EDBT 2026 Demo / reviewers in the wild / expert
Thomas Musta
dblp:417/4224 · also Tom Musta
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2025
0009-0002-4577-4125ORCID · 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 |
Hardware reliability and fault tolerance · 30% High-performance computing · 30% Distributed systems · 30% |
Topics — the 3 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
High-performance computing › supercomputing
exascale computing |
0.9 | 1 | 2025 | Fine-grained Automated Failure Management for Extreme-Scale GPU Accelerated Systems · SC 2025 |
Distributed systems
fault tolerance |
0.9 | 1 | 2025 | Fine-grained Automated Failure Management for Extreme-Scale GPU Accelerated Systems · SC 2025 |
GPUs and heterogeneous computing › GPU computing
GPU-accelerated systems |
0.3 | 1 | 2025 | Fine-grained Automated Failure Management for Extreme-Scale GPU Accelerated Systems · SC 2025 |
Methods — techniques the papers use, named apart from their topics
failure analysis · 0.9
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2025 | Fine-grained Automated Failure Management for Extreme-Scale GPU Accelerated SystemsabstractAs high-performance computing (HPC) systems scale in size, system wide hardware failure rates increase. Historical data from previous large-scale HPC installations illustrate this trend, with the mean time between failures (MTBF) decreasing steadily over the past decade. Recent studies from artificial intelligence and machine-learning (AI/ML) training extrapolate MTBF declining even further for future GPU accelerated systems. As MTBF decreases, mean time to repair (MTTR) becomes more pronounced, highlighting the need for efficient recovery strategies. Yonatan Levitt, Richard Barella, Sam Zeltner, Thomas Musta, Lance C. Cheney, Gustavo Espinosa, Olivier Franza, Balazs Gerofi |
SC | 4 |