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Haining Tong

dblp:402/7693 · DBLP profile ↗
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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

TopicWeightPapersLastEvidence papers
GPUs and heterogeneous computing › GPU memory
GPU memory consistency
0.812024
Towards Unified Analysis of GPU Consistency · ASPLOS (4) 2024
GPUs and heterogeneous computing › GPU memory
GPU memory model
0.812024
Towards Unified Analysis of GPU Consistency · ASPLOS (4) 2024
Memory systems
memory consistency
0.812024
Towards Unified Analysis of GPU Consistency · ASPLOS (4) 2024
Parallel and multicore computing
memory model
0.812024
Towards Unified Analysis of GPU Consistency · ASPLOS (4) 2024
Programming languages and type systems › language semantics
formal semantics
0.212024
Towards Unified Analysis of GPU Consistency · ASPLOS (4) 2024

Methods — techniques the papers use, named apart from their topics

formal modeling · 1.5
YearPublicationVenuePosition
2024 Towards Unified Analysis of GPU Consistency
abstract
After 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