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2020 – today
- 2024
[j25]William F. Godoy, Pedro Valero-Lara, Keita Teranishi, Prasanna Balaprakash, Jeffrey S. Vetter:
Large language model evaluation for high-performance computing software development. Concurr. Comput. Pract. Exp. 36(26) (2024)- [j24]Xin Liu
, Mario Rüttgers, Alessio Quercia, Romain Egele, Elisabeth Pfaehler, Rushikesh Shende, Marcel Aach, Wolfgang Schröder, Prasanna Balaprakash, Andreas Lintermann:
Refining computer tomography data with super-resolution networks to increase the accuracy of respiratory flow simulations. Future Gener. Comput. Syst. 159: 474-488 (2024) - [j23]Sami Khairy, Prasanna Balaprakash:
Multi-fidelity reinforcement learning with control variates. Neurocomputing 597: 127963 (2024) - [j22]Romain Egele, Felix Mohr, Tom J. Viering, Prasanna Balaprakash:
The unreasonable effectiveness of early discarding after one epoch in neural network hyperparameter optimization. Neurocomputing 597: 127964 (2024) - [j21]Anindya Bhaduri, Nesar Ramachandra, Sandipp Krishnan Ravi, Lele Luan, Piyush Pandita, Prasanna Balaprakash, Mihai Anitescu, Changjie Sun, Liping Wang:
Efficient Mapping Between Void Shapes and Stress Fields Using Deep Convolutional Neural Networks With Sparse Data. J. Comput. Inf. Sci. Eng. 24(4) (2024) - [j20]Tanwi Mallick, Jane MacFarlane, Prasanna Balaprakash:
Uncertainty Quantification for Traffic Forecasting Using Deep-Ensemble-Based Spatiotemporal Graph Neural Networks. IEEE Trans. Intell. Transp. Syst. 25(8): 9141-9152 (2024) - [c70]Sajal Dash, Isaac Lyngaas, Junqi Yin, Xiao Wang, Romain Egele, J. Austin Ellis, Matthias Maiterth, Guojing Cong, Feiyi Wang, Prasanna Balaprakash:
Optimizing Distributed Training on Frontier for Large Language Models. ISC 2024: 1-11 - [i69]Thomas Randall, Jaehoon Koo, Brice Videau, Michael Kruse, Xingfu Wu, Paul D. Hovland, Mary W. Hall, Rong Ge, Prasanna Balaprakash:
Transfer-Learning-Based Autotuning Using Gaussian Copula. CoRR abs/2401.04669 (2024) - [i68]Xingfu Wu, John R. Tramm, Jeffrey Larson, John-Luke Navarro, Prasanna Balaprakash, Brice Videau, Michael Kruse, Paul D. Hovland, Valerie Taylor, Mary W. Hall:
Integrating ytopt and libEnsemble to Autotune OpenMC. CoRR abs/2402.09222 (2024) - [i67]Romain Egele, Felix Mohr, Tom J. Viering, Prasanna Balaprakash:
The Unreasonable Effectiveness Of Early Discarding After One Epoch In Neural Network Hyperparameter Optimization. CoRR abs/2404.04111 (2024) - [i66]Yixuan Sun, Olaperi Yeside Sowunmi, Romain Egele, Sri Hari Krishna Narayanan, Luke Van Roekel, Prasanna Balaprakash:
Streamlining Ocean Dynamics Modeling with Fourier Neural Operators: A Multiobjective Hyperparameter and Architecture Optimization Approach. CoRR abs/2404.05768 (2024) - [i65]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:
AI Competitions and Benchmarks: Dataset Development. CoRR abs/2404.09703 (2024) - [i64]Adarsha Balaji, Ramyad Hadidi, Gregory Kollmer, Mohammed E. Fouda, Prasanna Balaprakash:
Network architecture search of X-ray based scientific applications. CoRR abs/2404.10689 (2024) - [i63]Xiao Wang, Aristeidis Tsaris, Siyan Liu, Jong-Youl Choi, Ming Fan, Wei Zhang, Junqi Yin, Moetasim Ashfaq, Dan Lu, Prasanna Balaprakash:
ORBIT: Oak Ridge Base Foundation Model for Earth System Predictability. CoRR abs/2404.14712 (2024) - [i62]Krishnan Raghavan, George Papadimitriou, Hongwei Jin, Anirban Mandal, Mariam Kiran, Prasanna Balaprakash, Ewa Deelman:
Advancing Anomaly Detection in Computational Workflows with Active Learning. CoRR abs/2405.06133 (2024) - [i61]Hongwei Jin, Prasanna Balaprakash, Allen Zou, Pieter Ghysels, Aditi S. Krishnapriyan, Adam Mate, Arthur K. Barnes, Russell Bent:
Physics-Informed Heterogeneous Graph Neural Networks for DC Blocker Placement. CoRR abs/2405.10389 (2024) - [i60]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:
Sequence Length Scaling in Vision Transformers for Scientific Images on Frontier. CoRR abs/2405.15780 (2024) - [i59]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:
Scalable Training of Graph Foundation Models for Atomistic Materials Modeling: A Case Study with HydraGNN. CoRR abs/2406.12909 (2024) - [i58]Hongwei Jin, George Papadimitriou, Krishnan Raghavan, Pawel Zuk, Prasanna Balaprakash, Cong Wang, Anirban Mandal, Ewa Deelman:
Large Language Models for Anomaly Detection in Computational Workflows: from Supervised Fine-Tuning to In-Context Learning. CoRR abs/2407.17545 (2024) - 2023
- [j19]Hongwei Jin, Krishnan Raghavan, George Papadimitriou, Cong Wang, Anirban Mandal, Mariam Kiran, Ewa Deelman, Prasanna Balaprakash:
Graph neural networks for detecting anomalies in scientific workflows. Int. J. High Perform. Comput. Appl. 37(3-4): 394-411 (2023) - [j18]Jongeun Kim, Sven Leyffer, Prasanna Balaprakash:
Learning Symbolic Expressions: Mixed-Integer Formulations, Cuts, and Heuristics. INFORMS J. Comput. 35(6): 1383-1403 (2023) - [j17]Alec J. Linot, Joshua W. Burby, Qi Tang, Prasanna Balaprakash, Michael D. Graham, Romit Maulik:
Stabilized neural ordinary differential equations for long-time forecasting of dynamical systems. J. Comput. Phys. 474: 111838 (2023) - [j16]Sahil Bhola, Suraj Pawar, Prasanna Balaprakash, Romit Maulik:
Multi-fidelity reinforcement learning framework for shape optimization. J. Comput. Phys. 482: 112018 (2023) - [c69]Anirban Samaddar, Sandeep Madireddy, Prasanna Balaprakash, Taps Maiti, Gustavo de los Campos, Ian Fischer:
Sparsity-Inducing Categorical Prior Improves Robustness of the Information Bottleneck. AISTATS 2023: 10207-10222 - [c68]Sandeep Madireddy, Angel Yanguas-Gil, Prasanna Balaprakash:
Improving Performance in Continual Learning Tasks using Bio-Inspired Architectures. CoLLAs 2023: 992-1008 - [c67]Romain Égelé, Isabelle Guyon, Venkatram Vishwanath, Prasanna Balaprakash:
Asynchronous Decentralized Bayesian Optimization for Large Scale Hyperparameter Optimization. e-Science 2023: 1-10 - [c66]Romain Egele, Isabelle Guyon, Yixuan Sun, Prasanna Balaprakash:
Is One Epoch All You Need For Multi-Fidelity Hyperparameter Optimization? ESANN 2023 - [c65]Sami Khairy, Prasanna Balaprakash:
Multi-Fidelity Reinforcement Learning with Control Variates. ESANN 2023 - [c64]Weiheng Zhong, Tanwi Mallick, Jane MacFarlane, Hadi Meidani, Prasanna Balaprakash:
Graph Pyramid Autoformer for Long- Term Traffic Forecasting. ICMLA 2023: 384-391 - [c63]Adarsha Balaji, Prasanna Balaprakash:
Application - Hardware Co-Optimization of Crossbar-Based Neuromorphic Systems. ICMLA 2023: 1253-1260 - [c62]William F. Godoy, Pedro Valero-Lara, Keita Teranishi, Prasanna Balaprakash, Jeffrey S. Vetter:
Evaluation of OpenAI Codex for HPC Parallel Programming Models Kernel Generation. ICPP Workshops 2023: 136-144 - [c61]Thomas Randall, Jaehoon Koo, Brice Videau, Michael Kruse, Xingfu Wu, Paul D. Hovland, Mary W. Hall, Rong Ge, Prasanna Balaprakash:
Transfer-learning-based Autotuning using Gaussian Copula. ICS 2023: 37-49 - [c60]Prasanna Balaprakash:
iWAPT2023 Invited Speaker Optimizing HPC Systems for Scientific Applications: Machine Learning Approaches to Performance Tuning and Anomaly Detection. IPDPS Workshops 2023: 705 - [c59]Prasanna Balaprakash:
Scalable Automated Design and Development of Deep Neural Networks for Scientific and Engineering Applications. IPDPS Workshops 2023: 787 - [i57]Tanwi Mallick, Joshua David Bergerson, Duane R. Verner, John K. Hutchison, Leslie-Anne Levy, Prasanna Balaprakash:
Analyzing the impact of climate change on critical infrastructure from the scientific literature: A weakly supervised NLP approach. CoRR abs/2302.01887 (2023) - [i56]Romit Maulik, Romain Egele, Krishnan Raghavan, Prasanna Balaprakash:
Quantifying uncertainty for deep learning based forecasting and flow-reconstruction using neural architecture search ensembles. CoRR abs/2302.09748 (2023) - [i55]Xingfu Wu, Prasanna Balaprakash, Michael Kruse, Jaehoon Koo, Brice Videau, Paul D. Hovland, Valerie Taylor, Brad Geltz, Siddhartha Jana, Mary W. Hall:
ytopt: Autotuning Scientific Applications for Energy Efficiency at Large Scales. CoRR abs/2303.16245 (2023) - [i54]Lele Luan, Nesar Ramachandra, Sandipp Krishnan Ravi, Anindya Bhaduri, Piyush Pandita, Prasanna Balaprakash, Mihai Anitescu, Changjie Sun, Liping Wang:
Application of probabilistic modeling and automated machine learning framework for high-dimensional stress field. CoRR abs/2303.16869 (2023) - [i53]Krishnan Raghavan, Prasanna Balaprakash:
Learning Continually on a Sequence of Graphs - The Dynamical System Way. CoRR abs/2305.12030 (2023) - [i52]George Papadimitriou, Hongwei Jin, Cong Wang, Krishnan Raghavan, Anirban Mandal, Prasanna Balaprakash, Ewa Deelman:
Flow-Bench: A Dataset for Computational Workflow Anomaly Detection. CoRR abs/2306.09930 (2023) - [i51]William F. Godoy, Pedro Valero-Lara, Keita Teranishi, Prasanna Balaprakash, Jeffrey S. Vetter:
Evaluation of OpenAI Codex for HPC Parallel Programming Models Kernel Generation. CoRR abs/2306.15121 (2023) - [i50]Shengli Jiang, Shiyi Qin, Reid C. Van Lehn, Prasanna Balaprakash, Victor M. Zavala:
Uncertainty Quantification for Molecular Property Predictions with Graph Neural Architecture Search. CoRR abs/2307.10438 (2023) - [i49]Romain Egele, Isabelle Guyon, Yixuan Sun, Prasanna Balaprakash:
Is One Epoch All You Need For Multi-Fidelity Hyperparameter Optimization? CoRR abs/2307.15422 (2023) - [i48]Sandeep Madireddy, Angel Yanguas-Gil, Prasanna Balaprakash:
Improving Performance in Continual Learning Tasks using Bio-Inspired Architectures. CoRR abs/2308.04539 (2023) - [i47]Pedro Valero-Lara, Alexis Huante, Mustafa Al-Lail, William F. Godoy, Keita Teranishi, Prasanna Balaprakash, Jeffrey S. Vetter:
Comparing Llama-2 and GPT-3 LLMs for HPC kernels generation. CoRR abs/2309.07103 (2023) - [i46]Romain Egele, Tyler Chang, Yixuan Sun, Venkatram Vishwanath, Prasanna Balaprakash:
Parallel Multi-Objective Hyperparameter Optimization with Uniform Normalization and Bounded Objectives. CoRR abs/2309.14936 (2023) - [i45]Hongwei Jin, Krishnan Raghavan, George Papadimitriou, Cong Wang, Anirban Mandal, Ewa Deelman, Prasanna Balaprakash:
Self-supervised Learning for Anomaly Detection in Computational Workflows. CoRR abs/2310.01247 (2023) - [i44]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:
DeepSpeed4Science Initiative: Enabling Large-Scale Scientific Discovery through Sophisticated AI System Technologies. CoRR abs/2310.04610 (2023) - [i43]Sajal Dash, Isaac Lyngaas, Junqi Yin, Xiao Wang, Romain Egele, Guojing Cong, Feiyi Wang, Prasanna Balaprakash:
Optimizing Distributed Training on Frontier for Large Language Models. CoRR abs/2312.12705 (2023) - 2022
- [j15]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:
A cross-study analysis of drug response prediction in cancer cell lines. Briefings Bioinform. 23(1) (2022) - [j14]Dominic Yang, Prasanna Balaprakash, Sven Leyffer:
Modeling design and control problems involving neural network surrogates. Comput. Optim. Appl. 83(3): 759-800 (2022) - [j13]Xingfu Wu, Michael Kruse, Prasanna Balaprakash, Hal Finkel, Paul D. Hovland, Valerie Taylor, Mary W. Hall:
Autotuning PolyBench benchmarks with LLVM Clang/Polly loop optimization pragmas using Bayesian optimization. Concurr. Comput. Pract. Exp. 34(20) (2022) - [j12]Nathan A Garland, Romit Maulik, Qi Tang, Xian-Zhu Tang, Prasanna Balaprakash:
Efficient data acquisition and training of collisional-radiative model artificial neural network surrogates through adaptive parameter space sampling. Mach. Learn. Sci. Technol. 3(4): 45003 (2022) - [j11]Sami Khairy, Prasanna Balaprakash, Lin X. Cai, H. Vincent Poor:
Data-Driven Random Access Optimization in Multi-Cell IoT Networks Using NOMA. IEEE Trans. Wirel. Commun. 21(7): 4938-4953 (2022) - [c58]Matthieu Dorier, Romain Egele, Prasanna Balaprakash, Jaehoon Koo, Sandeep Madireddy, Srinivasan Ramesh, Allen D. Malony, Robert B. Ross:
HPC Storage Service Autotuning Using Variational- Autoencoder -Guided Asynchronous Bayesian Optimization. CLUSTER 2022: 381-393 - [c57]R. Krishnan, Prasanna Balaprakash:
Continual Learning via Dynamic Programming. ICPR 2022: 1350-1356 - [c56]Romain Egele, Romit Maulik, Krishnan Raghavan, Bethany Lusch, Isabelle Guyon, Prasanna Balaprakash:
AutoDEUQ: Automated Deep Ensemble with Uncertainty Quantification. ICPR 2022: 1908-1914 - [c55]Orcun Yildiz, Henry Chan, Krishnan Raghavan, William Judge, Mathew J. Cherukara, Prasanna Balaprakash, Subramanian Sankaranarayanan, Tom Peterka:
Automated Continual Learning of Defect Identification in Coherent Diffraction Imaging. AI4S 2022: 1-6 - [c54]Mihailo Isakov, Mikaela Currier, Eliakin Del Rosario, Sandeep Madireddy, Prasanna Balaprakash, Philip H. Carns, Robert B. Ross, Glenn K. Lockwood, Michel A. Kinsy:
A Taxonomy of Error Sources in HPC I/O Machine Learning Models. SC 2022: 16:1-16:14 - [c53]Hongwei Jin, Krishnan Raghavan, George Papadimitriou, Cong Wang, Anirban Mandal, Patrycja Krawczuk, Loïc Pottier, Mariam Kiran, Ewa Deelman, Prasanna Balaprakash:
Workflow Anomaly Detection with Graph Neural Networks. WORKS@SC 2022: 35-42 - [i42]Sahil Bhola, Suraj Pawar, Prasanna Balaprakash, Romit Maulik:
Multi-fidelity reinforcement learning framework for shape optimization. CoRR abs/2202.11170 (2022) - [i41]Anirban Samaddar, Sandeep Madireddy, Prasanna Balaprakash:
Sparsity-Inducing Categorical Prior Improves Robustness of the Information Bottleneck. CoRR abs/2203.02592 (2022) - [i40]Alec J. Linot, Joshua W. Burby, Qi Tang, Prasanna Balaprakash, Michael D. Graham, Romit Maulik:
Stabilized Neural Ordinary Differential Equations for Long-Time Forecasting of Dynamical Systems. CoRR abs/2203.15706 (2022) - [i39]Tanwi Mallick, Prasanna Balaprakash, Jane MacFarlane:
Deep-Ensemble-Based Uncertainty Quantification in Spatiotemporal Graph Neural Networks for Traffic Forecasting. CoRR abs/2204.01618 (2022) - [i38]Mihailo Isakov, Mikaela Currier, Eliakin Del Rosario, Sandeep Madireddy, Prasanna Balaprakash, Philip H. Carns, Robert B. Ross, Glenn K. Lockwood, Michel A. Kinsy:
A Taxonomy of Error Sources in HPC I/O Machine Learning Models. CoRR abs/2204.08180 (2022) - [i37]Sanket R. Jantre, Sandeep Madireddy, Shrijita Bhattacharya, Tapabrata Maiti, Prasanna Balaprakash:
Sequential Bayesian Neural Subnetwork Ensembles. CoRR abs/2206.00794 (2022) - [i36]Sami Khairy, Prasanna Balaprakash:
Multifidelity Reinforcement Learning with Control Variates. CoRR abs/2206.05165 (2022) - [i35]Romain Egele, Joceran Gouneau, Venkatram Vishwanath, Isabelle Guyon, Prasanna Balaprakash:
Asynchronous Distributed Bayesian Optimization at HPC Scale. CoRR abs/2207.00479 (2022) - [i34]Weiheng Zhong, Tanwi Mallick, Hadi Meidani, Jane MacFarlane, Prasanna Balaprakash:
Explainable Graph Pyramid Autoformer for Long-Term Traffic Forecasting. CoRR abs/2209.13123 (2022) - [i33]Matthieu Dorier, Romain Egele, Prasanna Balaprakash, Jaehoon Koo, Sandeep Madireddy, Srinivasan Ramesh, Allen D. Malony, Robert B. Ross:
HPC Storage Service Autotuning Using Variational-Autoencoder-Guided Asynchronous Bayesian Optimization. CoRR abs/2210.00798 (2022) - [i32]Sumegha Premchandar, Sandeep Madireddy, Sanket R. Jantre, Prasanna Balaprakash:
Unified Probabilistic Neural Architecture and Weight Ensembling Improves Model Robustness. CoRR abs/2210.04083 (2022) - 2021
- [j10]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. IEEE J. Sel. Areas Commun. 39(4): 1101-1115 (2021) - [j9]Sandeep Madireddy, Ji Hwan Park, Sunwoo Lee, Prasanna Balaprakash, Shinjae Yoo, Wei-keng Liao, Cory D. Hauck, M. Paul Laiu, Richard Archibald:
In situ compression artifact removal in scientific data using deep transfer learning and experience replay. Mach. Learn. Sci. Technol. 2(2): 25010 (2021) - [c52]Grant Getzelman, Prasanna Balaprakash:
Learning to Switch Optimizers for Quadratic Programming. ACML 2021: 1553-1568 - [c51]Krishnan Raghavan, Prasanna Balaprakash:
Formalizing the Generalization-Forgetting Trade-off in Continual Learning. NeurIPS 2021: 17284-17297 - [c50]Jaehoon Koo, Prasanna Balaprakash, Michael Kruse, Xingfu Wu, Paul D. Hovland, Mary W. Hall:
Customized Monte Carlo Tree Search for LLVM/Polly's Composable Loop Optimization Transformations. PMBS 2021: 82-93 - [c49]Romain Égelé, Prasanna Balaprakash, Isabelle Guyon, Venkatram Vishwanath, Fangfang Xia, Rick Stevens, Zhengying Liu:
AgEBO-tabular: joint neural architecture and hyperparameter search with autotuned data-parallel training for tabular data. SC 2021: 30 - [d1]Manuel López-Ibáñez, Leslie Pérez Cáceres, Jérémie Dubois-Lacoste, Thomas Stützle, Mauro Birattari, Zhi Yuan, Prasanna Balaprakash:
irace. Zenodo, 2021
