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ORCIDPrasanna Balaprakash
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2020 – today
- 2021
[i23]Sami Khairy, Prasanna Balaprakash, Lin X. Cai, H. Vincent Poor:
Learning-Based Distributed Random Access for Multi-Cell IoT Networks with NOMA. CoRR abs/2101.00464 (2021)- 2020
- [c44]Sami Khairy, Ruslan Shaydulin, Lukasz Cincio, Yuri Alexeev, Prasanna Balaprakash:
Learning to Optimize Variational Quantum Circuits to Solve Combinatorial Problems. AAAI 2020: 2367-2375 - [c43]Yi He, Prasanna Balaprakash, Yanjing Li:
FIdelity: Efficient Resilience Analysis Framework for Deep Learning Accelerators. MICRO 2020: 270-281 - [c42]Xingfu Wu, Michael Kruse, Prasanna Balaprakash, Hal Finkel, Paul D. Hovland, Valerie E. Taylor, Mary W. Hall:
Autotuning PolyBench Benchmarks with LLVM Clang/Polly Loop Optimization Pragmas Using Bayesian Optimization. PMBS@SC 2020: 61-70 - [c41]Mihailo Isakov, Eliakin Del Rosario, Sandeep Madireddy, Prasanna Balaprakash, Philip H. Carns, Robert B. Ross, Michel A. Kinsy:
Toward Generalizable Models of I/O Throughput. ROSS@SC 2020: 41-49 - [c40]Eliakin Del Rosario, Mikaela Currier, Mihailo Isakov, Sandeep Madireddy, Prasanna Balaprakash, Philip H. Carns, Robert B. Ross, Kevin Harms, Shane Snyder, Michel A. Kinsy:
Gauge: An Interactive Data-Driven Visualization Tool for HPC Application I/O Performance Analysis. PDSW@SC 2020: 15-21 - [i22]Romit Maulik, Rajeev Surendran Array, Prasanna Balaprakash:
Site-specific graph neural network for predicting protonation energy of oxygenate molecules. CoRR abs/2001.03136 (2020) - [i21]Sami Khairy, Prasanna Balaprakash, Lin X. Cai, Yu Cheng:
Constrained Deep Reinforcement Learning for Energy Sustainable Multi-UAV based Random Access IoT Networks with NOMA. CoRR abs/2002.00073 (2020) - [i20]Tanwi Mallick, Prasanna Balaprakash, Eric Rask, Jane MacFarlane:
Transfer Learning with Graph Neural Networks for Short-Term Highway Traffic Forecasting. CoRR abs/2004.08038 (2020) - [i19]Sami Khairy, Prasanna Balaprakash, Lin X. Cai:
A Gradient-Aware Search Algorithm for Constrained Markov Decision Processes. CoRR abs/2005.03718 (2020) - [i18]Nathan Wycoff, Prasanna Balaprakash, Fangfang Xia:
Towards On-Chip Bayesian Neuromorphic Learning. CoRR abs/2005.04165 (2020) - [i17]Sandeep Madireddy, Angel Yanguas-Gil, Prasanna Balaprakash:
Multilayer Neuromodulated Architectures for Memory-Constrained Online Continual Learning. CoRR abs/2007.08159 (2020) - [i16]R. Krishnan, Prasanna Balaprakash:
Meta Continual Learning via Dynamic Programming. CoRR abs/2008.02219 (2020) - [i15]Shengli Jiang, Prasanna Balaprakash:
Graph Neural Network Architecture Search for Molecular Property Prediction. CoRR abs/2008.12187 (2020) - [i14]Tanwi Mallick, Mariam Kiran, Bashir Mohammed, Prasanna Balaprakash:
Dynamic Graph Neural Network for Traffic Forecasting in Wide Area Networks. CoRR abs/2008.12767 (2020) - [i13]Xingfu Wu, Michael Kruse, Prasanna Balaprakash, Hal Finkel, Paul D. Hovland, Valerie E. Taylor, Mary W. Hall:
Autotuning PolyBench Benchmarks with LLVM Clang/Polly Loop Optimization Pragmas Using Bayesian Optimization. CoRR abs/2010.08040 (2020) - [i12]Romain Egele, Prasanna Balaprakash, Venkatram Vishwanath, Isabelle Guyon, Zhengying Liu:
AgEBO-Tabular: Joint Neural Architecture and Hyperparameter Search with Autotuned Data-Parallel Training for Tabular Data. CoRR abs/2010.16358 (2020)
2010 – 2019
- 2019
- [c39]Sunwoo Lee, Qiao Kang, Sandeep Madireddy, Prasanna Balaprakash, Ankit Agrawal, Alok N. Choudhary, Richard Archibald
, Wei-keng Liao:
Improving Scalability of Parallel CNN Training by Adjusting Mini-Batch Size at Run-Time. BigData 2019: 830-839 - [c38]Sandeep Madireddy, Angel Yanguas-Gil, Prasanna Balaprakash:
Neuromorphic Architecture Optimization for Task-Specific Dynamic Learning. ICONS 2019: 5:1-5:5 - [c37]Nathan Wycoff, Prasanna Balaprakash, Fangfang Xia:
Neuromorphic Acceleration for Approximate Bayesian Inference on Neural Networks via Permanent Dropout. ICONS 2019: 9:1-9:4 - [c36]Sandeep Madireddy, Prasanna Balaprakash, Philip H. Carns, Robert Latham, Glenn K. Lockwood, Robert B. Ross, Shane Snyder, Stefan M. Wild:
Adaptive Learning for Concept Drift in Application Performance Modeling. ICPP 2019: 79:1-79:11 - [c35]Vinu Sreenivasan, Rajath Javali, Mary W. Hall, Prasanna Balaprakash, Thomas R. W. Scogland, Bronis R. de Supinski:
A Framework for Enabling OpenMP Autotuning. IWOMP 2019: 50-60 - [c34]Prasanna Balaprakash, Romain Egele, Misha Salim, Stefan M. Wild, Venkatram Vishwanath, Fangfang Xia, Tom Brettin, Rick Stevens:
Scalable reinforcement-learning-based neural architecture search for cancer deep learning research. SC 2019: 37:1-37:33 - [c33]Shashi M. Aithal, Prasanna Balaprakash:
MaLTESE: Large-Scale Simulation-Driven Machine Learning for Transient Driving Cycles. ISC 2019: 186-205 - [i11]Nathan Wycoff, Prasanna Balaprakash, Fangfang Xia:
Neuromorphic Acceleration for Approximate Bayesian Inference on Neural Networks via Permanent Dropout. CoRR abs/1904.12904 (2019) - [i10]Sandeep Madireddy, Angel Yanguas-Gil, Prasanna Balaprakash:
Neuromorphic Architecture Optimization for Task-Specific Dynamic Learning. CoRR abs/1906.01668 (2019) - [i9]Prasanna Balaprakash, Romain Egele, Misha Salim, Stefan M. Wild, Venkatram Vishwanath, Fangfang Xia, Tom Brettin, Rick Stevens:
Scalable Reinforcement-Learning-Based Neural Architecture Search for Cancer Deep Learning Research. CoRR abs/1909.00311 (2019) - [i8]Michael A. Salim, Thomas D. Uram, J. Taylor Childers, Prasanna Balaprakash, Venkatram Vishwanath, Michael E. Papka:
Balsam: Automated Scheduling and Execution of Dynamic, Data-Intensive HPC Workflows. CoRR abs/1909.08704 (2019) - [i7]Romit Maulik, Vishwas Rao, Sandeep Madireddy, Bethany Lusch, Prasanna Balaprakash:
Using recurrent neural networks for nonlinear component computation in advection-dominated reduced-order models. CoRR abs/1909.09144 (2019) - [i6]Shashi M. Aithal, Prasanna Balaprakash:
MaLTESE: Large-Scale Simulation-Driven Machine Learning for Transient Driving Cycles. CoRR abs/1909.09929 (2019) - [i5]Tanwi Mallick, Prasanna Balaprakash, Eric Rask, Jane MacFarlane:
Graph-Partitioning-Based Diffusion Convolution Recurrent Neural Network for Large-Scale Traffic Forecasting. CoRR abs/1909.11197 (2019) - [i4]Sandeep Madireddy, Nan Li, Nesar Ramachandra, Prasanna Balaprakash, Salman Habib:
Modular Deep Learning Analysis of Galaxy-Scale Strong Lensing Images. CoRR abs/1911.03867 (2019) - [i3]Sami Khairy, Ruslan Shaydulin, Lukasz Cincio, Yuri Alexeev, Prasanna Balaprakash:
