51 citations · 114 across the 8 of their papers we have counts for
8 papers
AI Surrogate Model for Distributed Computing Workloads
David K. Park, Yihui Ren, Ozgur O. Kilic +18
Large-scale international scientific collaborations, such as ATLAS, Belle II, CMS, and DUNE, generate vast volumes of data. These experiments necessitate substantial computational…
DeepSpeed4Science Initiative: Enabling Large-Scale Scientific Discovery through Sophisticated AI System Technologies
Shuaiwen Leon Song, Bonnie Kruft, Minjia Zhang +89
In the upcoming decade, deep learning may revolutionize the natural sciences, enhancing our capacity to model and predict natural occurrences. This could herald a new era of scient…
Learning Generalizable Program and Architecture Representations for Performance Modeling
Lingda Li, Thomas Flynn, Adolfy Hoisie
Performance modeling is an essential tool in many areas, including performance characterization/optimization, design space exploration, and resource allocation problems, to name a…
SimNet: Accurate and High-Performance Computer Architecture Simulation using Deep Learning
Lingda Li, Santosh Pandey, Thomas Flynn +3
While discrete-event simulators are essential tools for architecture research, design, and development, their practicality is limited by an extremely long time-to-solution for real…
TRUST: Triangle Counting Reloaded on GPUs
Santosh Pandey, Zhibin Wang, Sheng Zhong +8
Triangle counting is a building block for a wide range of graph applications. Traditional wisdom suggests that i) hashing is not suitable for triangle counting, ii) edge-centric tr…
C-SAW: A Framework for Graph Sampling and Random Walk on GPUs
Santosh Pandey, Lingda Li, Adolfy Hoisie +2
Many applications require to learn, mine, analyze and visualize large-scale graphs. These graphs are often too large to be addressed efficiently using conventional graph processing…
Performance Analysis of Deep Learning Workloads on Leading-edge Systems
Yihui Ren, Shinjae Yoo, Adolfy Hoisie
This work examines the performance of leading-edge systems designed for machine learning computing, including the NVIDIA DGX-2, Amazon Web Services (AWS) P3, IBM Power System Accel…
Fast Support Vector Machines Using Parallel Adaptive Shrinking on Distributed Systems
Jeyanthi Narasimhan, Abhinav Vishnu, Lawrence Holder +1
Support Vector Machines (SVM), a popular machine learning technique, has been applied to a wide range of domains such as science, finance, and social networks for supervised learni…