EDBT 2026 Demo / reviewers in the wild / expert
Daniel Edelman
dblp:298/7821
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2022
—ORCID · none
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 |
Performance modeling and evaluation · 61% GPUs and heterogeneous computing · 39% |
Topics — the 4 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Performance modeling and evaluation › workload characterization
AI workload characterization |
0.6 | 1 | 2022 | AI-Enabling Workloads on Large-Scale GPU-Accelerated System: Characterization, Opportunities, and Implications · HPCA 2022 |
GPUs and heterogeneous computing › GPU computing
GPU-accelerated systems |
0.6 | 1 | 2022 | AI-Enabling Workloads on Large-Scale GPU-Accelerated System: Characterization, Opportunities, and Implications · HPCA 2022 |
Performance modeling and evaluation
workload characterization |
0.6 | 1 | 2022 | AI-Enabling Workloads on Large-Scale GPU-Accelerated System: Characterization, Opportunities, and Implications · HPCA 2022 |
GPUs and heterogeneous computing
GPU computing |
0.2 | 1 | 2022 | AI-Enabling Workloads on Large-Scale GPU-Accelerated System: Characterization, Opportunities, and Implications · HPCA 2022 |
Methods — techniques the papers use, named apart from their topics
user behavior analysis · 0.6job trace analysis · 0.6
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2022 | AI-Enabling Workloads on Large-Scale GPU-Accelerated System: Characterization, Opportunities, and ImplicationsabstractProduction high-performance computing (HPC) systems are adopting and integrating GPUs into their design to accommodate artificial intelligence (AI), machine learning, and data visualization workloads. To aid with the design and operations of new and existing GPU-based large-scale systems, we provide a detailed characterization of system operations, job characteristics, user behavior, and trends on a contemporary GPU-accelerated production HPC system. Our insights indicate that the pre-mature phases in modern AI workflow take up significant GPU hours while underutilizing GPUs, which opens up the opportunity for a multi-tier system. Finally, we provide various potential recommendations and areas for future investment for system architects, operators, and users. Baolin Li 0001, Rohin Arora, Siddharth Samsi, Tirthak Patel, William Arcand, David Bestor, Chansup Byun, Rohan Basu Roy, Bill Bergeron, John T. Holodnak, Michael Houle 0001, Matthew Hubbell, Michael Jones 0001, Jeremy Kepner, Anna Klein, Peter Michaleas, Joseph McDonald, Lauren Milechin, Julie Mullen, Andrew Prout, Benjamin Price, Albert Reuther, Antonio Rosa, Matthew L. Weiss, Charles Yee, Daniel Edelman, Allan Vanterpool, Anson Cheng, Vijay Gadepally, Devesh Tiwari |
HPCA | 26 |