Reinforcement-Learning-Based Variational Quantum Circuits Optimization for Combinatorial Problems. CoRR abs/1911.04574 (2019) - [i2]Peihong Jiang, Hieu Doan, Sandeep Madireddy, Rajeev Surendran Assary, Prasanna Balaprakash:
Value-Added Chemical Discovery Using Reinforcement Learning. CoRR abs/1911.07630 (2019) - [i1]Sami Khairy, Ruslan Shaydulin, Lukasz Cincio, Yuri Alexeev, Prasanna Balaprakash:
Learning to Optimize Variational Quantum Circuits to Solve Combinatorial Problems. CoRR abs/1911.11071 (2019) - 2018
- [j8]Justin M. Wozniak, Rajeev Jain, Prasanna Balaprakash, Jonathan Ozik, Nicholson T. Collier, John Bauer, Fangfang Xia, Thomas S. Brettin, Rick Stevens, Jamaludin Mohd-Yusof, Cristina Garcia-Cardona, Brian Van Essen, Matthew Baughman:
CANDLE/Supervisor: a workflow framework for machine learning applied to cancer research. BMC Bioinform. 19-S(18): 59-69 (2018) - [j7]Prasanna Balaprakash, Jack J. Dongarra, Todd Gamblin, Mary W. Hall, Jeffrey K. Hollingsworth, Boyana Norris, Richard W. Vuduc:
Autotuning in High-Performance Computing Applications. Proc. IEEE 106(11): 2068-2083 (2018) - [j6]Omer Subasi, Sheng Di, Leonardo Bautista-Gomez, Prasanna Balaprakash, Osman S. Unsal, Jesús Labarta, Adrián Cristal, Sriram Krishnamoorthy, Franck Cappello:
Exploring the capabilities of support vector machines in detecting silent data corruptions. Sustain. Comput. Informatics Syst. 19: 277-290 (2018) - [c32]Sandeep Madireddy, Prasanna Balaprakash, Philip H. Carns, Robert Latham, Robert B. Ross, Shane Snyder, Stefan M. Wild:
Modeling I/O Performance Variability Using Conditional Variational Autoencoders. CLUSTER 2018: 109-113 - [c31]Prasanna Balaprakash, Michael A. Salim, Thomas D. Uram, Venkat Vishwanath, Stefan M. Wild:
DeepHyper: Asynchronous Hyperparameter Search for Deep Neural Networks. HiPC 2018: 42-51 - [c30]Zhengchun Liu, Rajkumar Kettimuthu, Prasanna Balaprakash, Nageswara S. V. Rao, Ian T. Foster:
Building a Wide-Area File Transfer Performance Predictor: An Empirical Study. MLN 2018: 56-78 - [c29]Preeti Malakar, Prasanna Balaprakash, Venkatram Vishwanath, Vitali A. Morozov, Kalyan Kumaran:
Benchmarking Machine Learning Methods for Performance Modeling of Scientific Applications. PMBS@SC 2018: 33-44 - [c28]Sandeep Madireddy, Prasanna Balaprakash, Philip H. Carns, Robert Latham, Robert B. Ross, Shane Snyder, Stefan M. Wild:
Machine Learning Based Parallel I/O Predictive Modeling: A Case Study on Lustre File Systems. ISC 2018: 184-204 - 2017
- [c27]Sudheer Chunduri, Prasanna Balaprakash, Vitali A. Morozov, Venkatram Vishwanath, Kalyan Kumaran:
Analytical Performance Modeling and Validation of Intel's Xeon Phi Architecture. Conf. Computing Frontiers 2017: 247-250 - [c26]Omer Subasi, Sheng Di, Prasanna Balaprakash, Osman S. Unsal, Jesús Labarta, Adrián Cristal, Sriram Krishnamoorthy, Franck Cappello:
MACORD: Online Adaptive Machine Learning Framework for Silent Error Detection. CLUSTER 2017: 717-724 - [c25]Zhengchun Liu, Prasanna Balaprakash, Rajkumar Kettimuthu, Ian T. Foster:
Explaining Wide Area Data Transfer Performance. HPDC 2017: 167-178 - [c24]Sandeep Madireddy, Prasanna Balaprakash, Philip H. Carns, Robert Latham, Robert B. Ross, Shane Snyder, Stefan M. Wild:
Analysis and Correlation of Application I/O Performance and System-Wide I/O Activity. NAS 2017: 1-10 - 2016
