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Harrison Barclay

dblp:242/9082 · DBLP profile ↗
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2ranked-venue papers
0as first author
0since 2021 · last 2019
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Systems, architecture and hardware · 2Software engineering, systems software and programming languages · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Computer architecture, parallel and distributed computing, and storage systems
1 paper
Performance modeling and evaluation · 65% GPUs and heterogeneous computing · 35%

Topics — the 6 heaviest of 6, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Performance modeling and evaluation › processor performance modeling › accelerator performance modeling
GPU performance modeling
0.412019
MGPUSim: enabling multi-GPU performance modeling and optimization · ISCA 2019
Performance modeling and evaluation › simulation › parallel architecture simulation
GPU simulation
0.412019
MGPUSim: enabling multi-GPU performance modeling and optimization · ISCA 2019
GPUs and heterogeneous computing
multi-GPU computing
0.412019
MGPUSim: enabling multi-GPU performance modeling and optimization · ISCA 2019
Performance modeling and evaluation
simulation
0.412019
MGPUSim: enabling multi-GPU performance modeling and optimization · ISCA 2019
GPUs and heterogeneous computing
GPU microarchitecture
0.112019
MGPUSim: enabling multi-GPU performance modeling and optimization · ISCA 2019
GPUs and heterogeneous computing › multi-GPU computing
multi-GPU interconnect
0.112019
MGPUSim: enabling multi-GPU performance modeling and optimization · ISCA 2019

Methods — techniques the papers use, named apart from their topics

simulation · 0.4
YearPublicationVenuePosition
2019 MGPUSim: enabling multi-GPU performance modeling and optimization
abstract
The rapidly growing popularity and scale of data-parallel workloads demand a corresponding increase in raw computational power of Graphics Processing Units (GPUs). As single-GPU platforms struggle to satisfy these performance demands, multi-GPU platforms have started to dominate the high-performance computing world. The advent of such systems raises a number of design challenges, including the GPU microarchitecture, multi-GPU interconnect fabric, runtime libraries, and associated programming models. The research community currently lacks a publicly available and comprehensive multi-GPU simulation framework to evaluate next-generation multi-GPU system designs.
Yifan Sun 0002, Trinayan Baruah, Saiful A. Mojumder, Shi Dong 0002, Shane Treadway, Yuhui Bao, Spencer Hance, Carter McCardwell, Vincent Zhao, Harrison Barclay, Amir Kavyan Ziabari, Zhongliang Chen, Rafael Ubal, José L. Abellán, John Kim 0001, Ajay Joshi, David R. Kaeli
ISCA11
2019 Student cluster competition 2018, team northeastern university: Reproducing performance of a multi-physics simulations of the Tsunamigenic 2004 Sumatra Megathrust earthquake on the AMD EPYC 7551 architecture
Christopher C. Bunn, Harrison Barclay, A. Lazarev, F. Yusuf, J. Fitch, J. Booth, Kaustubh Shivdikar, David R. Kaeli
Parallel Comput.2