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Mingun Cho

dblp:339/0724 · DBLP profile ↗
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2ranked-venue papers
0as first author
2since 2021 · last 2023
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Software engineering, systems software and programming languages · 2 · 2 since 2021Systems, 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
2 papers
Memory systems · 67% GPUs and heterogeneous computing · 33%
Software engineering, system software, and programming languages
1 paper
Software testing · 50% Concurrent programming · 50%

Topics — the 5 heaviest of 5, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Software testing
differential testing
0.712023
MC Mutants: Evaluating and Improving Testing for Memory Consistency Specifications · ASPLOS (2) 2023
Concurrent programming › memory models
memory model testing
0.712023
MC Mutants: Evaluating and Improving Testing for Memory Consistency Specifications · ASPLOS (2) 2023
GPUs and heterogeneous computing › GPU memory
GPU memory consistency
0.712023
GPUHarbor: Testing GPU Memory Consistency at Large (Experience Paper) · ISSTA 2023
Memory systems
memory consistency
0.712023
GPUHarbor: Testing GPU Memory Consistency at Large (Experience Paper) · ISSTA 2023
Memory systems › memory consistency
memory consistency model
0.712023
MC Mutants: Evaluating and Improving Testing for Memory Consistency Specifications · ASPLOS (2) 2023

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

litmus testing · 0.7
YearPublicationVenuePosition
2023 MC Mutants: Evaluating and Improving Testing for Memory Consistency Specifications
abstract
Shared memory platforms provide a memory consistency specification (MCS) so that developers can reason about the behaviors of their parallel programs. Unfortunately, ensuring that a platform conforms to its MCS is difficult, as is exemplified by numerous bugs in well-used platforms. While existing MCS testing approaches find bugs, their efficacy depends on the testing environment (e.g. if synthetic memory pressure is applied). MCS testing environments are difficult to evaluate since legitimate MCS violations are too rare to use as an efficacy metric. As a result, prior approaches have missed critical MCS bugs.
Reese Levine, Tianhao Guo, Mingun Cho, Alan Baker, Raph Levien, David Neto, Andrew Quinn 0001, Tyler Sorensen 0001
ASPLOS (2)3
2023 GPUHarbor: Testing GPU Memory Consistency at Large (Experience Paper)
abstract
Memory consistency specifications (MCSs) are a difficult, yet critical, part of a concurrent programming framework. Existing MCS testing tools are not immediately accessible, and thus, have only been applied to a limited number of devices. However, in the post-Dennard scaling landscape, there has been an explosion of new architectures and frameworks. Studying the shared memory behaviors of these new platforms is important to understand their behavior and ensure conformance to framework specifications.
Reese Levine, Mingun Cho, Devon McKee, Andrew Quinn 0001, Tyler Sorensen 0001
ISSTA2