Paper Info
Title
SWORD: A Bounded Memory-Overhead Detector of OpenMP Data Races in Production Runs
Abstract
The detection and elimination of data races in largescale OpenMP programs is of critical importance. Unfortunately, today's state-of-the-art OpenMP race checkers suffer from high memory overheads and/or miss races. In this paper, we present SWORD, a data race detector that significantly improves upon these limitations. SWORD limits the application slowdown and memory usage by utilizing only a bounded, user-adjustable memory buffer to collect targeted memory accesses. When the buffer fills up, the accesses are compressed and flushed to a file system for later offline analysis. SWORD builds on an operational semantics that formally captures the notion of concurrent accesses within OpenMP regions. An offline race checker that is driven by these semantic rules allows SWORD to improve upon happens-before techniques that are known to mask races. To make its offline analysis highly efficient and scalable, SWORD employs effective self-balancing interval-tree-based algorithms. Our experimental results demonstrate that SWORD is capable of detecting races even within programs that use over 90% of the memory on each compute node. Further, our evaluation shows that it matches or exceeds the best available dynamic OpenMP race checker in detection capability while remaining efficient in execution time.
Year
DOI
Venue
2018
10.1109/IPDPS.2018.00094
2018 IEEE International Parallel and Distributed Processing Symposium (IPDPS)
Keywords
Field
DocType
Dynamic Data Race Detection,Concurrency Bugs,Data Races,OpenMP,High Performance Computing,HPC,Offline Analysis
File system,Supercomputer,High memory,Instruction set,Computer science,Parallel computing,Concurrent computing,Memory buffer register,SWORD,Scalability
Conference
ISSN
ISBN
Citations 
1530-2075
978-1-5386-4369-3
2
PageRank 
References 
Authors
0.40
19
6
Name
Order
Citations
PageRank
Simone Atzeni1322.68
Ganesh Gopalakrishnan21619130.11
Zvonimir Rakamaric332721.22
Ignacio Laguna423924.56
Gregory L. Lee519914.30
Dong H. Ahn632522.61