Prasanna Balaprakash

According to our database, Prasanna Balaprakash authored at least 144 papers between 2006 and 2024.

Collaborative distances:

• Dijkstra number of three.
• Erdős number of three.

2024

Large language model evaluation for high-performance computing software development.

William F. Godoy, Pedro Valero-Lara, Keita Teranishi, Prasanna Balaprakash, Jeffrey S. Vetter

Concurr. Comput. Pract. Exp., November, 2024

Uncertainty Quantification for Traffic Forecasting Using Deep-Ensemble-Based Spatiotemporal Graph Neural Networks.

Tanwi Mallick, Jane MacFarlane, Prasanna Balaprakash

IEEE Trans. Intell. Transp. Syst., August, 2024

Efficient Mapping Between Void Shapes and Stress Fields Using Deep Convolutional Neural Networks With Sparse Data.

Anindya Bhaduri, Nesar Ramachandra, Sandipp Krishnan Ravi, Lele Luan, Piyush Pandita, Prasanna Balaprakash, Mihai Anitescu, Changjie Sun, Liping Wang

J. Comput. Inf. Sci. Eng., April, 2024

Multi-fidelity reinforcement learning with control variates.

Sami Khairy, Prasanna Balaprakash

Neurocomputing, 2024

The unreasonable effectiveness of early discarding after one epoch in neural network hyperparameter optimization.

Romain Egele, Felix Mohr, Tom J. Viering, Prasanna Balaprakash

Neurocomputing, 2024

Refining computer tomography data with super-resolution networks to increase the accuracy of respiratory flow simulations.

Xin Liu, Mario Rüttgers, Alessio Quercia, Romain Egele, Elisabeth Pfaehler, Rushikesh Shende, Marcel Aach, Wolfgang Schröder, Prasanna Balaprakash, Andreas Lintermann

Future Gener. Comput. Syst., 2024

Generalizable Prediction Model of Molten Salt Mixture Density with Chemistry-Informed Transfer Learning.

Julian Barra, Shayan Shahbazi, Anthony Birri, Rajni Chahal, Ibrahim Isah, Muhammad Nouman Anwar, Tyler Starkus, Prasanna Balaprakash, Stephen Lam

CoRR, 2024

Large Language Models for Anomaly Detection in Computational Workflows: from Supervised Fine-Tuning to In-Context Learning.

Hongwei Jin, George Papadimitriou, Krishnan Raghavan, Pawel Zuk, Prasanna Balaprakash, Cong Wang, Anirban Mandal, Ewa Deelman

CoRR, 2024

Scalable Training of Graph Foundation Models for Atomistic Materials Modeling: A Case Study with HydraGNN.

Massimiliano Lupo Pasini, Jong Youl Choi, Kshitij Mehta, Pei Zhang, David M. Rogers, Jonghyun Bae, Khaled Z. Ibrahim, Ashwin M. Aji, Karl W. Schulz, Jorda Polo, Prasanna Balaprakash

CoRR, 2024

Sequence Length Scaling in Vision Transformers for Scientific Images on Frontier.

Aristeidis Tsaris, Chengming Zhang, Xiao Wang, Junqi Yin, Siyan Liu, Moetasim Ashfaq, Ming Fan, Jong-Youl Choi, Mohamed Wahib, Dan Lu, Prasanna Balaprakash, Fei-Yue Wang

CoRR, 2024

Physics-Informed Heterogeneous Graph Neural Networks for DC Blocker Placement.

Hongwei Jin, Prasanna Balaprakash, Allen Zou, Pieter Ghysels, Aditi S. Krishnapriyan, Adam Mate, Arthur K. Barnes, Russell Bent

CoRR, 2024

Advancing Anomaly Detection in Computational Workflows with Active Learning.

Krishnan Raghavan, George Papadimitriou, Hongwei Jin, Anirban Mandal, Mariam Kiran, Prasanna Balaprakash, Ewa Deelman

CoRR, 2024

ORBIT: Oak Ridge Base Foundation Model for Earth System Predictability.

Xiao Wang, Aristeidis Tsaris, Siyan Liu, Jong-Youl Choi, Ming Fan, Wei Zhang, Junqi Yin, Moetasim Ashfaq, Dan Lu, Prasanna Balaprakash

CoRR, 2024

Network architecture search of X-ray based scientific applications.

Adarsha Balaji, Ramyad Hadidi, Gregory Kollmer, Mohammed E. Fouda, Prasanna Balaprakash

CoRR, 2024

AI Competitions and Benchmarks: Dataset Development.

Romain Egele, Júlio C. S. Jacques Júnior, Jan N. van Rijn, Isabelle Guyon, Xavier Baró, Albert Clapés, Prasanna Balaprakash, Sergio Escalera, Thomas B. Moeslund, Jun Wan

CoRR, 2024

Streamlining Ocean Dynamics Modeling with Fourier Neural Operators: A Multiobjective Hyperparameter and Architecture Optimization Approach.

Yixuan Sun, Olaperi Yeside Sowunmi, Romain Egele, Sri Hari Krishna Narayanan, Luke Van Roekel, Prasanna Balaprakash

CoRR, 2024

Integrating ytopt and libEnsemble to Autotune OpenMC.

