[1]
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S. Khairy and P. Balaprakash.
Challenges and opportunities for ai-enabled science applications over
5g.
In 5G Enabled Energy Innovation Workshop (5GEEIW), March 2020.
[ bib ]
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[2]
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S. Khairy and P. Balaprakash.
Edge intelligence meets cloud intelligence over 5g: Unmanned aerial
vehicle swarm for extremeenvironments.
In 5G Enabled Energy Innovation Workshop (5GEEIW), March 2020.
[ bib ]
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[3]
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P. Beckman, C. Catlett, M. Ahmed, M. Alawad, L. Bai, P. Balaprakash, K. Barker,
P. Beckman, R. Berry, A. Bhuyan, G. Brebner, K. Burkes, A. Butko,
F. Cappello, R. Chard, S. Collis, J. Cree, D. Dasgupta, A. Evdokimov, J. M.
Fields, P. Fuhr, C. Harper, Y. Jin, R. Kettimuthu, M. Kiran, R. Kozma, P. A.
Kumar, Y. Kumar, L. Luo, L. Mashayekhy, I. Monga, B. Nickless, T. Pappas,
E. Peterson, T. Pfeffer, S. Rakheja, V. R. Tribaldos, S. Rooke, S. Roy,
T. Saadawi, A. Sandy, R. Sankaran, N. Schwarz, S. Somnath, M. Stan,
C. Stuart, R. Sullivan, A. Sumant, G. Tchilinguirian, N. Tran, A. Veeramany,
A. Wang, B. Wang, A. Wiedlea, S. Wielandt, T. Windus, Y. Wu, X. Yang, Z. Yao,
R. Yu, Y. Zeng, and Y. Zhang.
5g enabled energy innovation: Advanced wireless networks for science,
workshop report.
2020.
[ bib |
DOI ]
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[4]
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Y. He, P. Balaprakash, and Y. Li.
FIdelity: Efficient Resilience Analysis Framework for Deep Learning
Accelerators.
In 53rd IEEE/ACM International Symposium on Microarchitecture
(MICRO), 2020.
[ bib ]
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[5]
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S. Madireddy, A. Yanguas-Gil, and P. Balaprakash.
Multilayer neuromodulated architectures for memory-constrained online
continual learning.
In ICML Workshop on LifelongML, 2020.
[ bib ]
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[6]
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T. Mallick, P. Balaprakash, E. Rask, and J. Macfarlane.
Graph-partitioning-based diffusion convolutional recurrent neural
network for large-scale traffic forecasting.
Transportation Research Record, 2020.
[ bib ]
Traffic forecasting approaches are critical to developing adaptive strategies for mobility. Traffic patterns have complex spatial and temporal dependencies that make accurate forecasting on large highway networks a challenging task. Recently, diffusion convolutional recurrent neural networks (DCRNNs) have achieved state-of-the-art results in traffic forecasting by capturing the spatiotemporal dynamics of the traffic. Despite the promising results, however, applying DCRNNs for large highway networks still remains elusive because of computational and memory bottlenecks. This paper presents an approach for implementing a DCRNN for a large highway network that overcomes these limitations. This approach uses a graph-partitioning method to decompose a large highway network into smaller networks and trains them independently. The efficacy of the graph-partitioning-based DCRNN approach to model the traffic on a large California highway network with 11,160 sensor locations is demonstrated. An overlapping-nodes approach for the graph-partitioning-based DCRNN is developed to include sensor locations from partitions that are geographically close to a given partition. Furthermore, it is demonstrated that the DCRNN model can be used to forecast the speed and flow simultaneously and that the forecasted values preserve fundamental traffic flow dynamics. This approach to developing DCRNN models that represent large highway networks can be a potential core capability in advanced highway traffic monitoring systems, where a trained DCRNN model forecasting traffic at all sensor locations can be used to adjust traffic management strategies proactively based on anticipated future conditions.