- [c23]Omer Subasi, Sheng Di, Leonardo Bautista-Gomez, Prasanna Balaprakash, Osman S. Ünsal, Jesús Labarta, Adrián Cristal, Franck Cappello:
Spatial Support Vector Regression to Detect Silent Errors in the Exascale Era. CCGrid 2016: 413-424 - [c22]Prasanna Balaprakash, Vitali A. Morozov, Rajkumar Kettimuthu, Kalyan Kumaran, Ian T. Foster:
Improving Data Transfer Throughput with Direct Search Optimization. ICPP 2016: 248-257 - [c21]Amit Roy, Prasanna Balaprakash, Paul D. Hovland, Stefan M. Wild:
Exploiting Performance Portability in Search Algorithms for Autotuning. IPDPS Workshops 2016: 1535-1544 - [c20]Prasanna Balaprakash, Ananta Tiwari, Stefan M. Wild, Laura Carrington, Paul D. Hovland:
AutoMOMML: Automatic Multi-objective Modeling with Machine Learning. ISC 2016: 219-239 - 2015
- [j5]Prasanna Balaprakash, Mauro Birattari, Thomas Stützle, Marco Dorigo:
Estimation-based metaheuristics for the single vehicle routing problem with stochastic demands and customers. Comput. Optim. Appl. 61(2): 463-487 (2015) - [c19]Florin Isaila, Prasanna Balaprakash, Stefan M. Wild, Dries Kimpe, Robert Latham, Robert B. Ross, Paul D. Hovland:
Collective I/O Tuning Using Analytical and Machine Learning Models. CLUSTER 2015: 128-137 - [c18]Azamat Mametjanov, Prasanna Balaprakash, Chekuri Choudary, Paul D. Hovland, Stefan M. Wild, Gerald Sabin:
Autotuning FPGA Design Parameters for Performance and Power. FCCM 2015: 84-91 - [c17]Thomas Nelson, Axel Rivera, Prasanna Balaprakash, Mary W. Hall, Paul D. Hovland, Elizabeth R. Jessup, Boyana Norris:
Generating Efficient Tensor Contractions for GPUs. ICPP 2015: 969-978 - 2014
- [c16]Leonardo Arturo Bautista-Gomez, Prasanna Balaprakash, Mohamed-Slim Bouguerra, Stefan M. Wild, Franck Cappello, Paul D. Hovland:
Energy-performance tradeoffs in multilevel checkpoint strategies. CLUSTER 2014: 278-279 - [c15]Prasanna Balaprakash, Leonardo Arturo Bautista-Gomez, Mohamed-Slim Bouguerra, Stefan M. Wild, Franck Cappello, Paul D. Hovland:
Analysis of the Tradeoffs Between Energy and Run Time for Multilevel Checkpointing. PMBS@SC 2014: 249-263 - [c14]Prasanna Balaprakash, Yuri Alexeev, Sheri A. Mickelson, Sven Leyffer, Robert L. Jacob, Anthony P. Craig:
Machine-Learning-Based Load Balancing for Community Ice Code Component in CESM. VECPAR 2014: 79-91 - 2013
- [c13]Prasanna Balaprakash, Robert B. Gramacy, Stefan M. Wild:
Active-learning-based surrogate models for empirical performance tuning. CLUSTER 2013: 1-8 - [c12]Prasanna Balaprakash, Darius Buntinas, Anthony Chan, Apala Guha, Rinku Gupta, Sri Hari Krishna Narayanan, Andrew A. Chien, Paul D. Hovland, Boyana Norris:
Exascale workload characterization and architecture implications. ISPASS 2013: 120-121 - [c11]Prasanna Balaprakash, Karl Rupp, Azamat Mametjanov, Robert B. Gramacy, Paul D. Hovland, Stefan M. Wild:
Empirical performance modeling of GPU kernels using active learning. PARCO 2013: 646-655 - [c10]Prasanna Balaprakash, Ananta Tiwari, Stefan M. Wild:
Multi Objective Optimization of HPC Kernels for Performance, Power, and Energy. PMBS@SC 2013: 239-260 - [c9]Prasanna Balaprakash, Darius Buntinas, Anthony Chan, Apala Guha, Rinku Gupta, Sri Hari Krishna Narayanan, Andrew A. Chien, Paul D. Hovland, Boyana Norris:
Exascale workload characterization and architecture implications. SpringSim (HPC) 2013: 5 - 2012
- [c8]Prasanna Balaprakash, Darius Buntinas, Anthony Chan, Apala Guha, Rinku Gupta, Sri Hari Krishna Narayanan, Andrew A. Chien, Paul D. Hovland, Boyana Norris:
Abstract: An Exascale Workload Study. SC Companion 2012: 1463-1464 - [c7]Prasanna Balaprakash, Darius Buntinas, Anthony Chan, Apala Guha, Rinku Gupta, Sri Hari Krishna Narayanan, Andrew A. Chien, Paul D. Hovland, Boyana Norris:
Poster: An Exascale Workload Study. SC Companion 2012: 1465 - [c6]Prasanna Balaprakash, Stefan M. Wild, Paul D. Hovland:
An Experimental Study of Global and Local Search Algorithms in Empirical Performance Tuning. VECPAR 2012: 261-269 - [c5]Prasanna Balaprakash, Stefan M. Wild, Boyana Norris:
SPAPT: Search Problems in Automatic Performance Tuning. ICCS 2012: 1959-1968 - 2011
- [c4]Prasanna Balaprakash, Stefan M. Wild, Paul D. Hovland:
Can search algorithms save large-scale automatic performance tuning? ICCS 2011: 2136-2145 - 2010
- [j4]Prasanna Balaprakash, Mauro Birattari, Thomas Stützle, Marco Dorigo:
Estimation-based metaheuristics for the probabilistic traveling salesman problem. Comput. Oper. Res. 37(11): 1939-1951 (2010) - [p3]Mauro Birattari, Zhi Yuan, Prasanna Balaprakash, Thomas Stützle:
F-Race and Iterated F-Race: An Overview. Experimental Methods for the Analysis of Optimization Algorithms 2010: 311-336
2000 – 2009
- 2009
- [j3]Prasanna Balaprakash, Mauro Birattari, Thomas Stützle, Marco Dorigo:
Adaptive sample size and importance sampling in estimation-based local search for the probabilistic traveling salesman problem. Eur. J. Oper. Res. 199(1): 98-110 (2009) - [j2]Prasanna Balaprakash, Mauro Birattari, Thomas Stützle, Zhi Yuan, Marco Dorigo:
Estimation-based ant colony optimization and local search for the probabilistic traveling salesman problem. Swarm Intell. 3(3): 223-242 (2009) - 2008
- [j1]Mauro Birattari, Prasanna Balaprakash, Thomas Stützle, Marco Dorigo:
Estimation-Based Local Search for Stochastic Combinatorial Optimization Using Delta Evaluations: A Case Study on the Probabilistic Traveling Salesman Problem. INFORMS J. Comput. 20(4): 644-658 (2008) - [c3]Zhi Yuan, Armin Fügenschuh, Henning Homfeld, Prasanna Balaprakash, Thomas Stützle, Michael Schoch:
Iterated Greedy Algorithms for a Real-World Cyclic Train Scheduling Problem. Hybrid Metaheuristics 2008: 102-116 - [p2]Prasanna Balaprakash, Mauro Birattari, Thomas Stützle:
Engineering Stochastic Local Search Algorithms: A Case Study in Estimation-Based Local Search for the Probabilistic Travelling Salesman Problem. Recent Advances in Evolutionary Computation for Combinatorial Optimization 2008: 53-66 - 2007
- [c2]Prasanna Balaprakash, Mauro Birattari, Thomas Stützle:
Improvement Strategies for the F-Race Algorithm: Sampling Design and Iterative Refinement. Hybrid Metaheuristics 2007: 108-122 - [p1]Mauro Birattari, Prasanna Balaprakash, Marco Dorigo:
The ACO/F-Race Algorithm for Combinatorial Optimization Under Uncertainty. Metaheuristics 2007: 189-203 - 2006
- [c1]Prasanna Balaprakash, Mauro Birattari, Thomas Stützle, Marco Dorigo:
Incremental Local Search in Ant Colony Optimization: Why It Fails for the Quadratic Assignment Problem. ANTS Workshop 2006: 156-166
Coauthor Index
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