Xingfu Wu, John R. Tramm, Jeffrey Larson, John-Luke Navarro, Prasanna Balaprakash, Brice Videau, Michael Kruse, Paul D. Hovland, Valerie Taylor, Mary W. Hall

CoRR, 2024

Optimizing Distributed Training on Frontier for Large Language Models.

Sajal Dash, Isaac Lyngaas, Junqi Yin, Xiao Wang, Romain Egele, J. Austin Ellis, Matthias Maiterth, Guojing Cong, Feiyi Wang, Prasanna Balaprakash

Proceedings of the ISC High Performance 2024 Research Paper Proceedings (39th International Conference), 2024

2023

Graph neural networks for detecting anomalies in scientific workflows.

Hongwei Jin, Krishnan Raghavan, George Papadimitriou, Cong Wang, Anirban Mandal, Mariam Kiran, Ewa Deelman, Prasanna Balaprakash

Int. J. High Perform. Comput. Appl., July, 2023

Multi-fidelity reinforcement learning framework for shape optimization.

Sahil Bhola, Suraj Pawar, Prasanna Balaprakash, Romit Maulik

J. Comput. Phys., June, 2023

Stabilized neural ordinary differential equations for long-time forecasting of dynamical systems.

Alec J. Linot, Joshua W. Burby, Qi Tang, Prasanna Balaprakash, Michael D. Graham, Romit Maulik

J. Comput. Phys., February, 2023

Learning Symbolic Expressions: Mixed-Integer Formulations, Cuts, and Heuristics.

Jongeun Kim, Sven Leyffer, Prasanna Balaprakash

INFORMS J. Comput., 2023

Optimizing Distributed Training on Frontier for Large Language Models.

Sajal Dash, Isaac Lyngaas, Junqi Yin, Xiao Wang, Romain Egele, Guojing Cong, Feiyi Wang, Prasanna Balaprakash

CoRR, 2023

DeepSpeed4Science Initiative: Enabling Large-Scale Scientific Discovery through Sophisticated AI System Technologies.

Shuaiwen Leon Song, Bonnie Kruft, Minjia Zhang, Conglong Li, Shiyang Chen, Chengming Zhang, Masahiro Tanaka, Xiaoxia Wu, Jeff Rasley, Ammar Ahmad Awan, Connor Holmes, Martin Cai, Adam Ghanem, Zhongzhu Zhou, Yuxiong He, Pete Luferenko, Divya Kumar, Jonathan A. Weyn, Ruixiong Zhang, Sylwester Klocek, Volodymyr Vragov, Mohammed AlQuraishi, Gustaf Ahdritz, Christina Floristean, Cristina Negri, Rao Kotamarthi, Venkatram Vishwanath, Arvind Ramanathan, Sam Foreman, Kyle Hippe, Troy Arcomano, Romit Maulik, Maxim Zvyagin, Alexander Brace, Bin Zhang, Cindy Orozco Bohorquez, Austin Clyde, Bharat Kale, Danilo Perez-Rivera, Heng Ma, Carla M. Mann, Michael W. Irvin, J. Gregory Pauloski, Logan T. Ward, Valérie Hayot-Sasson, Murali Emani, Zhen Xie, Diangen Lin, Maulik Shukla, Ian T. Foster, James J. Davis, Michael E. Papka, Thomas S. Brettin, Prasanna Balaprakash, Gina Tourassi, John Gounley, Heidi A. Hanson, Thomas E. Potok, Massimiliano Lupo Pasini, Kate Evans, Dan Lu, Dalton D. Lunga, Junqi Yin, Sajal Dash, Feiyi Wang, Mallikarjun Shankar, Isaac Lyngaas, Xiao Wang, Guojing Cong, Pei Zhang, Ming Fan, Siyan Liu, Adolfy Hoisie, Shinjae Yoo, Yihui Ren, William Tang, Kyle Felker, Alexey Svyatkovskiy, Hang Liu, Ashwin M. Aji, Angela Dalton, Michael J. Schulte, Karl Schulz, Yuntian Deng, Weili Nie, Josh Romero, Christian Dallago, Arash Vahdat, Chaowei Xiao, Thomas Gibbs, Anima Anandkumar, Rick Stevens

CoRR, 2023

Self-supervised Learning for Anomaly Detection in Computational Workflows.

Hongwei Jin, Krishnan Raghavan, George Papadimitriou, Cong Wang, Anirban Mandal, Ewa Deelman, Prasanna Balaprakash

CoRR, 2023

Parallel Multi-Objective Hyperparameter Optimization with Uniform Normalization and Bounded Objectives.

Romain Egele, Tyler Chang, Yixuan Sun, Venkatram Vishwanath, Prasanna Balaprakash

CoRR, 2023

Comparing Llama-2 and GPT-3 LLMs for HPC kernels generation.

Pedro Valero-Lara, Alexis Huante, Mustafa Al-Lail, William F. Godoy, Keita Teranishi, Prasanna Balaprakash, Jeffrey S. Vetter

CoRR, 2023

Uncertainty Quantification for Molecular Property Predictions with Graph Neural Architecture Search.

Shengli Jiang, Shiyi Qin, Reid C. Van Lehn, Prasanna Balaprakash, Victor M. Zavala

CoRR, 2023

Flow-Bench: A Dataset for Computational Workflow Anomaly Detection.