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[7]
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R. Maulik, N. A. Garland, J. W. Burby, X.-Z. Tang, and P. Balaprakash.
Neural network representability of fully ionized plasma fluid model
closures.
Physics of Plasmas, 27(7):072106, 2020.
[ bib ]
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[8]
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R. Maulik, B. Lusch, and P. Balaprakash.
Non-autoregressive time-series methods for stable parametric
reduced-order models.
Physics of Fluids, 32(8), 2020.
[ bib ]
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[9]
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S. Khairy, R. Shaydulin, L. Cincio, Y. Alexeev, and P. Balaprakash.
Learning to optimize variational quantum circuits to solve
combinatorial problems.
In AAAI Conference on Artificial Intelligence, 2020.
[ bib ]
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[10]
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S. Khairy, P. Balaprakash, L. X. Cai, and Y. Cheng.
Constrained deep reinforcement learning for energy sustainable
multi-uav based random access IoT networks with NOMA.
IEEE Journal on Selected Areas in Communications, 2020.
[ bib ]
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[11]
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M. Isakov, E. Rosario, S. Madireddy, P. Balaprakash, P. Carns, R. Ross, and
M. Kinsy.
HPC I/O throughput bottleneck analysis with explainable local
models.
In SC '20: IEEE/ACM International Conference on High Performance
Computing, Networking, Storage and Analysis, 2020.
[ bib ]
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[12]
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R. Maulik, R. Egele, B. Lusch, and P. Balaprakash.
Recurrent neural network architecture search for geophysical
emulation.
In SC '20: IEEE/ACM International Conference on High Performance
Computing, Networking, Storage and Analysis, 2020.
[ bib ]
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[13]
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R. Maulik, V. Rao, S. Madireddy, B. Lusch, and P. Balaprakash.
Using recurrent neural networks for nonlinear component computation
in advection-dominated reduced-order models.
In NeurIPS Workshop on ML and the Physical Sciences, 2019.
[ bib ]
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[14]
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R. Maulik and P. Balaprakash.
Site-specific graph neural network for predicting protonation energy
of oxygenate molecules.
In NeurIPS Workshop on ML and the Physical Sciences, 2019.
[ bib ]
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[15]
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S. Madireddy, N. Li, N. Ramachandra, P. Balaprakash, and S. Habib.
Modular deep learning analysis of galaxy-scale strong lensing images.
In NeurIPS Workshop on ML and the Physical Sciences, 2019.
[ bib ]
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[16]
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P. Jiang, H. Doan, S. Madireddy, R. S. Assary, and P. Balaprakash.
Value-added chemical discovery using reinforcement learning.
In NeurIPS Workshop on ML and the Physical Sciences, 2019.
[ bib ]
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[17]
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S. Khairy, R. Shaydulin, L. Cincio, Y. Alexeev, and P. Balaprakash.
Reinforcement-learning-based variational quantum circuits
optimization for combinatorial problems.
In NeurIPS Workshop on ML and the Physical Sciences, 2019.
[ bib ]
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[18]
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R. Maulik, A. Mohan, B. Lusch, S. Madireddy, P. Balaprakash, and D. Livescu.
Time-series learning of latent-space dynamics for reduced-order model
closure.
Physica D: Nonlinear Phenomena, 405, 2019.
[ bib ]
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[19]
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S. Madireddy, D.-W. Chung, T. Loeffler, S. K. Sankaranarayanan, D. N. Seidman,
P. Balaprakash, and O. Heinonen.
Phase segmentation in atom-probe tomography using deep learning-based
edge detection.
Scientific reports, 9(1):1–10, 2019.
[ bib ]
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[20]
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S. Lee, Q. Kang, S. Madireddy, P. Balaprakash, A. Agrawal, A. Choudhary,
R. Archibald, and W.-k. Liao.
Improving scalability of parallel cnn training by adjusting
mini-batch size at run-time.
