Demo Data from EDBT 2026 / reviewers in the wild / expert
Haining Tong
dblp:402/7693
· DBLP profile ↗
1ranked-venue papers
1as first author
1since 2021 · last 2024
0009-0002-3702-4754ORCID · reported
Domains — the database's venue-derived domains; a paper can count in several
Systems, architecture and hardware · 1 · 1 first-author · 1 since 2021Software engineering, systems software and programming languages · 1 · 1 first-author · 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% Memory systems · 25% Parallel and multicore computing · 25% | |
| Software engineering, system software, and programming languages
1 paper |
Programming languages and type systems · 100% |
Topics — the 5 heaviest of 5, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
GPUs and heterogeneous computing › GPU memory
GPU memory consistency |
0.8 | 1 | 2024 | Towards Unified Analysis of GPU Consistency · ASPLOS (4) 2024 |
GPUs and heterogeneous computing › GPU memory
GPU memory model |
0.8 | 1 | 2024 | Towards Unified Analysis of GPU Consistency · ASPLOS (4) 2024 |
Memory systems
memory consistency |
0.8 | 1 | 2024 | Towards Unified Analysis of GPU Consistency · ASPLOS (4) 2024 |
Parallel and multicore computing
memory model |
0.8 | 1 | 2024 | Towards Unified Analysis of GPU Consistency · ASPLOS (4) 2024 |
Programming languages and type systems › language semantics
formal semantics |
0.2 | 1 | 2024 | Towards Unified Analysis of GPU Consistency · ASPLOS (4) 2024 |
Methods — techniques the papers use, named apart from their topics
formal modeling · 1.5
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2024 | Towards Unified Analysis of GPU ConsistencyabstractAfter more than 30 years of research, there is a solid understanding of the consistency guarantees given by CPU systems. Unfortunately, the same is not yet true for GPUs. The growing popularity of general purpose GPU programming has been a call for action which industry players like Nvidia and Khronos have answered by formalizing their Ptx and Vulkan consistency models. These models give precise answers to questions about program's correctness. However, interpreting them still requires a level of expertise that escapes most developers, and the current tool support is insufficient. Haining Tong, Natalia Gavrilenko, Hernán Ponce de León, Keijo Heljanko |
ASPLOS (4) | 1 |