George Papadimitriou, Hongwei Jin, Cong Wang, Krishnan Raghavan, Anirban Mandal, Prasanna Balaprakash, Ewa Deelman

CoRR, 2023

Learning Continually on a Sequence of Graphs - The Dynamical System Way.

Krishnan Raghavan, Prasanna Balaprakash

CoRR, 2023

Application of probabilistic modeling and automated machine learning framework for high-dimensional stress field.

Lele Luan, Nesar Ramachandra, Sandipp Krishnan Ravi, Anindya Bhaduri, Piyush Pandita, Prasanna Balaprakash, Mihai Anitescu, Changjie Sun, Liping Wang

CoRR, 2023

ytopt: Autotuning Scientific Applications for Energy Efficiency at Large Scales.

Xingfu Wu, Prasanna Balaprakash, Michael Kruse, Jaehoon Koo, Brice Videau, Paul D. Hovland, Valerie Taylor, Brad Geltz, Siddhartha Jana, Mary W. Hall

CoRR, 2023

Quantifying uncertainty for deep learning based forecasting and flow-reconstruction using neural architecture search ensembles.

Romit Maulik, Romain Egele, Krishnan Raghavan, Prasanna Balaprakash

CoRR, 2023

Analyzing the impact of climate change on critical infrastructure from the scientific literature: A weakly supervised NLP approach.

Tanwi Mallick, Joshua David Bergerson, Duane R. Verner, John K. Hutchison, Leslie-Anne Levy, Prasanna Balaprakash

CoRR, 2023

Scalable Automated Design and Development of Deep Neural Networks for Scientific and Engineering Applications.

Prasanna Balaprakash

Proceedings of the IEEE International Parallel and Distributed Processing Symposium, 2023

iWAPT2023 Invited Speaker Optimizing HPC Systems for Scientific Applications: Machine Learning Approaches to Performance Tuning and Anomaly Detection.

Prasanna Balaprakash

Proceedings of the IEEE International Parallel and Distributed Processing Symposium, 2023

Transfer-learning-based Autotuning using Gaussian Copula.

Thomas Randall, Jaehoon Koo, Brice Videau, Michael Kruse, Xingfu Wu, Paul D. Hovland, Mary W. Hall, Rong Ge, Prasanna Balaprakash

Proceedings of the 37th International Conference on Supercomputing, 2023

Evaluation of OpenAI Codex for HPC Parallel Programming Models Kernel Generation.

William F. Godoy, Pedro Valero-Lara, Keita Teranishi, Prasanna Balaprakash, Jeffrey S. Vetter

Proceedings of the 52nd International Conference on Parallel Processing Workshops, 2023

Graph Pyramid Autoformer for Long- Term Traffic Forecasting.

Weiheng Zhong, Tanwi Mallick, Jane MacFarlane, Hadi Meidani, Prasanna Balaprakash

Proceedings of the International Conference on Machine Learning and Applications, 2023

Application - Hardware Co-Optimization of Crossbar-Based Neuromorphic Systems.

Adarsha Balaji, Prasanna Balaprakash

Proceedings of the International Conference on Machine Learning and Applications, 2023

Is One Epoch All You Need For Multi-Fidelity Hyperparameter Optimization?

Romain Egele, Isabelle Guyon, Yixuan Sun, Prasanna Balaprakash

Proceedings of the 31st European Symposium on Artificial Neural Networks, 2023

Asynchronous Decentralized Bayesian Optimization for Large Scale Hyperparameter Optimization.

Romain Égelé, Isabelle Guyon, Venkatram Vishwanath, Prasanna Balaprakash

Proceedings of the 19th IEEE International Conference on e-Science, 2023

Improving Performance in Continual Learning Tasks using Bio-Inspired Architectures.

Sandeep Madireddy, Angel Yanguas-Gil, Prasanna Balaprakash

Proceedings of the Conference on Lifelong Learning Agents, 2023

Sparsity-Inducing Categorical Prior Improves Robustness of the Information Bottleneck.

Anirban Samaddar, Sandeep Madireddy, Prasanna Balaprakash, Taps Maiti, Gustavo de los Campos, Ian Fischer

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2023

2022

Efficient data acquisition and training of collisional-radiative model artificial neural network surrogates through adaptive parameter space sampling.

Nathan A Garland, Romit Maulik, Qi Tang, Xian-Zhu Tang, Prasanna Balaprakash

Mach. Learn. Sci. Technol., December, 2022

Data-Driven Random Access Optimization in Multi-Cell IoT Networks Using NOMA.

Sami Khairy, Prasanna Balaprakash, Lin X. Cai, H. Vincent Poor

IEEE Trans. Wirel. Commun., 2022

Unified Probabilistic Neural Architecture and Weight Ensembling Improves Model Robustness.

Sumegha Premchandar, Sandeep Madireddy, Sanket R. Jantre, Prasanna Balaprakash

CoRR, 2022

Explainable Graph Pyramid Autoformer for Long-Term Traffic Forecasting.

Weiheng Zhong, Tanwi Mallick, Hadi Meidani, Jane MacFarlane, Prasanna Balaprakash

CoRR, 2022

Asynchronous Distributed Bayesian Optimization at HPC Scale.

