RaceWeir: Hardware-Assisted Data Race Detection in Multi-core System

Abstract

As the multicore platform becomes more and more widespread programmers usually adopt multithreaded programing to improve the performance However multithreaded applications are prone to cause synchronization bugs such as data races Unfortunately existing software race detectors are slow in performance while the published hardware-assisted race detectors are imprecise in reporting data race events In this thesis we propose two hardware-based data race detection mechanisms to catch synchronization bugs in a multicore system We first develop a hardware implementation of a hybrid-based data race detection mechanism based on the lockset and happens-before algorithms We find that the way this hybrid detection mechanism handles the barriers adversely affects the detection capability of the happens-before We remove most of the storage and mechanism in lockset and only keep the lock state and access ID for each variable Based on this we propose the improved happens-before detection mechanism which is proved to be a precise data race detector and has less cache area requirement To be precise in data race detection we also develop the backup and restore technique to eliminate the cache eviction problem for the cached metadata We evaluate the two proposed detection mechanisms by ten SPLASH2 benchmarks Through the simulations we have found the existing races in SPLASH2 benchmarks and our method has significantly enhanced the detection capability due to the employment of the backup and restore technique The improved happens-before detection mechanisms has caused about 1 22x-2 55x of overheads compared to the normal execution time

Date of Award	2014 Aug 18
Original language	English
Supervisor	Chung-Ho Chen (Supervisor)