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Jarim Seo

dblp:359/6171 · DBLP profile ↗
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1ranked-venue papers
0as first author
1since 2021 · last 2023
0000-0002-5023-5284ORCID · reported

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 · 50% Parallel and multicore computing · 50%
Theoretical computer science
1 paper
Graph algorithms and graph theory · 100%

Topics — the 4 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
GPUs and heterogeneous computing › GPU graph processing
GPU graph algorithms
0.712023
A High-Performance MST Implementation for GPUs · SC 2023
Parallel and multicore computing › parallel graph algorithms
minimum spanning tree
0.712023
A High-Performance MST Implementation for GPUs · SC 2023
Graph algorithms and graph theory
graph algorithms
0.712023
A High-Performance MST Implementation for GPUs · SC 2023
Graph algorithms and graph theory › spanning tree
minimum spanning tree
0.712023
A High-Performance MST Implementation for GPUs · SC 2023

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

kruskal's algorithm · 1.3implicit path compression · 1.3borůvka's algorithm · 1.3
YearPublicationVenuePosition
2023 A High-Performance MST Implementation for GPUs
abstract
Finding a minimum spanning tree (MST) is a fundamental graph algorithm with applications in many fields. This paper presents ECL-MST, a fast MST implementation designed specifically for GPUs. ECL-MST is based on a parallelization approach that unifies Kruskal's and Borůvka's algorithm and incorporates new and existing optimizations from the literature, including implicit path compression and edge-centric operation. On two test systems, it outperforms leading GPU and CPU codes from the literature on all of our 17 input graphs from various domains. On a Titan V GPU, ECL-MST is, on average, 4.6 times faster than the next fastest code, and on an RTX 3080 Ti GPU, it is 4.5 times faster. On both systems, ECL-MST running on the GPU is roughly 30 times faster than the fastest parallel CPU code.
Alex Fallin, Andres Gonzalez, Jarim Seo, Martin Burtscher
SC3