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Jeffrey Vetter

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Corporate Fellow, Section Head

Oak Ridge National Laboratory

United States

Research Interests

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About

Jeffrey Vetter is a Corporate Fellow and Section Head at Oak Ridge National Laboratory in the United States. His research focuses on high-performance computing and heterogeneous computing frameworks, as evidenced by his recent work on IRIS and Clacc. He is also engaged in advancing neuromorphic materials and architectures, contributing to the field with his article on Abisko. Additionally, he explores methods to enhance coding productivity for GPU programming through static graphs.

Recent Grants

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Keeneland: National Institute for Experimental Computing

Open Date: 2009-09-01

Close Date: 2015-04-30

Articles (19)

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Collaborators (7)

Jason Lowe-Power

Associate Professor

University of California, Davis

UNITED STATES

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Prasanna Balaprakash

Director of AI Programs and Distinguished R&D Scientist

Oak Ridge National Laboratory

UNITED STATES

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Yuan He

Research Scientist

RIKEN Center for Computational Science

JAPAN

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Paul Kent

Oak Ridge National Laboratory

UNITED STATES

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Marc Gonzalez-Tallada

Universitat Politècnica de Catalunya

SPAIN

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Matthew J. Marinella

Associate Professor

Arizona State University

UNITED STATES

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Rafael Ferreira da Silva

Group Leader / Senior Research Scientist

Oak Ridge National Laboratory

UNITED STATES

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Jeffrey Vetter

Research Interests

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About

Recent Grants

Articles (19)

Characterizing GPU Energy Usage in Exascale-Ready Portable Science Applications

A Survey on the Expanding Scope and Interdisciplinary Opportunities for Processing-in-Memory Techniques

Accl++ : A high-productivity programming language for performance and code portability on heterogeneous systems

Comparing Llama-2 and GPT-3 LLMs for HPC Kernels Generation

ChatMPI: LLM-Driven MPI Code Generation for HPC Workloads

Q-IRIS: The Evolution of the IRIS Task-Based Runtime to Enable Classical-Quantum Workflows

Benchmarking Operators in Deep Neural Networks for Improving Performance Portability of SYCL

ReSpike: A Co-Design Framework for Evaluating SNNs on ReRAM-Based Neuromorphic Processors

SEER: An On-node Performance-Portable C++ Library for Heterogeneous Systems

ChatHPC: Building the Foundations for a Productive and Trustworthy AI-Assisted HPC Ecosystem

Mojo: MLIR-based Performance-Portable HPC Science Kernels on GPUs for the Python Ecosystem

Filling Performance Portability and High-Productivity Gaps for Scientific Applications with Julia and JACC

IRIS-MASH: Efficient Multi-device Asynchronous Multi-Stream Heterogeneous Computing

Extending SEER for Extreme Heterogeneity

Performance Impact and Trade-Offs for Tuning Key Architectural Parameters on CPU+GPU Systems

IRIS: A Performance-Portable Framework for Cross-Platform Heterogeneous Computing

Clacc: OpenACC for C/C++ in Clang

Abisko: Deep codesign of an architecture for spiking neural networks using novel neuromorphic materials

Towards Enhancing Coding Productivity for GPU Programming Using Static Graphs

Collaborators (7)

Jason Lowe-Power

Prasanna Balaprakash

Yuan He

Paul Kent

Marc Gonzalez-Tallada

Matthew J. Marinella

Rafael Ferreira da Silva

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