P. Balaprakash, A. Tiwari, and S. M. Wild. Multi objective optimization of HPC kernels for performance, power, and energy. In S. A. Jarvis, S. A. Wright, and S. D. Hammond, editors, High Performance Computing Systems. Performance Modeling, Benchmarking and Simulation – PMBS 2013, Lecture Notes in Computer Science, pages 239–260. Springer International Publishing, 2014. Acceptance rate 30%. [ bib | DOI ]
[22] P. Balaprakash, Y. Alexeev, S. Mickelson, S. Leyffer, R. Jacob, and A. Craig. Machine-learning-based load balancing for community ice code component in CESM. In 11th International Meeting on High-Performance Computing for Computational Science (VECPAR 2014), 2014. [ bib ]
[23] Y. Zhang, P. Balaprakash, J. Meng, V. Morozov, S. Parker, and K. Kumaran. Raexplore: Enabling rapid, automated architecture exploration for full applications. Technical Report ANL/ALCF/TM-14/2, Argonne National Laboratory, 2014. [ bib | .pdf ]
[24] P. Balaprakash, D. Buntinas, A. Chan, A. Guha, R. Gupta, S. H. K. Narayanan, A. A. Chien, P. Hovland, and B. Norris. Exascale workload characterization and architecture implications. In Proceedings of the High Performance Computing Symposium, HPC '13, pages 5:1–5:8, San Diego, CA, USA, 2013. Society for Computer Simulation International. [ bib | http ]
[25] P. Balaprakash, S. M. Wild, and P. D. Hovland. Performance modeling for exascale autotuning: An integrated approach. White Paper, 2013. [ bib ]
[26] P. Balaprakash, S. M. Wild, and P. D. Hovland. An experimental study of global and local search algorithms in empirical performance tuning. In High Performance Computing for Computational Science - VECPAR 2012, 10th International Conference, Revised Selected Papers, Lecture Notes in Computer Science, pages 261–269. Springer, 2013. [ bib | DOI ]
[27] P. Balaprakash, D. Buntinas, A. Chan, A. Guha, R. Gupta, S. Narayanan, A. Chien, P. Hovland, and B. Norris. Exascale workload characterization and architecture implications. In Proceedings of the High Performance Computing Symposium, HPC '13, pages 5:1–5:8, San Diego, CA, USA, 2013. Society for Computer Simulation International. Best Paper Award. [ bib | http ]
[28] P. Balaprakash, R. B. Gramacy, and S. M. Wild. Active-learning-based surrogate models for empirical performance tuning. In 2013 IEEE International Conference on Cluster Computing (CLUSTER), pages 1–8, 2013. Acceptance rate 31%. [ bib | DOI ]
[29] P. Balaprakash, K. Rupp, A. Mametjanov, R. B. Gramacy, P. D. Hovland, and S. M. Wild. Empirical performance modeling of GPU kernels using active learning. In Parallel Computing: Accelerating Computational Science and Engineering (ParCo2013), Advances in Parallel Computing, pages 646–655, 2013. [ bib | DOI ]
[30] P. Balaprakash, S. M. Wild, and P. D. Hovland. Efficient optimization algorithms for empirical performance tuning. SIAM Conference on Parallel Processing (SIAM PP 2012), 2012. Abstract. [ bib ]
[31] P. Balaprakash and O. A. Lilienfeld. A sequential learning approach for quantum chemistry simulations. In IPAM Chemical Compound Space Reunion, 2012. Invited Abstract. [ bib ]
[32] P. Balaprakash, S. M. Wild, and B. Norris. SPAPT: Search Problems in Automatic Performance Tuning. In Proceedings of the International Conference on Computational Science, ICCS 2012, volume 9, pages 1959–1968, 2012. [ bib | DOI ]
[33] P. Balaprakash, S. M. Wild, and P. D. Hovland. Model-based optimization algorithms for empirical performance tuning. 2011 DOE Applied Mathematics Program Meeting, Washington, DC, 2011. Abstract. [ bib ]
[34] P. Balaprakash, S. M. Wild, and P. D. Hovland. Global and local search algorithms in empirical performance tuning. DOE CScADS Workshop on Libraries and Autotuning for Extreme-Scale Systems, 2011. Abstract. [ bib ]
[35] B. Norris, Q. Zhu, T. Nelson, P. Balaprakash, and S. M. Wild. Comparison of search strategies in empirical performance tuning of linear algebra kernels. 2011 SIAM Conference on Computational Science and Engineering, 2011. Abstract. [ bib ]