Romain Egele, Joceran Gouneau, Venkatram Vishwanath, Isabelle Guyon, Prasanna Balaprakash

CoRR, 2022

Multifidelity Reinforcement Learning with Control Variates.

Sami Khairy, Prasanna Balaprakash

CoRR, 2022

Sequential Bayesian Neural Subnetwork Ensembles.

Sanket R. Jantre, Sandeep Madireddy, Shrijita Bhattacharya, Tapabrata Maiti, Prasanna Balaprakash

CoRR, 2022

Deep-Ensemble-Based Uncertainty Quantification in Spatiotemporal Graph Neural Networks for Traffic Forecasting.

Tanwi Mallick, Prasanna Balaprakash, Jane MacFarlane

CoRR, 2022

Sparsity-Inducing Categorical Prior Improves Robustness of the Information Bottleneck.

Anirban Samaddar, Sandeep Madireddy, Prasanna Balaprakash

CoRR, 2022

Autotuning PolyBench benchmarks with LLVM Clang/Polly loop optimization pragmas using Bayesian optimization.

Xingfu Wu, Michael Kruse, Prasanna Balaprakash, Hal Finkel, Paul D. Hovland, Valerie Taylor, Mary W. Hall

Concurr. Comput. Pract. Exp., 2022

Modeling design and control problems involving neural network surrogates.

Dominic Yang, Prasanna Balaprakash, Sven Leyffer

Comput. Optim. Appl., 2022

A cross-study analysis of drug response prediction in cancer cell lines.

Fangfang Xia, Jonathan E. Allen, Prasanna Balaprakash, Thomas S. Brettin, Cristina Garcia-Cardona, Austin Clyde, Judith D. Cohn, James H. Doroshow, Xiaotian Duan, Veronika Dubinkina, Yvonne A. Evrard, Ya Ju Fan, Jason Gans, Stewart He, Pinyi Lu, Sergei Maslov, Alexander Partin, Maulik Shukla, Eric A. Stahlberg, Justin M. Wozniak, Hyun Seung Yoo, George F. Zaki, Yitan Zhu, Rick Stevens

Briefings Bioinform., 2022

Workflow Anomaly Detection with Graph Neural Networks.

Hongwei Jin, Krishnan Raghavan, George Papadimitriou, Cong Wang, Anirban Mandal, Patrycja Krawczuk, Loïc Pottier, Mariam Kiran, Ewa Deelman, Prasanna Balaprakash

Proceedings of the IEEE/ACM Workshop on Workflows in Support of Large-Scale Science, 2022

Automated Continual Learning of Defect Identification in Coherent Diffraction Imaging.

Orcun Yildiz, Henry Chan, Krishnan Raghavan, William Judge, Mathew J. Cherukara, Prasanna Balaprakash, Subramanian Sankaranarayanan, Tom Peterka

Proceedings of the IEEE/ACM International Workshop on Artificial Intelligence and Machine Learning for Scientific Applications, 2022

A Taxonomy of Error Sources in HPC I/O Machine Learning Models.

Mihailo Isakov, Mikaela Currier, Eliakin Del Rosario, Sandeep Madireddy, Prasanna Balaprakash, Philip H. Carns, Robert B. Ross, Glenn K. Lockwood, Michel A. Kinsy

Proceedings of the SC22: International Conference for High Performance Computing, 2022

Continual Learning via Dynamic Programming.

R. Krishnan, Prasanna Balaprakash

Proceedings of the 26th International Conference on Pattern Recognition, 2022

AutoDEUQ: Automated Deep Ensemble with Uncertainty Quantification.

Romain Egele, Romit Maulik, Krishnan Raghavan, Bethany Lusch, Isabelle Guyon, Prasanna Balaprakash

Proceedings of the 26th International Conference on Pattern Recognition, 2022

HPC Storage Service Autotuning Using Variational- Autoencoder -Guided Asynchronous Bayesian Optimization.

Matthieu Dorier, Romain Egele, Prasanna Balaprakash, Jaehoon Koo, Sandeep Madireddy, Srinivasan Ramesh, Allen D. Malony, Robert B. Ross

Proceedings of the IEEE International Conference on Cluster Computing, 2022

2021

irace.

Manuel López-Ibáñez, Leslie Pérez Cáceres, Jérémie Dubois-Lacoste, Thomas Stützle, Mauro Birattari, Zhi Yuan, Prasanna Balaprakash

Dataset, June, 2021

In situ compression artifact removal in scientific data using deep transfer learning and experience replay.

Sandeep Madireddy, Ji Hwan Park, Sunwoo Lee, Prasanna Balaprakash, Shinjae Yoo, Wei-keng Liao, Cory D. Hauck, M. Paul Laiu, Richard Archibald

Mach. Learn. Sci. Technol., 2021

Constrained Deep Reinforcement Learning for Energy Sustainable Multi-UAV Based Random Access IoT Networks With NOMA.

Sami Khairy, Prasanna Balaprakash, Lin X. Cai, Yu Cheng

IEEE J. Sel. Areas Commun., 2021

A data-centric weak supervised learning for highway traffic incident detection.

Yixuan Sun, Tanwi Mallick, Prasanna Balaprakash, Jane MacFarlane

CoRR, 2021

AutoDEUQ: Automated Deep Ensemble with Uncertainty Quantification.

