Home Page of Prasanna BALAPRAKASH

Publications

[1]	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. DOE ASCR Workshop Report, 2015. [ bib \| .pdf ]
[2]	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 ]
[3]	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 ]
[4]	P. Balaprakash, A. Tiwari, S. M. Wild, and P. D. Hovland. AutoMOMML: Automatic multiple objectives modeling with machine learning. Technical report, Argonne National Laboratory, 2015. [ bib ]
[5]	A. Roy, P. Balaprakash, P. D. Hovland, and S. M. Wild. Exploiting performance portability in search algorithms for autotuning. Technical Report ANL/MCS-P5400-0915, Argonne National Laboratory, 2015. [ bib ]
[6]	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. [ bib \| DOI ]
[7]	T. Nelson, A. Rivera, P. Balaprakash, M. Hall, P. D. Hovland, E. Jessup, and B. Norris. Generating efficient tensor contractions for GPUs. In Proceedings of the 44th International Conference on Parallel Processing (ICPP), 2015. Acceptance rate 32.5%. [ bib ]
[8]	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 ]
[9]	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). IEEE, 2015. Acceptance rate 24%. [ bib ]
[10]	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 ]
[11]	P. Balaprakash, A. Tiwari, and S. M. Wild. Framework for optimizing power, energy, and performance. The SUPER Project Newsletter, 2014. [ bib \| .pdf ]
[12]	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 ]
[13]	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 ]
[14]	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. [ bib \| DOI ]
[15]	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 ]
[16]	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 ]
[17]	P. Balaprakash, S. M. Wild, and P. D. Hovland. Performance modeling for exascale autotuning: An integrated approach. White Paper, 2013. [ bib ]
[18]	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 ]
[19]	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. Best Paper Award. [ bib \| http ]
[20]	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. IEEE, 2013. [ bib \| DOI ]
[21]	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 International Conference on Parallel Computing - ParCo2013, Advances in Parallel Computing, pages 646–655, 2013. [ bib \| DOI ]
[22]	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 ]
[23]	P. Balaprakash and O. A. Lilienfeld. A sequential learning approach for quantum chemistry simulations. In IPAM Chemical Compound Space Reunion, 2012. Invited Abstract. [ bib ]
[24]	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 ]
[25]	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 ]
[26]	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 ]
[27]	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 ]
[28]	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 ]
[29]	P. Balaprakash. Estimation-based metaheuristics for stochastic combinatorial optimization: Case studies in stochastic routing problems. PhD thesis, Université Libre de Bruxelles, 2010. [ bib \| .pdf ]
[30]	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 ]
[31]	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 ]
[32]	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 ]
[33]	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 ]
[34]	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 ]
[35]	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 ]
[36]	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 ]
[37]	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 ]
[38]	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 ]
[39]	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 ]
[40]	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 ]
[41]	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 ]
[42]	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 ]
[43]	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 ]
[44]	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 ]
[45]	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 ]
[46]	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 ]
[47]	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 ]
[48]	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 ]
[49]	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 ]
[50]	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 ]
[51]	P. Balaprakash. Ant colony optimization under uncertainty. Master's thesis, Université Libre de Bruxelles, Brussels, Belgium, 2005. [ bib \| .pdf ]
[52]	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 ]

Open-source software

SPAPT: A set of extensible and portable search problems in automatic performance tuning whose goal is to aid in the development and improvement of search strategies and performance-improving transformations. SPAPT contains representative implementations from a number of lower-level, serial performance-tuning tasks in scientific applications. Available with the Orio autotuning framework (with Stefan Wild and Boyana Norris). http://tinyurl.com/cskagx9
The irace Package: The irace package implements the iterated racing procedure, which is an extension of the Iterated F-race procedure. Its main purpose is to automatically configure optimization algorithms by finding the most appropriate settings given a set of instances of an optimization problem. It builds upon the race package by Birattari, and it is implemented in R (maintainers: Manuel Lopez-Ibanez and Jeremie Dubois-Lacoste; contributors: Thomas Stuetzle, Mauro Birattari, Eric Yuan, and Prasanna Balaprakash). http://cran.r-project.org/web/packages/irace/
ELS-PTSP: This software package provides a high-performance implementation of the estimation-based iterative improvement algorithm to tackle the probabilistic traveling salesman problem. A key novelty of the proposed algorithm is that the cost difference between two neighbor solutions is estimated by partial evaluation, adaptive, and importance sampling. Developed in C with GNU scientific library under Linux. http://els-ptsp.googlecode.com

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. Self-aware runtime and operating systems. 2015 ASCR Machine Learning Workshop, January 2015. Invited Talk. [ bib ]
[2]	P. Balaprakash. Automatic performance modeling and tuning. Department of Mathematics, Statistics and Computer Science Colloquium, Marquette University, October 2014. Invited Talk. [ bib ]
[3]	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), July 2014. Conference Talk. [ bib ]
[4]	P. Balaprakash. Active-learning-based surrogate models for empirical performance tuning. Computation Institute, UChicago, February 2014. Computation Institute Talk. [ bib ]
[5]	P. Balaprakash. Active-learning-based surrogate models for empirical performance tuning. 10th workshop of the INRIA-Illinois-ANL Joint Laboratory, November 2013. Invited Talk. [ bib ]
[6]	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), November 2013. Workshop Talk. [ bib ]
[7]	P. Balaprakash. Active-learning-based surrogate models for empirical performance tuning. 2013 IEEE International Conference on Cluster Computing (CLUSTER), September 2013. Conference Talk. [ bib ]
[8]	P. Balaprakash. A sequential learning approach for quantum chemistry simulations. IPAM Chemical Compound Space Reunion, December 2012. Invited Talk. [ bib ]
[9]	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, June 2012. Workshop Talk. [ bib ]
[10]	P. Balaprakash. Efficient optimization algorithms for empirical performance tuning. SIAM Conference on Parallel Processing (SIAM PP 2012), February 2012. Conference Talk. [ bib ]
[11]	P. Balaprakash. Global and local search algorithms in empirical performance tuning. DOE CScADS Workshop on Libraries and Autotuning for Extreme-Scale Systems, August 2011. Invited Talk. [ bib ]
[12]	P. Balaprakash. Can search algorithms save large-scale automatic performance tuning? Workshop on Automatic Performance Tuning, International Conference on Computational Science, ICCS 2011, June 2011. Workshop Talk. [ bib ]
[13]	P. Balaprakash. Comparison of search strategies in empirical performance tuning of linear algebra kernels. 2011 SIAM Conference on Computational Science and Engineering, March 2011. Conference Talk. [ bib ]

Prasanna Balaprakash

Publications

Open-source software

Posters and poster extended abstracts (peer reviewd)

Talks