OpenMP aware MHP Analysis for Improved Static Data-Race Detection

TL;DR

This paper introduces a fast, static data race detection tool for OpenMP programs within LLVM, capable of identifying data races with high accuracy, low runtime, and supporting various OpenMP constructs and offloading features.

Contribution

It presents a novel data flow analysis framework using Phase Interval Analysis to detect data races in OpenMP programs efficiently and accurately.

Findings

Achieves around 90% accuracy in race detection.

Offers almost perfect recall of data races.

Demonstrates significantly lower runtime and memory usage compared to existing tools.

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.