303 citations · 960 across the 44 of their papers we have counts for
46 papers
Report of the 2025 Workshop on Next-Generation Ecosystems for Scientific Computing: Harnessing Community, Software, and AI for Cross-Disciplinary Team Science
Lois Curfman McInnes, Dorian Arnold, Prasanna Balaprakash +40
This report summarizes insights from the 2025 Workshop on Next-Generation Ecosystems for Scientific Computing: Harnessing Community, Software, and AI for Cross-Disciplinary Team Sc…
Comparing Llama-2 and GPT-3 LLMs for HPC kernels generation
Pedro Valero-Lara, Alexis Huante, Mustafa Al Lail +4
We evaluate the use of the open-source Llama-2 model for generating well-known, high-performance computing kernels (e.g., AXPY, GEMV, GEMM) on different parallel programming models…
Evaluation of OpenAI Codex for HPC Parallel Programming Models Kernel Generation
William F. Godoy, Pedro Valero-Lara, Keita Teranishi +2
We evaluate AI-assisted generative capabilities on fundamental numerical kernels in high-performance computing (HPC), including AXPY, GEMV, GEMM, SpMV, Jacobi Stencil, and CG. We t…
Flow-Bench: A Dataset for Computational Workflow Anomaly Detection
George Papadimitriou, Hongwei Jin, Cong Wang +5
A computational workflow, also known as workflow, consists of tasks that must be executed in a specific order to attain a specific goal. Often, in fields such as biology, chemistry…
ytopt: Autotuning Scientific Applications for Energy Efficiency at Large Scales
Xingfu Wu, Prasanna Balaprakash, Michael Kruse +7
As we enter the exascale computing era, efficiently utilizing power and optimizing the performance of scientific applications under power and energy constraints has become critical…
A Taxonomy of Error Sources in HPC I/O Machine Learning Models
Mihailo Isakov, Mikaela Currier, Eliakin del Rosario +6
I/O efficiency is crucial to productivity in scientific computing, but the increasing complexity of the system and the applications makes it difficult for practitioners to understa…
Classification of events from -induced reactions in the MUSIC detector via statistical and ML methods
Krishnan Raghavan, Melina L. Avila, Prasanna Balaprakash +2
The Multi-Sampling Ionization Chamber (MUSIC) detector is typically used to measure nuclear reaction cross sections relevant for nuclear astrophysics, fusion studies, and other app…
A data-centric weak supervised learning for highway traffic incident detection
Yixuan Sun, Tanwi Mallick, Prasanna Balaprakash +1
Using the data from loop detector sensors for near-real-time detection of traffic incidents in highways is crucial to averting major traffic congestion. While recent supervised mac…
AutoDEUQ: Automated Deep Ensemble with Uncertainty Quantification
Romain Egele, Romit Maulik, Krishnan Raghavan +3
Deep neural networks are powerful predictors for a variety of tasks. However, they do not capture uncertainty directly. Using neural network ensembles to quantify uncertainty is co…
Formalizing the Generalization-Forgetting Trade-off in Continual Learning
Krishnan Raghavan, Prasanna Balaprakash
We formulate the continual learning (CL) problem via dynamic programming and model the trade-off between catastrophic forgetting and generalization as a two-player sequential game.…
Data-Driven Modeling of Coarse Mesh Turbulence for Reactor Transient Analysis Using Convolutional Recurrent Neural Networks
Yang Liu, Rui Hu, Adam Kraus +2
Advanced nuclear reactors often exhibit complex thermal-fluid phenomena during transients. To accurately capture such phenomena, a coarse-mesh three-dimensional (3-D) modeling capa…
Customized Monte Carlo Tree Search for LLVM/Polly's Composable Loop Optimization Transformations
Jaehoon Koo, Prasanna Balaprakash, Michael Kruse +3
Polly is the LLVM project's polyhedral loop nest optimizer. Recently, user-directed loop transformation pragmas were proposed based on LLVM/Clang and Polly. The search space expose…
Autotuning PolyBench Benchmarks with LLVM Clang/Polly Loop Optimization Pragmas Using Bayesian Optimization (extended version)
Xingfu Wu, Michael Kruse, Prasanna Balaprakash +4
In this paper, we develop a ytopt autotuning framework that leverages Bayesian optimization to explore the parameter space search and compare four different supervised learning met…
A cross-study analysis of drug response prediction in cancer cell lines
Fangfang Xia, Jonathan Allen, Prasanna Balaprakash +21
To enable personalized cancer treatment, machine learning models have been developed to predict drug response as a function of tumor and drug features. However, most algorithm deve…
Learning Symbolic Expressions: Mixed-Integer Formulations, Cuts, and Heuristics
Jongeun Kim, Sven Leyffer, Prasanna Balaprakash
In this paper we consider the problem of learning a regression function without assuming its functional form. This problem is referred to as symbolic regression. An expression tree…
Data-Driven Random Access Optimization in Multi-Cell IoT Networks with NOMA
Sami Khairy, Prasanna Balaprakash, Lin X. Cai +1
Non-orthogonal multiple access (NOMA) is a key technology to enable massive machine type communications (mMTC) in 5G networks and beyond. In this paper, NOMA is applied to improve…
Machine learning-based inversion of nuclear responses
Krishnan Raghavan, Prasanna Balaprakash, Alessandro Lovato +2
A microscopic description of the interaction of atomic nuclei with external electroweak probes is required for elucidating aspects of short-range nuclear dynamics and for the corre…
Autotuning PolyBench Benchmarks with LLVM Clang/Polly Loop Optimization Pragmas Using Bayesian Optimization
Xingfu Wu, Michael Kruse, Prasanna Balaprakash +4
An autotuning is an approach that explores a search space of possible implementations/configurations of a kernel or an application by selecting and evaluating a subset of implement…
AgEBO-Tabular: Joint Neural Architecture and Hyperparameter Search with Autotuned Data-Parallel Training for Tabular Data
Romain Egele, Prasanna Balaprakash, Venkatram Vishwanath +2
Developing high-performing predictive models for large tabular data sets is a challenging task. The state-of-the-art methods are based on expert-developed model ensembles from diff…
Dynamic Graph Neural Network for Traffic Forecasting in Wide Area Networks
Tanwi Mallick, Mariam Kiran, Bashir Mohammed +1
Wide area networking infrastructures (WANs), particularly science and research WANs, are the backbone for moving large volumes of scientific data between experimental facilities an…
Graph Neural Network Architecture Search for Molecular Property Prediction
Shengli Jiang, Prasanna Balaprakash
Predicting the properties of a molecule from its structure is a challenging task. Recently, deep learning methods have improved the state of the art for this task because of their…
Meta Continual Learning via Dynamic Programming
R. Krishnan, Prasanna Balaprakash
Meta continual learning algorithms seek to train a model when faced with similar tasks observed in a sequential manner. Despite promising methodological advancements, there is a la…
Neuromodulated Neural Architectures with Local Error Signals for Memory-Constrained Online Continual Learning
Sandeep Madireddy, Angel Yanguas-Gil, Prasanna Balaprakash
The ability to learn continuously from an incoming data stream without catastrophic forgetting is critical for designing intelligent systems. Many existing approaches to continual…
Non-autoregressive time-series methods for stable parametric reduced-order models
Romit Maulik, Bethany Lusch, Prasanna Balaprakash
Advection-dominated dynamical systems, characterized by partial differential equations, are found in applications ranging from weather forecasting to engineering design where accur…