Argonne National Laboratory

Prasanna Balaprakash


Publications

Google Scholar Profile

Recent preprints/papers

  1. P. Balaprakash, S. M. Wild, P. D. Hovland. Performance modeling for exascale autotuning: An integrated approach. Preprint ANL/MCS-P5000-0813, July 2013. MCS White Paper.
  2. P. Balaprakash, K. Rupp, A. Mametjanov, R. B. Gramacy, P. D. Hovland, S. M. Wild. Empirical performance modeling of GPU kernels using active learning. In proceedings of International Conference on Parallel Computing - ParCo2013.
  3. P. Balaprakash, R. B. Gramacy, S. M. Wild. Active-learning-based surrogate models for empirical performance tuning. In proceedings of IEEE Cluster 2013.
  4. P. Balaprakash, Y. Alexeev, S. Mickelson, S. Leyffer, R. Jacob, A. Craig. Machine learning based load-balancing for the CESM climate modeling package, Preprint ANL/MCS-P4070-0413, April 2013.
  5. P. Balaprakash, A. Tiwari, S. M. Wild. Multi-objective optimization of HPC kernels for performance, power, and energy, Preprint ANL/MCS-P4069-0413, April 2013.
  6. P. Balaprakash, M. Birattari, T. Stützle, and M. Dorigo. Estimation-based metaheuristics for the vehicle routing problem with stochastic demands and customers. Submitted to Journal of Computational Optimization and Applications.
  7. P. Balaprakash, D. Buntinas, A. Chan, A. Guha, R. Gupta, S. H. K. Narayanan, A. A. Chien, P. Hovland, B. Norris. Exascale workload characterization and architecture implications. 21st High Performance Computing Symposia (HPC), San Diego, April 2013. Best Paper Award
  8. P. Balaprakash, S. M. Wild, P. D. Hovland. An experimental study of global and local search algorithms in empirical performance tuning. High Performance Computing for Computational Science - VECPAR 2012, LNCS Vol. 7851, pp. 261–269, 2013.
  9. P. Balaprakash, S. M. Wild, and B. Norris. SPAPT: Search problems in automatic performance tuning, ICCS 2012. In Procedia Computer Science, Vol. 9, pp. 1959–1968, 2012.
  10. P. Balaprakash, S. M. Wild, and P. Hovland, Can search algorithms save large-scale automatic performance tuning? In Proceedings of the International Conference on Computational Science, ICCS 2011, Procedia Computer Science, Vol. 4, pp. 2136–2145, 2011.

Journal articles

  1. P. Balaprakash, M. Birattari, T. Stützle, and M. Dorigo. Estimation-based metaheuristics for the probabilistic traveling salesman problem. Computers and Operations Research, 37(11):1939–1951, 2010.
  2. 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.
  3. 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.
  4. M. Birattari, P. Balaprakash, T. Stützle, and M. Dorigo. Estimation-based local search for stochastic combinatorial optimization using delta evaluations: A case study in the probabilistic traveling salesman problem. INFORMS Journal on Computing, 20(4):644–658, 2008.
  5. D.G. Leo 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. Journal of Computational Materials Science, 32(3-4):480–488, 2005.

Refereed book chapters

  1. M. Birattari, Z. Yuan, P. Balaprakash, and T. Stützle. F-Race and iterated F-Race: An overview. In T. Bartz-Beielstein et. al. (Eds.), Experimental Methods for the Analysis of Optimization Algorithms, pages 311–336. Berlin, Germany, 2010. Springer
  2. P. Balaprakash, M. Birattari, and T. Stützle. Engineering stochastic local search algorithms: A case study in estimation-based local search for the probabilistic traveling salesman problem. In C. Cotta and J. van Hemert (Eds.), Recent Advances in Evolutionary Computation for Combinatorial Optimization, volume 153 of Studies in Computational Intelligence, pages 53–66, Berlin, Germany, 2008. Springer.
  3. M. Birattari, P. Balaprakash, and M. Dorigo. The ACO/F-Race algorithm for combinatorial optimization under uncertainty. In K. F. Doerner et. al. (Eds.), Metaheuristics - Progress in Complex Systems Optimization, Operations Research/Computer Science Interfaces Series, pages 189–203, Berlin, Germany, 2006. Springer.