[36] P. Balaprakash, S. M. Wild, and P. D. Hovland. Can search algorithms save large-scale automatic performance tuning? In Proceedings of the International Conference on Computational Science, ICCS 2011, volume 4, pages 2136–2145, 2011. [ bib | DOI ]
[37] P. Balaprakash. Estimation-based metaheuristics for stochastic combinatorial optimization: Case studies in stochastic routing problems. PhD thesis, Université Libre de Bruxelles, 2010. [ bib | .pdf ]
[38] M. Birattari, Z. Yuan, P. Balaprakash, and T. Stützle. F-Race and Iterated F-Race: An Overview. In T. Bartz-Beielstein, M. Chiarandini, L. Paquete, and M. Preuss, editors, Experimental Methods for the Analysis of Optimization Algorithms, pages 311–336. Springer Berlin Heidelberg, 2010. [ bib | DOI ]
[39] P. Balaprakash, M. Birattari, T. Stützle, and M. Dorigo. Estimation-based metaheuristics for the probabilistic traveling salesman problem. Computers & Operations Research, 37(11):1939–1951, 2010. [ bib | DOI ]
[40] P. Balaprakash. Estimation-based metaheuristics for stochastic combinatorial optimization: Case studies in stochastic routing problems. In SIGEVOlution Newsletter, volume 5, pages 18–19, New York, NY, USA, 2010. ACM. [ bib | DOI ]
[41] P. Balaprakash, M. Birattari, T. Stutzle, and M. Dorigo. Effective estimation-based stochastic local search algorithms for stochastic routing problems. In Proceedings of ORBEL 24, 24th Annual Conference of the Belgian Operations Research Society, pages 136–137, 2010. Extended Abstract. [ bib ]
[42] P. Balaprakash, M. Birattari, T. Stützle, and M. Dorigo. Adaptive sample size and importance sampling in estimation-based local search for the probabilistic traveling salesman problem. European Journal of Operational Research, 199(1):98 – 110, 2009. [ bib | DOI ]
[43] P. Balaprakash, M. Birattari, T. Stützle, Z. Yuan, and M. Dorigo. Estimation-based ant colony optimization and local search for the probabilistic traveling salesman problem. Swarm Intelligence, 3(3):223–242, 2009. [ bib | DOI ]
[44] M. Birattari, Z. Yuan, P. Balaprakash, and T. Stützle. Automated algorithm tuning using F-Races: Recent developments. In S. Voss and M. Caserta, editors, MIC 2009: The 8th Metaheuristics International Conference, volume proceedings on CD-ROM, page 10 pages, 2009. [ bib ]
[45] M. Birattari, P. Balaprakash, T. Stützle, and M. Dorigo. Estimation-based local search for stochastic combinatorial optimization using delta evaluations: A case study on the probabilistic traveling salesman problem. INFORMS Journal on Computing, 20(4):644–658, 2008. [ bib | DOI ]
[46] P. Balaprakash, M. Birattari, and T. Stützle. Engineering stochastic local search algorithms: A case study in estimation-based local search for the probabilistic travelling salesman problem. In C. Cotta and J. van Hemert, editors, Recent Advances in Evolutionary Computation for Combinatorial Optimization, volume 153 of Studies in Computational Intelligence, pages 53–66. Springer Berlin Heidelberg, 2008. [ bib | DOI ]
[47] Z. Yuan, A. Fügenschuh, H. Homfeld, P. Balaprakash, T. Stützle, and M. Schoch. Iterated greedy algorithms for a real-world cyclic train scheduling problem. In M. J. Blesa, C. Blum, C. Cotta, A. Fernández, J. Gallardo, A. Roli, and M. Sampels, editors, Hybrid Metaheuristics, volume 5296 of Lecture Notes in Computer Science, pages 102–116. Springer Berlin Heidelberg, 2008. [ bib | DOI ]
[48] P. Balaprakash, M. Birattari, T. Stützle, and M. Dorigo. Applications of estimation-based SLS algorithms to the stochastic routing problems. In P. Hansen and S. Voss, editors, Metaheuristics 2008, Second International Workshop on Model Based Metaheuristics, 2008. Extended Abstract. [ bib ]
[49] P. Balaprakash, M. Birattari, T. Stutzle, and M. Dorigo. Estimation-based stochastic local search algorithms for the stochastic routing problems. In E.-G. Talbi and K. Mellouli, editors, International Conference on Metaheuristics and Nature Inspired Computing, META'08, 2008. Extended Abstract. [ bib ]
