OpenMP aware MHP Analysis for Improved Static Data-Race Detection

Bora, Utpal and Vaishay, Shraiysh and Joshi, Saurabh and Upadrasta, Ramakrishna (2021) OpenMP aware MHP Analysis for Improved Static Data-Race Detection. In: 7th IEEE/ACM Annual Workshop on the LLVM Compiler Infrastructure in HPC, LLVM-HPC 2021, St. Louis.

Text
Proceedings_of_LLVM_HPC_2021.pdf - Published Version
Available under License Creative Commons Attribution.
Download (705kB)

Abstract

Data races, a major source of bugs in concurrent programs, can result in loss of manpower and time as well as data loss due to system failures. OpenMP, the de facto shared memory parallelism framework used in the HPC community, also suffers from data races. To detect race conditions in OpenMP programs and improve turnaround time and/or developer productivity, we present a data flow analysis based, fast, static data race checker in the LLVM compiler framework. Our tool can detect races in the presence or absence of explicit barriers, with implicit or explicit synchronization. In addition, our tool effectively works for the OpenMP target offloading constructs and also supports the frequently used OpenMP constructs.We formalize and provide a data flow analysis framework to perform Phase Interval Analysis (PIA) of OpenMP programs. Phase intervals are then used to compute the MHP (and its complement NHP) sets for the programs, which, in turn, are used to detect data races statically.We evaluate our work using multiple OpenMP race detection benchmarks and real world applications. Our experiments show that the checker is comparable to the state-of-The-Art in various performance metrics with around 90% accuracy, almost perfect recall, and significantly lower runtime and memory footprint. © 2021 IEEE.

[error in script]

IITH Creators:

IITH Creators	ORCiD
Upadrasta, Ramakrishna	https://orcid.org/0000-0002-5290-3266

Item Type:

Conference or Workshop Item (Paper)

Additional Information:

This research is funded by the Department of Electronics & Information Technology and the Ministry of Communications & Information Technology, Government of India. This work is partially supported by a Visvesvaraya PhD Scheme under the MEITY, GoI (PhD-MLA/04(02)/2015-16), an NSM research grant (MeitY/R&D/HPC/2(1)/2014), a Visvesvaraya Young Faculty Research Fellowship from MeitY, and a faculty research grant from AMD.

Uncontrolled Keywords:

Data Races; LLVM; MHP; OpenMP; Phase Interval Analysis; Program Analysis; Static Analysis

Subjects:

Computer science

Divisions:

Department of Computer Science & Engineering

Depositing User:

. LibTrainee 2021

Date Deposited:

12 Sep 2022 10:02

Last Modified:

12 Sep 2022 10:02