Demo Data from EDBT 2026 / reviewers in the wild / expert
Jarim Seo
dblp:359/6171
· DBLP profile ↗
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
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
GPUs and heterogeneous computing › GPU graph processing
GPU graph algorithms |
0.7 | 1 | 2023 | A High-Performance MST Implementation for GPUs · SC 2023 |
Parallel and multicore computing › parallel graph algorithms
minimum spanning tree |
0.7 | 1 | 2023 | A High-Performance MST Implementation for GPUs · SC 2023 |
Graph algorithms and graph theory
graph algorithms |
0.7 | 1 | 2023 | A High-Performance MST Implementation for GPUs · SC 2023 |
Graph algorithms and graph theory › spanning tree
minimum spanning tree |
0.7 | 1 | 2023 | 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
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2023 | A High-Performance MST Implementation for GPUsabstractFinding 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 |
SC | 3 |