[50] G. Di Tollo and P. Balaprakash. Index tracking by estimation-based local search. In A. Amendola, D. Belsley, E. Kontoghiorghes, and M. Paolella, editors, Second International Workshop on Computational and Financial Econometrics, CFE'08, 2008. Abstract. [ bib ]
[51] P. Balaprakash, M. Birattari, and T. Stützle. Improvement strategies for the F-Race algorithm: Sampling design and iterative refinement. In T. Bartz-Beielstein, M. Blesa Aguilera, C. Blum, B. Naujoks, A. Roli, G. Rudolph, and M. Sampels, editors, Hybrid Metaheuristics, volume 4771 of Lecture Notes in Computer Science, pages 108–122. Springer Berlin Heidelberg, 2007. [ bib | DOI ]
[52] M. Birattari, P. Balaprakash, and M. Dorigo. The ACO/F-Race algorithm for combinatorial optimization under uncertainty. In K. F. Doerner, M. Gendreau, P. Greistorfer, W. Gutjahr, R. F. Hartl, and M. Reimann, editors, Metaheuristics - Progress in Complex Systems Optimization, volume 39 of Operations Research/Computer Science Interfaces Series, pages 189–203. Springer US, 2007. [ bib | DOI ]
[53] P. Balaprakash, M. Birattari, T. Stützle, and M. Dorigo. An experimental study of estimation-based metaheuristics for the probabilistic traveling salesman problem. In V. Maniezzo, R. Battiti, and J.-P. Watson, editors, LION 2007 II: Learning and Intelligent Optimization., pages 8–12, 2007. Extended Abstract. [ bib ]
[54] P. Balaprakash, M. Birattari, T. Stützle, and M. Dorigo. Sampling strategies and local search for stochastic combinatorial optimization. In E. Ridge, T. Stützle, M. Birattari, and H. H. Hoos, editors, SLS-DS 2007: Doctoral Symposium on Engineering Stochastic Local Search Algorithms, pages 16–20, 2007. Nominated for the best paper award. [ bib ]
[55] M. Birattari, P. Balaprakash, T. Stützle, and M. Dorigo. Estimation-based local search for the probabilistic traveling salesman problem. In M. Gendreau, T. G. Crainic, L.-M. Rousseau, and P. Soriano, editors, Proceedings of MIC 2007, page 141, 2007. [ bib ]
[56] P. Balaprakash, M. Birattari, T. Stützle, and M. Dorigo. Incremental local search in ant colony optimization: Why it fails for the quadratic assignment problem. In M. Dorigo, L. Gambardella, M. Birattari, A. Martinoli, R. Poli, and T. Stützle, editors, Ant Colony Optimization and Swarm Intelligence, volume 4150 of Lecture Notes in Computer Science, pages 156–166. Springer Berlin Heidelberg, 2006. [ bib | DOI ]
[57] D. L. Prakash, P. Balaprakash, and D. Regener. Computational microstructure analyzing technique for quantitative characterization of shrinkage and gas pores in pressure die cast az91 magnesium alloys. Computational Materials Science, 32(3–4):480—488, 2005. [ bib | DOI ]
[58] M. Birattari, P. Balaprakash, and M. Dorigo. ACO/F-Race: Ant colony optimization and racing techniques for combinatorial optimization under uncertainty. In MIC 2005: The 6th Metaheuristics International Conference, pages 107–112. Vienna, Austria: University of Vienna, Department of Business Administration, 2005. [ bib | .pdf ]
[59] P. Balaprakash. Ant colony optimization under uncertainty. Master's thesis, Université Libre de Bruxelles, Brussels, Belgium, 2005. [ bib | .pdf ]
[60] P. Balaprakash. Pre-processing of stochastic Petri nets and an improved storage strategy for proxel based simulation. Master's thesis, Otto-von-Guericke-Universität Magdeburg, 2004. [ bib | .pdf ]

Posters and poster extended abstracts (peer reviewd)

[1] P. Balaprakash, V. Morozov, and R. Kettimuthu. Improving throughput by dynamically adapting concurrency of data transfer. High Performance Computing, Networking, Storage and Analysis (SCC), 2015 SC, 2015. Poster. [ bib ]
[2] Y. Alexeev and P. Balaprakash. Heuristic dynamic load balancing algorithm applied to the fragment molecular orbital method. High Performance Computing, Networking, Storage and Analysis (SCC), 2015 SC, 2015. Poster. [ bib ]
[3] Y. Alexeev and P. Balaprakash. Heuristic dynamic load balancing algorithm applied to the fragment molecular orbital method. High Performance Computing, Networking, Storage and Analysis (SCC), 2015 SC, 2015. Poster Extended Abstract. [ bib ]