Romain Egele, Romit Maulik, Krishnan Raghavan, Prasanna Balaprakash, Bethany Lusch

CoRR, 2021

AutoPhaseNN: Unsupervised Physics-aware Deep Learning of 3D Nanoscale Coherent Imaging.

Yudong Yao, Henry Chan, Subramanian Sankaranarayanan, Prasanna Balaprakash, Ross J. Harder, Mathew J. Cherukara

CoRR, 2021

Autotuning PolyBench Benchmarks with LLVM Clang/Polly Loop Optimization Pragmas Using Bayesian Optimization (extended version).

Xingfu Wu, Michael Kruse, Prasanna Balaprakash, Hal Finkel, Paul D. Hovland, Valerie Taylor, Mary W. Hall

CoRR, 2021

Learning-Based Distributed Random Access for Multi-Cell IoT Networks with NOMA.

Sami Khairy, Prasanna Balaprakash, Lin X. Cai, H. Vincent Poor

CoRR, 2021

AgEBO-tabular: joint neural architecture and hyperparameter search with autotuned data-parallel training for tabular data.

Romain Égelé, Prasanna Balaprakash, Isabelle Guyon, Venkatram Vishwanath, Fangfang Xia, Rick Stevens, Zhengying Liu

Proceedings of the International Conference for High Performance Computing, 2021

Customized Monte Carlo Tree Search for LLVM/Polly's Composable Loop Optimization Transformations.

Jaehoon Koo, Prasanna Balaprakash, Michael Kruse, Xingfu Wu, Paul D. Hovland, Mary W. Hall

Proceedings of the 2021 International Workshop on Performance Modeling, 2021

Formalizing the Generalization-Forgetting Trade-off in Continual Learning.

Krishnan Raghavan, Prasanna Balaprakash

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Learning to Switch Optimizers for Quadratic Programming.

Grant Getzelman, Prasanna Balaprakash

Proceedings of the Asian Conference on Machine Learning, 2021

2020

AgEBO-Tabular: Joint Neural Architecture and Hyperparameter Search with Autotuned Data-Parallel Training for Tabular Data.

Romain Egele, Prasanna Balaprakash, Venkatram Vishwanath, Isabelle Guyon, Zhengying Liu

CoRR, 2020

Dynamic Graph Neural Network for Traffic Forecasting in Wide Area Networks.

Tanwi Mallick, Mariam Kiran, Bashir Mohammed, Prasanna Balaprakash

CoRR, 2020

Meta Continual Learning via Dynamic Programming.

R. Krishnan, Prasanna Balaprakash

CoRR, 2020

Multilayer Neuromodulated Architectures for Memory-Constrained Online Continual Learning.

Sandeep Madireddy, Angel Yanguas-Gil, Prasanna Balaprakash

CoRR, 2020

Towards On-Chip Bayesian Neuromorphic Learning.

Nathan Wycoff, Prasanna Balaprakash, Fangfang Xia

CoRR, 2020

A Gradient-Aware Search Algorithm for Constrained Markov Decision Processes.

Sami Khairy, Prasanna Balaprakash, Lin X. Cai

CoRR, 2020

Site-specific graph neural network for predicting protonation energy of oxygenate molecules.

Romit Maulik, Rajeev Surendran Array, Prasanna Balaprakash

CoRR, 2020

Gauge: An Interactive Data-Driven Visualization Tool for HPC Application I/O Performance Analysis.

Eliakin Del Rosario, Mikaela Currier, Mihailo Isakov, Sandeep Madireddy, Prasanna Balaprakash, Philip H. Carns, Robert B. Ross, Kevin Harms, Shane Snyder, Michel A. Kinsy

Proceedings of the Fifth IEEE/ACM International Parallel Data Systems Workshop, 2020

Recurrent neural network architecture search for geophysical emulation.

Romit Maulik, Romain Egele, Bethany Lusch, Prasanna Balaprakash

Proceedings of the International Conference for High Performance Computing, 2020

HPC I/O throughput bottleneck analysis with explainable local models.

Mihailo Isakov, Eliakin Del Rosario, Sandeep Madireddy, Prasanna Balaprakash, Philip H. Carns, Robert B. Ross, Michel A. Kinsy

Proceedings of the International Conference for High Performance Computing, 2020

Toward Generalizable Models of I/O Throughput.

Mihailo Isakov, Eliakin Del Rosario, Sandeep Madireddy, Prasanna Balaprakash, Philip H. Carns, Robert B. Ross, Michel A. Kinsy

Proceedings of the 2020 IEEE/ACM International Workshop on Runtime and Operating Systems for Supercomputers, 2020

FIdelity: Efficient Resilience Analysis Framework for Deep Learning Accelerators.

Yi He, Prasanna Balaprakash, Yanjing Li

Proceedings of the 53rd Annual IEEE/ACM International Symposium on Microarchitecture, 2020

Transfer Learning with Graph Neural Networks for Short-Term Highway Traffic Forecasting.

Tanwi Mallick, Prasanna Balaprakash, Eric Rask, Jane MacFarlane

Proceedings of the 25th International Conference on Pattern Recognition, 2020

Graph Neural Network Architecture Search for Molecular Property Prediction.

Shengli Jiang, Prasanna Balaprakash

Proceedings of the 2020 IEEE International Conference on Big Data (IEEE BigData 2020), 2020

Learning to Optimize Variational Quantum Circuits to Solve Combinatorial Problems.

