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William Sands

dblp:258/0849 · also William A. Sands · DBLP profile ↗
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1ranked-venue papers
0as first author
1since 2021 · last 2023
0000-0003-3643-4751ORCID · verified

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

Systems, architecture and hardware · 1 · 1 since 2021

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
GPUs and heterogeneous computing · 100%
Theoretical computer science
1 paper
Graph algorithms and graph theory · 100%

Topics — the 2 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
GPUs and heterogeneous computing › GPU graph processing
GPU graph algorithms
0.712023
A GPU Algorithm for Detecting Strongly Connected Components · SC 2023
Graph algorithms and graph theory › graph algorithms › connectivity
strongly connected components
0.712023
A GPU Algorithm for Detecting Strongly Connected Components · SC 2023

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

maximum ID propagation · 1.3edge removal · 1.3
YearPublicationVenuePosition
2023 A GPU Algorithm for Detecting Strongly Connected Components
abstract
Detecting strongly connected components (SCCs) is an important step in various graph computations. The fastest GPU and CPU implementations from the literature work well on graphs where most of the vertices belong to a single SCC and the vertex degrees follow a power-law distribution. However, these algorithms can be slow on the mesh graphs used in certain radiative transfer simulations, which have a nearly constant vertex degree and can have significant variability in the number and size of SCCs. We introduce ECL-SCC, an SCC detection algorithm that addresses these shortcomings. Our approach is GPU friendly and employs innovative techniques such as maximum ID propagation and edge removal. On an A100 GPU, ECL-SCC performs on par with the fastest prior GPU code on power-law graphs and outperforms it by 7.8× on mesh graphs. Moreover, ECL-SCC running on the GPU outperforms fast parallel CPU code by three orders of magnitude on meshes.
Ghadeer Ahmed H. Alabandi, William Sands, George Biros, Martin Burtscher
SC2