[4] P. Balaprakash, V. Morozov, and R. Kettimuthu. Improving throughput by dynamically adapting concurrency of data transfer. High Performance Computing, Networking, Storage and Analysis (SCC), 2015 SC, 2015. Poster Extended Abstract. [ bib ]
[5] L. A. Gomez, P. Balaprakash, M.-S. Bouguerra, S. M. Wild, F. Cappello, and P. D. Hovland. Energy-performance tradeoffs in multilevel checkpoint strategies. In 2014 IEEE International Conference on Cluster Computing (CLUSTER), pages 278–279, 2014. Poster Extended Abstract. [ bib | DOI ]
[6] Y. Zhang, P. Balaprakash, J. Meng, V. Morozov, S. Parker, and K. Kumaran. Raexplore: Enabling rapid, automated architecture exploration for full applications. High Performance Computing, Networking, Storage and Analysis (SCC), 2014 SC, 2014. Poster. [ bib ]
[7] Y. Zhang, P. Balaprakash, J. Meng, V. Morozov, S. Parker, and K. Kumaran. Raexplore: Enabling rapid, automated architecture exploration for full applications. High Performance Computing, Networking, Storage and Analysis (SCC), 2014 SC, 2014. Poster Extended Abstract. [ bib ]
[8] L. A. Gomez, P. Balaprakash, M.-S. Bouguerra, S. M. Wild, F. Cappello, and P. D. Hovland. Energy-performance tradeoffs in multilevel checkpoint strategies. 2014 IEEE International Conference on Cluster Computing (CLUSTER), 2014. Poster. [ bib ]
[9] P. Balaprakash, D. Buntinas, A. Chan, A. Guha, R. Gupta, S. Narayanan, A. Chien, P. Hovland, and B. Norris. Exascale workload characterization and architecture implications. In 2013 IEEE International Symposium on Performance Analysis of Systems and Software (ISPASS), pages 120–121, 2013. Poster Extended Abstract. [ bib | DOI ]
[10] P. Balaprakash, A. Tiwari, and S. M. Wild. Framework for optimizing power, energy, and performance. High Performance Computing, Networking, Storage and Analysis (SCC), 2013 SC, 2013. Poster. Nominated for the best poster award. [ bib ]
[11] P. Balaprakash, D. Buntinas, A. Chan, A. Guha, R. Gupta, S. Narayanan, A. Chien, P. Hovland, and B. Norris. Exascale workload characterization and architecture implications. 2013 IEEE International Symposium on Performance Analysis of Systems and Software (ISPASS), 2013. Poster. [ bib ]
[12] P. Balaprakash, D. Buntinas, A. Chan, A. Guha, R. Gupta, S. Narayanan, A. Chien, P. Hovland, and B. Norris. An exascale workload study. In High Performance Computing, Networking, Storage and Analysis (SCC), 2012 SC Companion, pages 1463–1464, 2012. Poster Extended Abstract. [ bib | DOI ]
[13] P. Balaprakash, D. Buntinas, A. Chan, A. Guha, R. Gupta, S. Narayanan, A. Chien, P. Hovland, and B. Norris. An exascale workload study. High Performance Computing, Networking, Storage and Analysis (SCC), 2012 SC Companion, 2012. Poster. [ bib ]
[14] P. Balaprakash, S. M. Wild, and P. D. Hovland. Model-based optimization algorithms for empirical performance tuning. 2011 DOE Applied Mathematics Program Meeting, 2011. Poster. [ bib ]

Talks

[1] P. Balaprakash. Machine learning for high performance computing. Midwest Big Data Opportunities and Challenges, Chicago, IL, September 2016. [ bib ]
[2] P. Balaprakash. Improving data transfer throughput with direct search optimization. The 45th International Conference on Parallel Processing, ICPP 2016, Philadelphia, PA, August 2016. [ bib ]
[3] P. Balaprakash. Multi objective optimization of HPC kernels for performance, power, and energy. 6th Joint Laboratory for Extreme Scale Computing (JLESC) Workshop, Kobe, Japan, June 2016. [ bib ]
[4] P. Balaprakash. AutoMOMML: Automatic Multi-objective Modeling with Machine Learning. International Supercomputing Conference, ISC 2016, Frankfurt, Germany, June 2016. [ bib ]
[5] P. Balaprakash. AutoMOMML: Automatic Multi-objective Modeling with Machine Learning. 5th Joint Laboratory for Extreme Scale Computing (JLESC) Workshop, Lyon, France, June 2016. [ bib ]
