Barbara Chapman

Barbara Chapman

Stony Brook University

H-index: 39

North America-United States

About Barbara Chapman

Barbara Chapman, With an exceptional h-index of 39 and a recent h-index of 22 (since 2020), a distinguished researcher at Stony Brook University, specializes in the field of Parallel programming models, compilers, programming languages, application development.

His recent articles reflect a diverse array of research interests and contributions to the field:

Quantum optimization algorithms: Energetic implications

Cross-Feature Transfer Learning for Efficient Tensor Program Generation

Performance Study on CPU-based Machine Learning with PyTorch

Implementing OpenMP’s SIMD Directive in LLVM’s GPU Runtime

Transfer Learning Across Heterogeneous Features For Efficient Tensor Program Generation

Maximizing Parallelism and GPU Utilization For Direct GPU Compilation Through Ensemble Execution

MPI-based Remote OpenMP Offloading: A More Efficient and Easy-to-use Implementation

GPU First--Execution of Legacy CPU Codes on GPUs

Barbara Chapman Information

University

Position

Professor of Applied Mathematics and Statistics and Computer Science

Citations(all)

11065

Citations(since 2020)

2256

Cited By

9687

hIndex(all)

39

hIndex(since 2020)

22

i10Index(all)

142

i10Index(since 2020)

43

Email

University Profile Page

Stony Brook University

Google Scholar

View Google Scholar Profile

Barbara Chapman Skills & Research Interests

Parallel programming models

compilers

programming languages

application development

Top articles of Barbara Chapman

Title

Journal

Author(s)

Publication Date

Quantum optimization algorithms: Energetic implications

Concurrency and Computation: Practice and Experience

Rolando P Hong Enriquez

Rosa M Badia

Barbara Chapman

Kirk Bresniker

Scott Pakin

...

2024/4/22

Cross-Feature Transfer Learning for Efficient Tensor Program Generation

Applied Sciences

Gaurav Verma

Siddhisanket Raskar

Murali Emani

Barbara Chapman

2024/1/6

Performance Study on CPU-based Machine Learning with PyTorch

Smeet Chheda

Anthony Curtis

Eva Siegmann

Barbara Chapman

2023/2/27

Implementing OpenMP’s SIMD Directive in LLVM’s GPU Runtime

Eric Wright

Johannes Doerfert

Shilei Tian

Barbara Chapman

Sunita Chandrasekaran

2023/8/7

Transfer Learning Across Heterogeneous Features For Efficient Tensor Program Generation

Gaurav Verma

Siddhisanket Raskar

Zhen Xie

Abid M Malik

Murali Emani

...

2023/2/25

Maximizing Parallelism and GPU Utilization For Direct GPU Compilation Through Ensemble Execution

Shilei Tian

Barbara Chapman

Johannes Doerfert

2023/8/7

MPI-based Remote OpenMP Offloading: A More Efficient and Easy-to-use Implementation

Baodi Shan

Mauricio Araya-Polo

Abid M Malik

Barbara Chapman

2023/2/25

GPU First--Execution of Legacy CPU Codes on GPUs

arXiv preprint arXiv:2306.11686

Shilei Tian

Tom Scogland

Barbara Chapman

Johannes Doerfert

2023/6/20

OpenMP Kernel Language Extensions for Performance Portable GPU Codes

Shilei Tian

Tom Scogland

Barbara Chapman

Johannes Doerfert

2023/11/12

Openmp advisor

arXiv preprint arXiv:2301.03636

Alok Mishra

Abid M Malik

Meifeng Lin

Barbara Chapman

2023/1/9

Extreme Heterogeneity 2018: DOE ASCR Basic Research Needs Workshop on Extreme Heterogeneity

Surendra Byna

Jeff Vetter

Ron Brightwell

Maya Gokhale

Patrick McCormick

...

2023/4/29

Exploring the Limits of Generic Code Execution on GPUs via Direct (OpenMP) Offload

Shilei Tian

Barbara Chapman

Johannes Doerfert

2023/9/1

Estimating energy-efficiency in quantum optimization algorithms

Cray User Group Conference Proceedings

Rolando P Hong Enriquez

Rosa M Badia

Barbara Chapman

Kirk Bresniker

Aditya Dhakal

...

2023

ParaGraph: Weighted Graph Representation for Performance Optimization of HPC Kernels

arXiv preprint arXiv:2304.03487

Ali TehraniJamsaz

Alok Mishra

Akash Dutta

Abid M Malik

Barbara Chapman

...

2023/4/7

OpenMP Advisor: A Compiler Tool for Heterogeneous Architectures

Alok Mishra

Abid M Malik

Meifeng Lin

Barbara Chapman

2023/9/1

Languages and Compilers for Parallel Computing

Vikram Adve

María Jesús Garzarán

Paul Petersen

2008

Direct GPU compilation and execution for host applications with OpenMP Parallelism

Shilei Tian

Joseph Huber

Konstantinos Parasyris

Barbara Chapman

Johannes Doerfert

2022/11/13

Co-Designing an OpenMP GPU runtime and optimizations for near-zero overhead execution

Johannes Doerfert

Atemn Patel

Joseph Huber

Shilei Tian

Jose M Monsalve Diaz

...

2022/5/30

Extending OpenMP and OpenSHMEM for Efficient Heterogeneous Computing

Wenbin Lu

Shilei Tian

Tony Curtis

Barbara Chapman

2022/11/13

Compoff: A compiler cost model using machine learning to predict the cost of openmp offloading

Alok Mishra

Smeet Chheda

Carlos Soto

Abid M Malik

Meifeng Lin

...

2022/5/30

See List of Professors in Barbara Chapman University(Stony Brook University)