Demo Data from EDBT 2026 / reviewers in the wild / expert
Marcello Maggioni
dblp:292/4246
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2023
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Systems, architecture and hardware · 1 · 1 since 2021Software engineering, systems software and programming languages · 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.
| Artificial intelligence
1 paper |
Efficient and distributed learning · 87% Language models and text generation · 13% | |
| Computer architecture, parallel and distributed computing, and storage systems
1 paper |
Parallel and multicore computing · 50% Distributed systems · 50% |
Topics — the 5 heaviest of 5, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Machine learning › Efficient and distributed learning
distributed training |
0.7 | 1 | 2023 | Overlap Communication with Dependent Computation via Decomposition in Large Deep Learning Models · ASPLOS (1) 2023 |
Machine learning › Efficient and distributed learning › distributed training
model parallelism |
0.7 | 1 | 2023 | Overlap Communication with Dependent Computation via Decomposition in Large Deep Learning Models · ASPLOS (1) 2023 |
Distributed systems › communication optimization
communication-computation overlap |
0.7 | 1 | 2023 | Overlap Communication with Dependent Computation via Decomposition in Large Deep Learning Models · ASPLOS (1) 2023 |
Parallel and multicore computing
parallel programming models |
0.7 | 1 | 2023 | Overlap Communication with Dependent Computation via Decomposition in Large Deep Learning Models · ASPLOS (1) 2023 |
Natural language and speech › Language models and text generation
large language model |
0.2 | 1 | 2023 | Overlap Communication with Dependent Computation via Decomposition in Large Deep Learning Models · ASPLOS (1) 2023 |
Methods — techniques the papers use, named apart from their topics
intra-layer model parallelism · 1.3computation decomposition · 1.3
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2023 | Overlap Communication with Dependent Computation via Decomposition in Large Deep Learning ModelsabstractLarge deep learning models have shown great potential with state-of-the-art results in many tasks. However, running these large models is quite challenging on an accelerator (GPU or TPU) because the on-device memory is too limited for the size of these models. Intra-layer model parallelism is an approach to address the issues by partitioning individual layers or operators across multiple devices in a distributed accelerator cluster. But, the data communications generated by intra-layer model parallelism can contribute to a significant proportion of the overall execution time and severely hurt the computational efficiency. Jinliang Wei, Amit Sabne, Andy Davis, Berkin Ilbeyi, Blake Hechtman, Dehao Chen, Karthik Srinivasa Murthy, Marcello Maggioni, Tongfei Guo, Yuanzhong Xu, Zongwei Zhou |
ASPLOS (1) | 9 |