ETRI-Knowledge Sharing Plaform

ENGLISH

성과물

논문 검색
구분 SCI
연도 ~ 키워드

상세정보

학술지 AdaptiveLock: Efficient Hybrid Data Race Detection Based on Real-World Locking Patterns
Cited 3 time in scopus Download 23 time Share share facebook twitter linkedin kakaostory
저자
유미선, 이준상, 배두환
발행일
201912
출처
International Journal of Parallel Programming, v.47 no.5-6, pp.805-837
ISSN
0885-7458
출판사
Springer
DOI
https://dx.doi.org/10.1007/s10766-018-0579-5
협약과제
18HS6400, 인공지능 시스템을 위한 뉴로모픽 컴퓨팅 SW 플랫폼 기술 개발, 김태호
초록
Among the various types of concurrency bugs, the data race is one of the primary causes of other concurrency bugs. Thus, it is important to detect as many data races as possible during the development step of multithreaded programs. A hybrid data race detection technique that uses the Lockset algorithm and happens-before relation, can detect actually occurred and hidden data races in one execution trace. However, high runtime slowdown obstructs the frequent use of hybrid detectors. In this paper, we empirically demonstrate that most data race bugs are caused by the absence of a lock, and that multiple locks are rarely involved in a data race bug in the real world. Thus, we propose a fast hybrid detection algorithm that does not introduce additional false positives and false negatives to the current hybrid detectors. The suggested algorithm replaces the lock-set intersection by a simple comparison operation that focuses on exploring data-race-prone locking patterns. The experimental results indicate that the proposed algorithm detects the same data races as Multilock-HB, which is the most accurate hybrid detector, with a 1.18× slowdown of FastTrack for eight large-scale benchmark programs.
KSP 제안 키워드
Data Race Detection, Detection algorithm, Execution trace, False positives and false negatives, Happens-before relation, Hidden data, Large-scale benchmark, Real-world, concurrency bugs, detection techniques, hybrid data