VLDB 2026 Research / reviewers in the wild / expert
Sung Kook Kim
dblp:248/9285
· DBLP profile ↗
3ranked-venue papers
3as first author
1since 2021 · last 2025
0000-0001-8281-3379ORCID · corroborated
Domains — the database's venue-derived domains; a paper can count in several
Software engineering, systems software and programming languages · 2 · 2 first-authorTheory of computation · 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.
| Software engineering, system software, and programming languages
1 paper |
Program analysis · 100% | |
| Computer architecture, parallel and distributed computing, and storage systems
1 paper |
Parallel and multicore computing · 100% |
Topics — the 5 heaviest of 6, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Program analysis › static analysis
abstract interpretation |
0.4 | 1 | 2020 | Deterministic parallel fixpoint computation · Proc. ACM Program. Lang. 2020 |
Program analysis › static analysis › abstract interpretation
fixpoint computation |
0.4 | 1 | 2020 | Deterministic parallel fixpoint computation · Proc. ACM Program. Lang. 2020 |
Program analysis
static analysis |
0.4 | 1 | 2020 | Deterministic parallel fixpoint computation · Proc. ACM Program. Lang. 2020 |
Parallel and multicore computing › parallel algorithms › parallel algorithm design
deterministic parallel algorithms |
0.4 | 1 | 2020 | Deterministic parallel fixpoint computation · Proc. ACM Program. Lang. 2020 |
Parallel and multicore computing
parallel algorithms |
0.4 | 1 | 2020 | Deterministic parallel fixpoint computation · Proc. ACM Program. Lang. 2020 |
Methods — techniques the papers use, named apart from their topics
weak partial order · 0.9abstract interpretation · 0.9
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2025 | Memory-efficient fixpoint computationabstractAbstract Practical adoption of static analysis often requires trading precision for performance. This paper focuses on improving the memory efficiency of abstract interpretation without sacrificing precision or time efficiency. Computationally, abstract interpretation reduces the problem of inferring program invariants to computing a fixpoint of a set of equations. This paper presents a method to minimize the memory footprint in Bourdoncle’s iteration strategy, a widely-used technique for fixpoint computation. Our technique is agnostic to the abstract domain used. We prove that our technique is optimal (i.e., it results in minimum memory footprint) for Bourdoncle’s iteration strategy while computing the same result. We evaluate the efficacy of our technique by implementing it in a tool called $$\textsc {Mikos}$$ M I K O S , which extends the state-of-the-art abstract interpreter $$\text {IKOS}$$ IKOS . On average $$\textsc {Mikos}$$ M I K O S demonstrated a $$24.57\times $$ 24.57 × and $$2.29\times $$ 2.29 × reduction in peak-memory usage compared to $$\text {IKOS}$$ IKOS when verifying user-provided assertions and performing interprocedural buffer-overflow analysis, respectively. Sung Kook Kim, Arnaud Venet, Aditya V. Thakur |
Formal Methods Syst. Des. | 1 |
| 2020 | Memory-Efficient Fixpoint Computation
Sung Kook Kim, Arnaud Venet, Aditya V. Thakur |
SAS | 1 |
| 2020 | Deterministic parallel fixpoint computationabstractAbstract interpretation is a general framework for expressing static program analyses. It reduces the problem of extracting properties of a program to computing an approximation of the least fixpoint of a system of equations. The de facto approach for computing this approximation uses a sequential algorithm based on weak topological order (WTO). This paper presents a deterministic parallel algorithm for fixpoint computation by introducing the notion of weak partial order (WPO). We present an algorithm for constructing a WPO in almost-linear time. Finally, we describe Pikos, our deterministic parallel abstract interpreter, which extends the sequential abstract interpreter IKOS. We evaluate the performance and scalability of Pikos on a suite of 1017 C programs. When using 4 cores, Pikos achieves an average speedup of 2.06x over IKOS, with a maximum speedup of 3.63x. When using 16 cores, Pikos achieves a maximum speedup of 10.97x. Sung Kook Kim, Arnaud Venet, Aditya V. Thakur |
Proc. ACM Program. Lang. | 1 |