In 2019 IEEE International Conference on Big Data (Big Data),
pages 830–839. IEEE, 2019.
[ bib ]
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[21]
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P. Balaprakash, R. Egele, M. Salim, S. Wild, V. Vishwanath, F. Xia, T. Brettin,
and R. Stevens.
Scalable reinforcement-learning-based neural architecture search for
cancer deep learning research.
In SC '19: IEEE/ACM International Conference on High Performance
Computing, Networking, Storage and Analysis, 2019.
[ bib ]
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[22]
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J. Wang, P. Balaprakash, and R. Kotamarthi.
Fast domain-aware neural network emulation of a planetary boundary
layer parameterization in a numerical weather forecast model.
Geoscientific Model Development, 2019:1–31, 2019.
[ bib |
DOI ]
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[23]
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N. Wycoff, P. Balaprakash, and F. Xia.
Neuromorphic acceleration for approximate bayesian inference on
neural networks via permanent dropout.
In International Conference on Neuromorphic Computing, 2019.
[ bib ]
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[24]
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S. Madireddy, A. Yanguas-Gil, and P. Balaprakash.
Neuromorphic architecture optimization for task-specific dynamic
learning.
In International Conference on Neuromorphic Computing, 2019.
[ bib ]
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[25]
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S. M. Aithal and P. Balaprakash.
MaLTESE: Large-Scale Simulation-Driven Machine Learning for
Transient Driving Cycles.
In M. Weiland, G. Juckeland, C. Trinitis, and P. Sadayappan, editors,
High Performance Computing, pages 186–205. Springer International
Publishing, 2019.
[ bib ]
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[26]
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C. Kim, K. Kim, P. Balaprakash, and M. Anitescu.
Graph convolutional neural networks for optimal load shedding under
line contingency.
In IEEE Power & Energy Society General Meeting (PESGM), 2019.
[ bib ]
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[27]
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S. Madireddy, P. Balaprakash, P. Carns, R. Latham, G. K. Lockwood, R. Ross,
S. Snyder, and S. M. Wild.
Adaptive learning for concept drift in application performance
modeling.
In Proceedings of the 48th International Conference on Parallel
Processing, ICPP 2019, pages 79:1–79:11, New York, NY, USA, 2019. ACM.
[ bib |
DOI |
http ]
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[28]
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V. Sreenivasan, R. Javali, M. Hall, P. Balaprakash, T. R. W. Scogland, and
B. R. de Supinski.
A framework for enabling openmp autotuning.
In X. Fan, B. R. de Supinski, O. Sinnen, and N. Giacaman, editors,
OpenMP: Conquering the Full Hardware Spectrum, pages 50–60. Springer
International Publishing, 2019.
[ bib ]
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[29]
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J. M. Wozniak, R. Jain, P. Balaprakash, J. Ozik, N. T. Collier, J. Bauer,
F. Xia, T. S. Brettin, R. Stevens, J. Mohd-Yusof, C. Garcia-Cardona,
B. V. Essen, and M. Baughman.
Candle/supervisor: a workflow framework for machine learning applied
to cancer research.
BMC Bioinformatics, 19-S(18):59–69, 2018.
[ bib |
DOI |
http ]
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[30]
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Z. Liu, R. Kettimuthu, P. Balaprakash, and I. Foster.
Building a wide-area data transfer performance predictor: An
empirical study.
In the 1st International Conference on Machine Learning for
Networking, MLN 2018. Springer, 2018.
[ bib ]
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[31]
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S. Madireddy, P. Balaprakash, P. Carns, R. Latham, R. Ross, S. Snyder, and
S. M. Wild.
Modeling I/O performance variability using conditional variational
autoencoders.
In 2018 IEEE International Conference on Cluster Computing
(CLUSTER), 2018.