Sami Khairy, Ruslan Shaydulin, Lukasz Cincio, Yuri Alexeev, Prasanna Balaprakash

Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence, 2020

2019

Value-Added Chemical Discovery Using Reinforcement Learning.

Peihong Jiang, Hieu Doan, Sandeep Madireddy, Rajeev Surendran Assary, Prasanna Balaprakash

CoRR, 2019

Reinforcement-Learning-Based Variational Quantum Circuits Optimization for Combinatorial Problems.

Sami Khairy, Ruslan Shaydulin, Lukasz Cincio, Yuri Alexeev, Prasanna Balaprakash

CoRR, 2019

Modular Deep Learning Analysis of Galaxy-Scale Strong Lensing Images.

Sandeep Madireddy, Nan Li, Nesar Ramachandra, Prasanna Balaprakash, Salman Habib

CoRR, 2019

Graph-Partitioning-Based Diffusion Convolution Recurrent Neural Network for Large-Scale Traffic Forecasting.

Tanwi Mallick, Prasanna Balaprakash, Eric Rask, Jane MacFarlane

CoRR, 2019

Using recurrent neural networks for nonlinear component computation in advection-dominated reduced-order models.

Romit Maulik, Vishwas Rao, Sandeep Madireddy, Bethany Lusch, Prasanna Balaprakash

CoRR, 2019

Balsam: Automated Scheduling and Execution of Dynamic, Data-Intensive HPC Workflows.

Michael A. Salim, Thomas D. Uram, J. Taylor Childers, Prasanna Balaprakash, Venkatram Vishwanath, Michael E. Papka

CoRR, 2019

MaLTESE: Large-Scale Simulation-Driven Machine Learning for Transient Driving Cycles.

Shashi M. Aithal, Prasanna Balaprakash

Proceedings of the High Performance Computing - 34th International Conference, 2019

Scalable reinforcement-learning-based neural architecture search for cancer deep learning research.

Prasanna Balaprakash, Romain Egele, Misha Salim, Stefan M. Wild, Venkatram Vishwanath, Fangfang Xia, Tom Brettin, Rick Stevens

Proceedings of the International Conference for High Performance Computing, 2019

A Framework for Enabling OpenMP Autotuning.

Vinu Sreenivasan, Rajath Javali, Mary W. Hall, Prasanna Balaprakash, Thomas R. W. Scogland, Bronis R. de Supinski

Proceedings of the OpenMP: Conquering the Full Hardware Spectrum, 2019

Adaptive Learning for Concept Drift in Application Performance Modeling.

Sandeep Madireddy, Prasanna Balaprakash, Philip H. Carns, Robert Latham, Glenn K. Lockwood, Robert B. Ross, Shane Snyder, Stefan M. Wild

Proceedings of the 48th International Conference on Parallel Processing, 2019

Neuromorphic Acceleration for Approximate Bayesian Inference on Neural Networks via Permanent Dropout.

Nathan Wycoff, Prasanna Balaprakash, Fangfang Xia

Proceedings of the International Conference on Neuromorphic Systems, 2019

Neuromorphic Architecture Optimization for Task-Specific Dynamic Learning.

Sandeep Madireddy, Angel Yanguas-Gil, Prasanna Balaprakash

Proceedings of the International Conference on Neuromorphic Systems, 2019

Improving Scalability of Parallel CNN Training by Adjusting Mini-Batch Size at Run-Time.

Sunwoo Lee, Qiao Kang, Sandeep Madireddy, Prasanna Balaprakash, Ankit Agrawal, Alok N. Choudhary, Richard Archibald, Wei-keng Liao

Proceedings of the 2019 IEEE International Conference on Big Data (IEEE BigData), 2019

2018

Exploring the capabilities of support vector machines in detecting silent data corruptions.

Omer Subasi, Sheng Di, Leonardo Bautista-Gomez, Prasanna Balaprakash, Osman S. Unsal, Jesús Labarta, Adrián Cristal, Sriram Krishnamoorthy, Franck Cappello

Sustain. Comput. Informatics Syst., 2018

Autotuning in High-Performance Computing Applications.

Prasanna Balaprakash, Jack J. Dongarra, Todd Gamblin, Mary W. Hall, Jeffrey K. Hollingsworth, Boyana Norris, Richard W. Vuduc

Proc. IEEE, 2018

CANDLE/Supervisor: a workflow framework for machine learning applied to cancer research.

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

BMC Bioinform., 2018

Machine Learning Based Parallel I/O Predictive Modeling: A Case Study on Lustre File Systems.

Sandeep Madireddy, Prasanna Balaprakash, Philip H. Carns, Robert Latham, Robert B. Ross, Shane Snyder, Stefan M. Wild

Proceedings of the High Performance Computing - 33rd International Conference, 2018

Benchmarking Machine Learning Methods for Performance Modeling of Scientific Applications.

Preeti Malakar, Prasanna Balaprakash, Venkatram Vishwanath, Vitali A. Morozov, Kalyan Kumaran

Proceedings of the 2018 IEEE/ACM Performance Modeling, 2018

Building a Wide-Area File Transfer Performance Predictor: An Empirical Study.