[6] P. Balaprakash. Exploiting performance portability in search algorithms for autotuning. 11th International Workshop on Automatic Performance Tuning, iWAPT 2016, Chicago, IL, May 2016. [ bib ]
[7] P. Balaprakash. Predictive modeling for large scale vehicle simulations. Urban Data Analytics/City of Chicago SmartData Platform Workshop, Chicago, IL, May 2015. [ bib ]
[8] P. Balaprakash. Self-aware runtime and operating systems. 2015 DOE ASCR Machine Learning Workshop, Rockville, MD, January 2015. [ bib ]
[9] P. Balaprakash. Automatic performance modeling and tuning. Department of Mathematics, Statistics and Computer Science Colloquium, Marquette University, Milwaukee, WI, October 2014. [ bib ]
[10] P. Balaprakash. Machine-learning-based load balancing for community ice code component in CESM. 11th International Meeting on High-Performance Computing for Computational Science (VECPAR 2014), Eugene, OR, July 2014. Conference Talk. [ bib ]
[11] P. Balaprakash. Active-learning-based surrogate models for empirical performance tuning. Computation Institute, UChicago, Lemont, IL, February 2014. [ bib ]
[12] P. Balaprakash. Active-learning-based surrogate models for empirical performance tuning. 10th workshop of the INRIA-Illinois-ANL Joint Laboratory, Urbana, IL, November 2013. [ bib ]
[13] P. Balaprakash. Multi objective optimization of HPC kernels for performance, power, and energy. 4th International Workshop on Performance Modeling, Benchmarking and Simulation of High Performance Computer Systems (PMBS13), Denver, CO, November 2013. [ bib ]
[14] P. Balaprakash. Active-learning-based surrogate models for empirical performance tuning. 2013 IEEE International Conference on Cluster Computing (CLUSTER), Indianapolis, IN, September 2013. [ bib ]
[15] P. Balaprakash. Search algorithms in empirical performance tuning and machine learning for computationally expensive simulations. PMaC/SDSC Seminar, San Diego, CA, July 2013. [ bib ]
[16] P. Balaprakash. Search algorithms in empirical performance tuning and machine learning for computationally expensive simulations. ANL Mathematics and Computer Science Division Seminar, Lemont, IL, December 2012. [ bib ]
[17] P. Balaprakash. A sequential learning approach for quantum chemistry simulations. IPAM Chemical Compound Space Reunion, Lake Arrowhead, CA, December 2012. [ bib ]
[18] P. Balaprakash. SPAPT: Search Problems in Automatic Performance Tuning. Workshop on Tools for Program Development and Analysis in Computational Science, International Conference on Computational Science, ICCS 2012, Omaha, NE, June 2012. [ bib ]
[19] P. Balaprakash. Efficient optimization algorithms for empirical performance tuning. SIAM Conference on Parallel Processing (SIAM PP 2012), Savannah, GA, February 2012. [ bib ]
[20] P. Balaprakash. Global and local search algorithms in empirical performance tuning. DOE CScADS Workshop on Libraries and Autotuning for Extreme-Scale Systems, Snowbird, UT, August 2011. [ bib ]
[21] P. Balaprakash. Can search algorithms save large-scale automatic performance tuning? Workshop on Automatic Performance Tuning, International Conference on Computational Science, ICCS 2011, Singapore, June 2011. [ bib ]
[22] P. Balaprakash. Comparison of search strategies in empirical performance tuning of linear algebra kernels. 2011 SIAM Conference on Computational Science and Engineering, Reno, NV, March 2011. [ bib ]
[23] P. Balaprakash. An experimental study of estimation-based metaheuristics for the probabilistic traveling salesman problem. LION 2007 II: Learning and Intelligent Optimization, Trento, Italy, December 2007. [ bib ]
[24] P. Balaprakash. Improvement strategies for the F-Race algorithm: Sampling design and iterative refinement. 4th International Workshop on Hybrid Metaheuristics, Dortmund, Germany, October 2007. [ bib ]
[25] P. Balaprakash. ACO/F-Race: Ant colony optimization and racing techniques for combinatorial optimization under uncertainty. MIC 2005: The 6th Metaheuristics International Conference, Vienna, Austria, August 2005. [ bib ]