[ bib ]
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[32]
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S. Madireddy, P. Balaprakash, P. Carns, R. Latham, G. K. Lockwood, R. Ross,
S. Snyder, and S. M. Wild.
Online change point detection and adaptive predictive modeling of
I/O performance.
In In Review, 2018.
[ bib ]
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[33]
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P. Malakar, P. Balaprakash, V. Vishwanath, V. Morozov, and K. Kumaran.
Benchmarking machine learning methods for performance modeling of
scientific applications.
In PMBS 2018: Performance Modeling, Benchmarking and
Simulation of High Performance Computer Systems (held in conjunction with
SC18), 2018.
[ bib ]
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[34]
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P. Balaprakash, J. Larson, V. Vishwanath, and S. Wild.
Derivative-free mixed-integer optimization for automated predictive
modeling using machine learning.
In SciML 2018: DOE ASCR Scientific Machine Learning Workshop,
2018.
[ bib ]
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[35]
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M. Salim, T. Uram, J. Childers, P. Balaprakash, V. Vishwanath, and M. Papka.
Balsam: Automated scheduling and execution of dynamic, data-intensive
HPC workflows.
In Python for High-Performance and Scientific Computing (held in
conjunction with SC18), 2018.
[ bib ]
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[36]
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S. Lee, A. Agrawal, P. Balaprakash, A. Choudhary, and W. Liao.
Communication-efficient parallelization strategy for deep
convolutional neural network training.
In Machine Learning in HPC (held in conjunction with SC18),
2018.
[ bib ]
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[37]
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S. Madireddy, P. Balaprakash, P. Carns, R. Latham, R. Ross, S. Snyder, and
S. M. Wild.
Machine learning based parallel I/O predictive modeling: A case
study on Lustre file systems.
In High Performance Computing, pages 184–204. Springer
International Publishing, 2018.
[ bib ]
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[38]
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O. Subasi, S. Di, L. Bautista-Gomez, P. Balaprakash, O. Unsal, J. Labarta,
A. Cristal, S. Krishnamoorthy, and F. Cappello.
Exploring the capabilities of support vector machines in detecting
silent data corruptions.
Sustainable Computing: Informatics and Systems, 19:277 – 290,
2018.
[ bib |
DOI ]
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[39]
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P. Balaprakash, M. Salim, T. Uram, V. Vishwanath, and S. M. Wild.
DeepHyper: Asynchronous Hyperparameter Search for Deep Neural
Networks.
In 25th IEEE International Conference on High Performance
Computing, Data, and Analytics. IEEE, 2018.
[ bib ]
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[40]
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P. Balaprakash, J. Dongarra, T. Gamblin, M. Hall, J. K. Hollingsworth,
B. Norris, and R. Vuduc.
Autotuning in high-performance computing applications.
Proceedings of the IEEE, pages 1–16, 2018.
[ bib |
DOI ]
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[41]
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I. Foster, T. Lehman, N. Rao, B. Lyles, P. Balaprakash, K. Perumalla,
S. Prowell, and R. Vatsavi.
Towards new generation intelligent networking infrastructure for
distributed science environments.
Technical report, DOE ASCR Workshop Report, 2017.
[ bib |
.pdf ]
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[42]
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A. Mametjanov, P. Balaprakash, C. Choudary, P. D. Hovland, S. M. Wild,
G. Sabin, and G. Wolfe.
Improving FPGA design parameter exploration: Timing, power, and
area.
2017.
[ bib ]
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[43]
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S. Chunduri, P. Balaprakash, V. Morozov, V. Vishwanath, and K. Kumaran.
Analytical performance modeling and validation of intel's xeon phi
architecture.
In Proceedings of the Computing Frontiers Conference, CF'17,
pages 247–250, New York, NY, USA, 2017. ACM.
[ bib |
DOI |
http ]
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[44]
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Z. Liu, P. Balaprakash, R. Kettimuthu, and I. Foster.
Explaining wide area data transfer performance.