Zhengchun Liu, Rajkumar Kettimuthu, Prasanna Balaprakash, Nageswara S. V. Rao, Ian T. Foster

Proceedings of the Machine Learning for Networking - First International Conference, 2018

DeepHyper: Asynchronous Hyperparameter Search for Deep Neural Networks.

Prasanna Balaprakash, Michael Salim, Thomas D. Uram, Venkat Vishwanath, Stefan M. Wild

Proceedings of the 25th IEEE International Conference on High Performance Computing, 2018

Modeling I/O Performance Variability Using Conditional Variational Autoencoders.

Sandeep Madireddy, Prasanna Balaprakash, Philip H. Carns, Robert Latham, Robert B. Ross, Shane Snyder, Stefan M. Wild

Proceedings of the IEEE International Conference on Cluster Computing, 2018

2017

Analysis and Correlation of Application I/O Performance and System-Wide I/O Activity.

Sandeep Madireddy, Prasanna Balaprakash, Philip H. Carns, Robert Latham, Robert B. Ross, Shane Snyder, Stefan M. Wild

Proceedings of the 2017 International Conference on Networking, Architecture, and Storage, 2017

Explaining Wide Area Data Transfer Performance.

Zhengchun Liu, Prasanna Balaprakash, Rajkumar Kettimuthu, Ian T. Foster

Proceedings of the 26th International Symposium on High-Performance Parallel and Distributed Computing, 2017

MACORD: Online Adaptive Machine Learning Framework for Silent Error Detection.

Omer Subasi, Sheng Di, Prasanna Balaprakash, Osman S. Unsal, Jesús Labarta, Adrián Cristal, Sriram Krishnamoorthy, Franck Cappello

Proceedings of the 2017 IEEE International Conference on Cluster Computing, 2017

Analytical Performance Modeling and Validation of Intel's Xeon Phi Architecture.

Sudheer Chunduri, Prasanna Balaprakash, Vitali A. Morozov, Venkatram Vishwanath, Kalyan Kumaran

Proceedings of the Computing Frontiers Conference, 2017

2016

AutoMOMML: Automatic Multi-objective Modeling with Machine Learning.

Prasanna Balaprakash, Ananta Tiwari, Stefan M. Wild, Laura Carrington, Paul D. Hovland

Proceedings of the High Performance Computing - 31st International Conference, 2016

Exploiting Performance Portability in Search Algorithms for Autotuning.

Amit Roy, Prasanna Balaprakash, Paul D. Hovland, Stefan M. Wild

Proceedings of the 2016 IEEE International Parallel and Distributed Processing Symposium Workshops, 2016

Improving Data Transfer Throughput with Direct Search Optimization.

Prasanna Balaprakash, Vitali A. Morozov, Rajkumar Kettimuthu, Kalyan Kumaran, Ian T. Foster

Proceedings of the 45th International Conference on Parallel Processing, 2016

Spatial Support Vector Regression to Detect Silent Errors in the Exascale Era.

Omer Subasi, Sheng Di, Leonardo Bautista-Gomez, Prasanna Balaprakash, Osman S. Ünsal, Jesús Labarta, Adrián Cristal, Franck Cappello

Proceedings of the IEEE/ACM 16th International Symposium on Cluster, 2016

2015

Estimation-based metaheuristics for the single vehicle routing problem with stochastic demands and customers.

Prasanna Balaprakash, Mauro Birattari, Thomas Stützle, Marco Dorigo

Comput. Optim. Appl., 2015

Generating Efficient Tensor Contractions for GPUs.

Thomas Nelson, Axel Rivera, Prasanna Balaprakash, Mary W. Hall, Paul D. Hovland, Elizabeth R. Jessup, Boyana Norris

Proceedings of the 44th International Conference on Parallel Processing, 2015

Autotuning FPGA Design Parameters for Performance and Power.

Azamat Mametjanov, Prasanna Balaprakash, Chekuri Choudary, Paul D. Hovland, Stefan M. Wild, Gerald Sabin

Proceedings of the 23rd IEEE Annual International Symposium on Field-Programmable Custom Computing Machines, 2015

Collective I/O Tuning Using Analytical and Machine Learning Models.

Florin Isaila, Prasanna Balaprakash, Stefan M. Wild, Dries Kimpe, Robert Latham, Robert B. Ross, Paul D. Hovland

Proceedings of the 2015 IEEE International Conference on Cluster Computing, 2015

2014

Machine-Learning-Based Load Balancing for Community Ice Code Component in CESM.

Prasanna Balaprakash, Yuri Alexeev, Sheri A. Mickelson, Sven Leyffer, Robert L. Jacob, Anthony P. Craig

Proceedings of the High Performance Computing for Computational Science - VECPAR 2014 - 11th International Conference, Eugene, OR, USA, June 30, 2014

Analysis of the Tradeoffs Between Energy and Run Time for Multilevel Checkpointing.

Prasanna Balaprakash, Leonardo Arturo Bautista-Gomez, Mohamed-Slim Bouguerra, Stefan M. Wild, Franck Cappello, Paul D. Hovland

Proceedings of the High Performance Computing Systems. Performance Modeling, Benchmarking, and Simulation, 2014

Energy-performance tradeoffs in multilevel checkpoint strategies.

