Demo Data from EDBT 2026 / reviewers in the wild / expert
Pankaj Kukreja
dblp:319/0351
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 2020
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Systems, architecture and hardware · 1
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.
| Software engineering, system software, and programming languages
1 paper |
Concurrent programming · 50% Program analysis · 50% | |
| Computer architecture, parallel and distributed computing, and storage systems
1 paper |
High-performance computing · 100% |
Topics — the 4 heaviest of 5, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Concurrent programming › concurrency bugs
data races |
0.4 | 1 | 2020 | LLOV: A Fast Static Data-Race Checker for OpenMP Programs · ACM Trans. Archit. Code Optim. 2020 |
Concurrent programming › concurrency bug detection
data race detection |
0.4 | 1 | 2020 | LLOV: A Fast Static Data-Race Checker for OpenMP Programs · ACM Trans. Archit. Code Optim. 2020 |
Program analysis
static analysis |
0.4 | 1 | 2020 | LLOV: A Fast Static Data-Race Checker for OpenMP Programs · ACM Trans. Archit. Code Optim. 2020 |
Program analysis › concurrent program analysis
static race detection |
0.4 | 1 | 2020 | LLOV: A Fast Static Data-Race Checker for OpenMP Programs · ACM Trans. Archit. Code Optim. 2020 |
Methods — techniques the papers use, named apart from their topics
LLVM compiler framework · 0.9
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2020 | LLOV: A Fast Static Data-Race Checker for OpenMP ProgramsabstractIn the era of Exascale computing, writing efficient parallel programs is indispensable, and, at the same time, writing sound parallel programs is very difficult. Specifying parallelism with frameworks such as OpenMP is relatively easy, but data races in these programs are an important source of bugs. In this article, we propose LLOV, a fast, lightweight, language agnostic, and static data race checker for OpenMP programs based on the LLVM compiler framework. We compare LLOV with other state-of-the-art data race checkers on a variety of well-established benchmarks. We show that the precision, accuracy, and the F1 score of LLOV is comparable to other checkers while being orders of magnitude faster. To the best of our knowledge, LLOV is the only tool among the state-of-the-art data race checkers that can verify a C/C++ or FORTRAN program to be data race free. Utpal Bora 0001, Pankaj Kukreja, Saurabh Joshi 0001, Ramakrishna Upadrasta, Sanjay V. Rajopadhye |
ACM Trans. Archit. Code Optim. | 3 |