Newsletter

  1. P. Balaprakash. Estimation-based metaheuristics for stochastic combinatorial optimization: Case studies in stochastic routing problems. ACM SIGEVOlution, 5(1):18–19, 2010.

Theses

  1. P. Balaprakash. Estimation-based metaheuristics for stochastic combinatorial optimization: Case studies in stochastic routing problems. Ph.D. thesis, Université Libre de Bruxelles, Belgium, 2010.
  2. P. Balaprakash. Ant colony optimization under uncertainty. D.E.A. thesis, Université Libre de Bruxelles, Belgium, 2005.
  3. P. Balaprakash. Preprocessing of stochastic Petri nets and an improved storage strategy for proxel based simulation. Master’s thesis, Otto-von-Guericke Universität Magdeburg, Germany, 2004.

Refereed proceeding articles

  1. M. Birattari, Z. Yuan, P. Balaprakash, T. Stützle. Automated algorithm tuning using F-races: Recent developments. In S. Voss and M. Caserta (Eds.) MIC 2009: Eighth Metaheuristics International Conference, July 13-16, 2009, Hamburg, Germany.
  2. Z. Yuan, A. Fügenschuh, H. Homfeld, P. Balaprakash, T. Stützle, M. Schoch. Hybrid iterated constructive algorithms for scheduling locomotives in freight transport. In M. J. Blesa et. al. (Eds.) Hybrid Metaheuristics: Fifth International Workshop on Hybrid Metaheuristics, LNCS, 5296 pp. 102–116. Springer Verlag, Berlin, Germany.
  3. P. Balaprakash, M. Birattari, T. Stützle, and M. Dorigo. Sampling strategies and local search for stochastic combinatorial optimization. In E. Ridge et. al. (Eds.) SLS-DS 2007: Doctoral Symposium on Engineering Stochastic Local Search Algorithms, pp. 16–20, September 6-8, 2007, Brussels, Belgium. Nominated for the best paper award
  4. P. Balaprakash, M. Birattari, and T. Stützle. Improvement strategies for the F-Race algorithm: Sampling design and iterative refinement. In T. Bartz-Beielstein et. al. (Eds.) HM 2007: Fourth International Workshop on Hybrid Metaheuristics, LNCS, 4771, pp. 113–127. Springer Verlag, Berlin, Germany.
  5. 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 et. al. (Eds.) Ant Colony Optimization and Swarm Intelligence, Fifth International Workshop, ANTS 2006, LNCS 4150 pp. 156–166. Springer Verlag, Berlin, Germany.
  6. M. Birattari, P. Balaprakash, and M. Dorigo. ACO/F-Race: Ant colony optimization and racing techniques for combinatorial optimization under uncertainty. In R. F. Hartl et. al. (Eds.) MIC 2005: Sixth Metaheuristics International Conference, August 22-26, 2005, Vienna, Austria.
  7. D.G. Leo Prakash, P. Balaprakash, D. Regener. Computational microstructure analyzing technique for quantitative characterization of shrinkage and gas pores in pressure die cast AZ91 magnesium alloys. In Thirteenth International Workshop on Computational Mechanics of Materials, September 22-23, 2003, Magdeburg, Germany.