Leonardo Arturo Bautista-Gomez, Prasanna Balaprakash, Mohamed-Slim Bouguerra, Stefan M. Wild, Franck Cappello, Paul D. Hovland

Proceedings of the 2014 IEEE International Conference on Cluster Computing, 2014

2013

Multi Objective Optimization of HPC Kernels for Performance, Power, and Energy.

Prasanna Balaprakash, Ananta Tiwari, Stefan M. Wild

Proceedings of the High Performance Computing Systems. Performance Modeling, Benchmarking and Simulation, 2013

Empirical performance modeling of GPU kernels using active learning.

Prasanna Balaprakash, Karl Rupp, Azamat Mametjanov, Robert B. Gramacy, Paul D. Hovland, Stefan M. Wild

Proceedings of the Parallel Computing: Accelerating Computational Science and Engineering (CSE), 2013

Exascale workload characterization and architecture implications.

Prasanna Balaprakash, Darius Buntinas, Anthony Chan, Apala Guha, Rinku Gupta, Sri Hari Krishna Narayanan, Andrew A. Chien, Paul D. Hovland, Boyana Norris

Proceedings of the 2012 IEEE International Symposium on Performance Analysis of Systems & Software, 2013

Active-learning-based surrogate models for empirical performance tuning.

Prasanna Balaprakash, Robert B. Gramacy, Stefan M. Wild

Proceedings of the 2013 IEEE International Conference on Cluster Computing, 2013

2012

SPAPT: Search Problems in Automatic Performance Tuning.

Prasanna Balaprakash, Stefan M. Wild, Boyana Norris

Proceedings of the International Conference on Computational Science, 2012

An Experimental Study of Global and Local Search Algorithms in Empirical Performance Tuning.

Prasanna Balaprakash, Stefan M. Wild, Paul D. Hovland

Proceedings of the High Performance Computing for Computational Science, 2012

Poster: An Exascale Workload Study.

Prasanna Balaprakash, Darius Buntinas, Anthony Chan, Apala Guha, Rinku Gupta, Sri Hari Krishna Narayanan, Andrew A. Chien, Paul D. Hovland, Boyana Norris

Proceedings of the 2012 SC Companion: High Performance Computing, 2012

Abstract: An Exascale Workload Study.

Prasanna Balaprakash, Darius Buntinas, Anthony Chan, Apala Guha, Rinku Gupta, Sri Hari Krishna Narayanan, Andrew A. Chien, Paul D. Hovland, Boyana Norris

Proceedings of the 2012 SC Companion: High Performance Computing, 2012

2011

Can search algorithms save large-scale automatic performance tuning?

Prasanna Balaprakash, Stefan M. Wild, Paul D. Hovland

Proceedings of the International Conference on Computational Science, 2011

2010

Estimation-based metaheuristics for the probabilistic traveling salesman problem.

Prasanna Balaprakash, Mauro Birattari, Thomas Stützle, Marco Dorigo

Comput. Oper. Res., 2010

F-Race and Iterated F-Race: An Overview.

Mauro Birattari, Zhi Yuan, Prasanna Balaprakash, Thomas Stützle

Proceedings of the Experimental Methods for the Analysis of Optimization Algorithms., 2010

2009

Estimation-based ant colony optimization and local search for the probabilistic traveling salesman problem.

Prasanna Balaprakash, Mauro Birattari, Thomas Stützle, Zhi Yuan, Marco Dorigo

Swarm Intell., 2009

Adaptive sample size and importance sampling in estimation-based local search for the probabilistic traveling salesman problem.

Prasanna Balaprakash, Mauro Birattari, Thomas Stützle, Marco Dorigo

Eur. J. Oper. Res., 2009

2008

Engineering Stochastic Local Search Algorithms: A Case Study in Estimation-Based Local Search for the Probabilistic Travelling Salesman Problem.

Prasanna Balaprakash, Mauro Birattari, Thomas Stützle

Proceedings of the Recent Advances in Evolutionary Computation for Combinatorial Optimization, 2008

Estimation-Based Local Search for Stochastic Combinatorial Optimization Using Delta Evaluations: A Case Study on the Probabilistic Traveling Salesman Problem.

Mauro Birattari, Prasanna Balaprakash, Thomas Stützle, Marco Dorigo

INFORMS J. Comput., 2008

Iterated Greedy Algorithms for a Real-World Cyclic Train Scheduling Problem.

Zhi Yuan, Armin Fügenschuh, Henning Homfeld, Prasanna Balaprakash, Thomas Stützle, Michael Schoch

Proceedings of the Hybrid Metaheuristics, 5th International Workshop, 2008

2007

Improvement Strategies for the F-Race Algorithm: Sampling Design and Iterative Refinement.

Prasanna Balaprakash, Mauro Birattari, Thomas Stützle

Proceedings of the Hybrid Metaheuristics, 4th International Workshop, 2007

The ACO/F-Race Algorithm for Combinatorial Optimization Under Uncertainty.

Mauro Birattari, Prasanna Balaprakash, Marco Dorigo

Proceedings of the Metaheuristics, 2007

2006

Incremental Local Search in Ant Colony Optimization: Why It Fails for the Quadratic Assignment Problem.

Prasanna Balaprakash, Mauro Birattari, Thomas Stützle, Marco Dorigo

Proceedings of the Ant Colony Optimization and Swarm Intelligence, 2006