DataRaceBench a benchmark suite for systematic evaluation of data race detection tools

Data races in multi-threaded parallel applications are notoriously damaging while extremely difficult to detect. Many tools have been developed to help programmers find data races. However, there is no dedicated OpenMP benchmark suite to systematically evaluate data race detection tools for their st...

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Bibliographic Details
Published in:International Conference for High Performance Computing, Networking, Storage and Analysis (Online) pp. 1 - 14
Main Authors: Liao, Chunhua, Lin, Pei-Hung, Asplund, Joshua, Schordan, Markus, Karlin, Ian
Format: Conference Proceeding
Language:English
Published: New York, NY, USA ACM 12.11.2017
Series:ACM Conferences
Subjects:
Benchmark Suite
Computing methodologies > Parallel computing methodologies > Parallel programming languages
Data Race Detection
Instruction sets
Measurement
OpenMP
Parallel programming
Runtime
Software and its engineering > Software creation and management > Software verification and validation
Software and its engineering > Software organization and properties > Software functional properties > Correctness
Static analysis
Systematics
Waste materials
data race detection
benchmark suite
openMP
ISBN:9781450351140, 145035114X
ISSN:2167-4337
Online Access:Get full text
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Summary:Data races in multi-threaded parallel applications are notoriously damaging while extremely difficult to detect. Many tools have been developed to help programmers find data races. However, there is no dedicated OpenMP benchmark suite to systematically evaluate data race detection tools for their strengths and limitations. In this paper, we present DataRaceBench, an open-source benchmark suite designed to systematically and quantitatively evaluate the effectiveness of data race detection tools. We focus on data race detection in programs written in OpenMP, the popular parallel programming model for multi-threaded applications. In particular, DataRaceBench includes a set of microbenchmark programs with or without data races. These microbenchmarks are either manually written, extracted from real scientific applications, or automatically generated optimization variants. We also define several metrics to represent effectiveness and efficiency of data race detection tools. Using DataRaceBench and its metrics, we evaluate four different data race detection tools: Helgrind, ThreadSanitizer, Archer, and Intel Inspector. The evaluation results show that DataRaceBench is effective to provide comparable, quantitative results and discover strengths and weaknesses of the tools being evaluated.
ISBN:9781450351140
145035114X
ISSN:2167-4337
DOI:10.1145/3126908.3126958