8 citations · 9 across the 8 of their papers we have counts for
9 papers
Coordinated Power Management on Heterogeneous Systems
Zhong Zheng, Zhiling Lan, Xingfu Wu +2
Performance prediction is essential for energy-efficient computing in heterogeneous computing systems that integrate CPUs and GPUs. However, traditional performance modeling method…
Generalizing Scaling Laws for Dense and Sparse Large Language Models
Md Arafat Hossain, Xingfu Wu, Valerie Taylor +1
Despite recent advancements of large language models (LLMs), optimally predicting the model size for LLM pretraining or allocating optimal resources still remains a challenge. Seve…
Leveraging LLMs to Automate Energy-Aware Refactoring of Parallel Scientific Codes
Matthew T. Dearing, Yiheng Tao, Xingfu Wu +2
Large language models (LLMs) are increasingly used for generating parallel scientific codes, with a primary focus on generating functionally correct code. Recent work has focused o…
Co-Design of 2D Heterojunctions for Data Filtering in Tracking Systems
Tupendra Oli, Wilkie Olin-Ammentorp, Xingfu Wu +5
As particle physics experiments evolve to achieve higher energies and resolutions, handling the massive data volumes produced by silicon pixel detectors, which are used for charged…
LASSI: An LLM-based Automated Self-Correcting Pipeline for Translating Parallel Scientific Codes
Matthew T. Dearing, Yiheng Tao, Xingfu Wu +2
This paper addresses the problem of providing a novel approach to sourcing significant training data for LLMs focused on science and engineering. In particular, a crucial challenge…
Modeling Performance of Data Collection Systems for High-Energy Physics
Wilkie Olin-Ammentorp, Xingfu Wu, Andrew A. Chien
Exponential increases in scientific experimental data are outstripping the rate of progress in silicon technology. As a result, heterogeneous combinations of architectures and proc…
An Autotuning-based Optimization Framework for Mixed-kernel SVM Classifications in Smart Pixel Datasets and Heterojunction Transistors
Xingfu Wu, Tupendra Oli, Justin H. Qian +3
Support Vector Machine (SVM) is a state-of-the-art classification method widely used in science and engineering due to its high accuracy, its ability to deal with high dimensional…
Integrating ytopt and libEnsemble to Autotune OpenMC
Xingfu Wu, John R. Tramm, Jeffrey Larson +7
ytopt is a Python machine-learning-based autotuning software package developed within the ECP PROTEAS-TUNE project. The ytopt software adopts an asynchronous search framework that…
Autotuning Apache TVM-based Scientific Applications Using Bayesian Optimization
Xingfu Wu, Praveen Paramasivam, Valerie Taylor
Apache TVM (Tensor Virtual Machine), an open source machine learning compiler framework designed to optimize computations across various hardware platforms, provides an opportunity…