- [i31]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) - [i30]Jongeun Kim, Sven Leyffer, Prasanna Balaprakash:
Learning Symbolic Expressions: Mixed-Integer Formulations, Cuts, and Heuristics. CoRR abs/2102.08351 (2021) - [i29]Xingfu Wu, Michael Kruse, Prasanna Balaprakash, Hal Finkel, Paul D. Hovland, Valerie Taylor, Mary W. Hall:
Autotuning PolyBench Benchmarks with LLVM Clang/Polly Loop Optimization Pragmas Using Bayesian Optimization (extended version). CoRR abs/2104.13242 (2021) - [i28]Jaehoon Koo, Prasanna Balaprakash, Michael Kruse, Xingfu Wu, Paul D. Hovland, Mary W. Hall:
Customized Monte Carlo Tree Search for LLVM/Polly's Composable Loop Optimization Transformations. CoRR abs/2105.04555 (2021) - [i27]Krishnan Raghavan, Prasanna Balaprakash:
Formalizing the Generalization-Forgetting Trade-off in Continual Learning. CoRR abs/2109.14035 (2021) - [i26]Yudong Yao, Henry Chan, Subramanian Sankaranarayanan, Prasanna Balaprakash, Ross J. Harder, Mathew J. Cherukara:
AutoPhaseNN: Unsupervised Physics-aware Deep Learning of 3D Nanoscale Coherent Imaging. CoRR abs/2109.14053 (2021) - [i25]Romain Egele, Romit Maulik, Krishnan Raghavan, Prasanna Balaprakash, Bethany Lusch:
AutoDEUQ: Automated Deep Ensemble with Uncertainty Quantification. CoRR abs/2110.13511 (2021) - [i24]Dominic Yang, Prasanna Balaprakash, Sven Leyffer:
Modeling Design and Control Problems Involving Neural Network Surrogates. CoRR abs/2111.10489 (2021) - [i23]Yixuan Sun, Tanwi Mallick, Prasanna Balaprakash, Jane MacFarlane:
A data-centric weak supervised learning for highway traffic incident detection. CoRR abs/2112.09792 (2021) - 2020
- [c48]Sami Khairy, Ruslan Shaydulin, Lukasz Cincio, Yuri Alexeev, Prasanna Balaprakash:
Learning to Optimize Variational Quantum Circuits to Solve Combinatorial Problems. AAAI 2020: 2367-2375 - [c47]Shengli Jiang, Prasanna Balaprakash:
Graph Neural Network Architecture Search for Molecular Property Prediction. IEEE BigData 2020: 1346-1353 - [c46]Tanwi Mallick, Prasanna Balaprakash, Eric Rask, Jane MacFarlane:
Transfer Learning with Graph Neural Networks for Short-Term Highway Traffic Forecasting. ICPR 2020: 10367-10374 - [c45]Yi He, Prasanna Balaprakash, Yanjing Li:
FIdelity: Efficient Resilience Analysis Framework for Deep Learning Accelerators. MICRO 2020: 270-281 - [c44]Xingfu Wu, Michael Kruse, Prasanna Balaprakash, Hal Finkel, Paul D. Hovland, Valerie Taylor, Mary W. Hall:
Autotuning PolyBench Benchmarks with LLVM Clang/Polly Loop Optimization Pragmas Using Bayesian Optimization. PMBS@SC 2020: 61-70 - [c43]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 - [c42]Romit Maulik, Romain Egele, Bethany Lusch, Prasanna Balaprakash:
Recurrent neural network architecture search for geophysical emulation. SC 2020: 8 - [c41]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 - [c40]Mihailo Isakov, Eliakin Del Rosario, Sandeep Madireddy, Prasanna Balaprakash, Philip H. Carns, Robert B. Ross, Michel A. Kinsy:
HPC I/O throughput bottleneck analysis with explainable local models. SC 2020: 33 - [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 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. IEEE 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 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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