Refereed extended abstracts

  1. P. Balaprakash, D. Buntinas, A. Chan, A. Guha, R. Gupta, S. H. K. Narayanan, A. A. Chien, P. Hovland, B. Norris. Exascale workload characterization and architecture implications, 2013 IEEE International Symposium on Performance Analysis of Systems Software (ISPASS), April 2013.
  2. P. Balaprakash, D. Buntinas, A. Chan, A. Guha, R. Gupta, S. H. K. Narayanan, A. A. Chien, P. Hovland, B. Norris. An exascale workload study, ACM/IEEE Conference on Supercomputing (SC), November 2012.
  3. P. Balaprakash and O. A. Lilienfeld. A sequential learning approach for quantum chemistry simulations. IPAM Chemical Compound Space Reunion conference, December 9–14, 2012, Lake Arrowhead, CA.
  4. P. Balaprakash, M. Birattari, T. Stützle, and M. Dorigo. Effective estimation-based stochastic local search algorithms for stochastic routing problems. In Schyns et. al. (Eds.), 24th Conference on Quantitative Methods for Decision Making, ORBEL 24, January 28-29, 2010, Liège, Belgium
  5. P. Balaprakash, M. Birattari, T. Stützle, and M. Dorigo. Estimation-based stochastic local search algorithms for the stochastic routing problems. In E.-G. Talbi and K. Mellouli (Eds.) International Conference on Metaheuristics and Nature Inspired Computing, META’08, October 29-31, 2008, Hammamet, Tunisia.
  6. G. di Tollo and P. Balaprakash. Index tracking by estimation-based local search. In A. Amendola (Eds.) Second International Workshop on Computational and Financial Econometrics, CFE’08, June 19-21, 2008, Neuchatel, Switzerland.
  7. P. Balaprakash, M. Birattari, T. Stützle, and M. Dorigo. Applications of estimation-based SLS algorithms to stochastic routing problems. In P. Hansen and S. Voss (Eds.) Metaheuristics 2008, Second International Workshop on Model Based Metaheuristics, June 16-18, 2008, Bertinoro, Italy.
  8. 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 et. al. (Eds.) LION 2007 II: Learning and Intelligent Optimization, December 8-12, 2007, Trento, Italy.
  9. M. Birattari, P. Balaprakash, T. Stützle, and M. Dorigo. Estimation-based local search for the probabilistic traveling salesman problem. In M. Gendreau et. al (Eds.) MIC 2007: Seventh Metaheuristics International Conference, June 25-29, 2007, Montreal, Canada.

Recent talks, presentations, and posters

  1. Poster at IEEE International Symposium on Performance Analysis of Systems Software (ISPASS), Austin, TX, April 2013. Title: Exascale Workload Characterization and Architecture Implications.
  2. Poster at ACM/IEEE Conference on Supercomputing (SC), November 2012, Salt Lake City, UT, November 2012. Title: An Exascale Workload Study
  3. Talk at Chemical Compound Space Reunion (invitation only) conference - Lake Arrowhead, CA, December 2012. Title: A sequential learning approach for quantum chemistry simulations.
  4. Talk at SIAM Conference on Parallel Processing (SIAM PP 2012), Savannah, GA, February 2012. Title: Efficient Optimization Algorithms for Empirical Performance Tuning.
  5. Talk at Workshop on Tools for Program Development and Analysis in Computational Science, International Conference on Computational Science (ICCS 2012), Omaha, Nebraska, June 2012. Title: SPAPT: Search Problems in Automatic Performance Tuning.
  6. Talk at DOE CScADS Workshop on Libraries and Autotuning for Extreme-Scale Systems, Snowbird, UT, August 2012. Title: Global and local search algorithms in empirical performance tuning.
  7. Poster at 2011 DOE Applied Mathematics Program Meeting, Washington, DC, October 2011. Title: Model-Based Optimization Algorithms for Empirical Performance Tuning.
  8. Talk at SIAM Conference on Computational Science and Engineering (SIAM CSE 2011), Reno, NV, March 4, 2011. Title: Comparison of search strategies in empirical performance tuning of linear algebra kernels.
  9. Talk at the International Conference on Computational Science (ICCS 2011), Singapore, June 2011. Title: Can search algorithms save large-scale auto- matic performance tuning?
  10. Talk at CACHE Institute meeting. Berkeley, CA, August 2011. Title: Optimization-based search for autotuning.

Open-source software

  1. 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 Orio autotuning framework (with Stefan Wild and Boyana Norris).
    http://tinyurl.com/cskagx9
  2. 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 Stützle, Mauro Birattari, Eric Yuan, and Prasanna Balaprakash).
    http://cran.r-project.org/web/packages/irace/
  3. 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