2026 Michael Garland: Computer Science Researcher – H-Index, Publications & Awards

Discipline name	D-Index	World Ranking	Current World Ranking	National Ranking	Current National Ranking	Publications	Citations
Computer Science	56	3942	3831	1873	1806	140	27176

Discipline name

D-Index

World Ranking

Current World Ranking

National Ranking

Current National Ranking

Publications

Citations

Computer Science

3942

3831

1873

1806

140

27176

Overview

Michael Garland is affiliated with Nvidia in the United States and conducts research primarily in the field of Computer Science, with a focus on Artificial Intelligence, Hardware and Architecture, and Computer Networks and Communications. Their work spans several subfields, including Computer Vision and Pattern Recognition and Computational Mechanics.

The main topics of Garland's research include:

Parallel Computing and Optimization Techniques
Machine Learning and Data Classification
Advanced Neural Network Applications
Tensor decomposition and applications
Computational Physics and Python Applications
Distributed and Parallel Computing Systems
Algorithms and Data Compression

Garland has contributed to various publication venues. The most frequent include:

arXiv (Cornell University)
IEEE Micro
ACM Transactions on Architecture and Code Optimization
Computing in Science & Engineering
Proceedings on Privacy Enhancing Technologies

Among recent scholarly works involving Garland or related closely to their research network, notable papers include:

"A Programmable Approach to Neural Network Compression," 2020, IEEE Micro
"Supercomputing in Python With Legate," 2021, Computing in Science & Engineering
"Stream-K: Work-centric Parallel Decomposition for Dense Matrix-Matrix Multiplication on the GPU," 2023, arXiv (Cornell University)
"Exploring Data Layout for Sparse Tensor Times Dense Matrix on GPUs," 2023, ACM Transactions on Architecture and Code Optimization
"GPU-Initiated On-Demand High-Throughput Storage Access in the BaM System Architecture," 2022, arXiv (Cornell University)

The network of frequent coauthors in Garland's research includes:

V. Roshan Joseph
Saurav Muralidharan
Michael Bauer
Charles Gouert
Steven Dalton

Best Publications

Surface simplification using quadric error metrics

Michael Garland;Paul S. Heckbert

5367
Citations
Scalable Parallel Programming with CUDA: Is CUDA the parallel programming model that application developers have been waiting for?

John Nickolls;Ian Buck;Michael Garland;Kevin Skadron

3233
Citations
Scalable parallel programming with CUDA

John Nickolls;Ian Buck;Michael Garland;Kevin Skadron

2702
Citations
Implementing sparse matrix-vector multiplication on throughput-oriented processors

Nathan Bell;Michael Garland

1167
Citations
Ecient Sparse Matrix-Vector Multiplication on CUDA

Nathan Bell;Michael Garland

1086
Citations
Designing efficient sorting algorithms for manycore GPUs

Nadathur Satish;Mark Harris;Michael Garland

913
Citations
GPUs and the Future of Parallel Computing

S. W. Keckler;W. J. Dally;B. Khailany;M. Garland

822
Citations
Survey of Polygonal Surface Simplification Algorithms

Paul S. Heckbert;Michael Garland

816
Citations
Parallel Computing Experiences with CUDA

M. Garland;S. Le Grand;J. Nickolls;J. Anderson

812
Citations
Simplifying surfaces with color and texture using quadric error metrics

Michael Garland;Paul S. Heckbert

751
Citations
Scalable GPU graph traversal

Duane Merrill;Michael Garland;Andrew Grimshaw

678
Citations
Fast BVH Construction on GPUs

Christian Lauterbach;Michael Garland;Shubhabrata Sengupta;David P. Luebke

597
Citations
Hierarchical face clustering on polygonal surfaces

Michael Garland;Andrew Willmott;Paul S. Heckbert

557
Citations
Multiresolution Modeling: Survey and Future Opportunities

Michael Garland

458
Citations
Spectral surface quadrangulation

Shen Dong;Peer-Timo Bremer;Michael Garland;Valerio Pascucci

418
Citations
Quadric-based polygonal surface simplification

Michael Garland;Paul Heckbert

386
Citations
Fast Polygonal Approximation of Terrains and Height Fields

Michael Garland

368
Citations
Multiresolution Modeling for Fast Rendering

Paul S. Heckbert;Michael Garland

305
Citations
Optimal triangulation and quadric-based surface simplification

Paul S. Heckbert;Michael Garland

276
Citations
Copperhead: compiling an embedded data parallel language

Bryan Catanzaro;Michael Garland;Kurt Keutzer

274
Citations
Fair morse functions for extracting the topological structure of a surface mesh

Xinlai Ni;Michael Garland;John C. Hart

259
Citations

Frequent Co-Authors

David Luebke Nvidia (United States)

Timo Aila Aalto University

Ganesh Gopalakrishnan University of Utah

Bryan Catanzaro Nvidia (United States)

Mary Hall University of Utah

Samuli Laine Nvidia (United States)

John D. Owens University of California, Davis

Animesh Garg University of Toronto

Andrew S. Grimshaw University of Virginia

Kevin Skadron University of Virginia

External Links

Personal website of Michael Garland Google Scholar page

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