In Proceedings of the 26th International Symposium on
High-Performance Parallel and Distributed Computing, HPDC '17, pages
167–178, New York, NY, USA, 2017. ACM.
Acceptance rate 19%.
[ bib |
DOI |
http ]
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[45]
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S. Madireddy, P. Balaprakash, P. Carns, R. Latham, R. Ross, S. Snyder, and
S. M. Wild.
Analysis and correlation of application I/O performance and
system-wide I/O activity.
In 2017 International Conference on Networking, Architecture,
and Storage (NAS), pages 1–10, Aug 2017.
Acceptance rate 33%.
[ bib |
DOI ]
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[46]
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S. Madireddy, P. Balaprakash, P. Carns, R. Latham, R. Ross, S. Snyder, and
S. Wild.
Modeling application I/O performance variability: A probabilistic
graphical model approach, 2017.
[ bib ]
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[47]
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O. Subasi, S. Di, P. Balaprakash, O. Unsal, J. Labarta, A. Cristal,
S. Krishnamoorthy, and F. Cappello.
MACORD: online adaptive machine learning framework for silent error
detection.
In In 3rd Workshop on Fault Tolerance Systems (FTS'17), 2017.
[ bib ]
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[48]
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T. Munson and P. Balaprakash.
Dynamic Adversarial Games in Complex Systems and Machine Learning.
8 2017.
[ bib |
DOI |
http ]
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[49]
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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 ]
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[50]
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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 ]
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[51]
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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 ]
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[52]
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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 ]
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[53]
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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 ]
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[54]
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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 ]
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[55]
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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 ]
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[56]
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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 ]
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[57]
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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 ]
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[58]
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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 ]
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[59]
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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 ]
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[60]
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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 ]
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[61]
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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 ]
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[62]
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P. Balaprakash, A. Tiwari, and S. M. Wild.
Framework for optimizing power, energy, and performance.
In The SUPER Project Newsletter, 2014.
[ bib |
.pdf ]
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[63]
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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 ]
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[64]
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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 ]
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[65]
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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 ]
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[66]
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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 ]
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[67]
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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 ]
|
[68]
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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 ]
|
[69]
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P. Balaprakash, S. M. Wild, and P. D. Hovland.
Performance modeling for exascale autotuning: An integrated
approach.
White Paper, 2013.
[ bib ]
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[70]
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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 ]
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[71]
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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 ]
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[72]
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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 ]
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[73]
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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 ]
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[74]
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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 ]
|
[75]
|
P. Balaprakash and O. A. Lilienfeld.
A sequential learning approach for quantum chemistry simulations.
In IPAM Chemical Compound Space Reunion, 2012.
Invited Abstract.
[ bib ]
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[76]
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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 ]
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[77]
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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 ]
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[78]
|
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 ]
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[79]
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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 ]
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[80]
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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.
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DOI ]
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[81]
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P. Balaprakash.
Estimation-based metaheuristics for stochastic combinatorial
optimization: Case studies in stochastic routing problems.
PhD thesis, Université Libre de Bruxelles, 2010.
[ bib |
.pdf ]
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[82]
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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.
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DOI ]
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[83]
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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.
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DOI ]
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[84]
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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.
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DOI ]
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[85]
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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 ]
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[86]
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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.
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DOI ]
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[87]
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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 ]
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[88]
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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.
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[89]
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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.
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DOI ]
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[90]
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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.
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[91]
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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.
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[92]
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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.
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[93]
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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.
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[94]
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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.
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[95]
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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.
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[96]
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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.
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[97]
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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.
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[98]
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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 ]
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[99]
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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.
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[100]
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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.
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[101]
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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 ]
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[102]
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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.
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[103]
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P. Balaprakash.
Ant colony optimization under uncertainty.
Master's thesis, Université Libre de Bruxelles, Brussels, Belgium,
2005.
[ bib |
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[104]
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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.
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