PARSNIP performant architecture for race safety with no impact on precision

Data race detection is a useful dynamic analysis for multithreaded programs that is a key building block in record-and-replay, enforcing strong consistency models, and detecting concurrency bugs. Existing software race detectors are precise but slow, and hardware support for precise data race detect...

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Bibliographic Details
Published in:MICRO-50 : the 50th annual IEEE/ACM International Symposium on Microarchitecture : proceedings : October 14-18, 2017, Cambridge, MA pp. 490 - 502
Main Authors: Peng, Yuanfeng, Wood, Benjamin P., Devietti, Joseph
Format: Conference Proceeding
Language:English
Published: New York, NY, USA ACM 14.10.2017
Series:ACM Conferences
Subjects:
Clocks
Computer systems organization > Architectures > Parallel architectures > Multicore architectures
data race detection
Detectors
Hardware
hardware support
History
Metadata
multithreaded programming
Safety
Software
Software and its engineering > Software notations and tools > Software maintenance tools
data race detection
hardware support
multithreaded programming
ISBN:1450349528, 9781450349529
ISSN:2379-3155
Online Access:Get full text
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Summary:Data race detection is a useful dynamic analysis for multithreaded programs that is a key building block in record-and-replay, enforcing strong consistency models, and detecting concurrency bugs. Existing software race detectors are precise but slow, and hardware support for precise data race detection relies on assumptions like type safety that many programs violate in practice. We propose Parsnip, a fully precise hardware-supported data race detector. Parsnip exploits new insights into the redundancy of race detection metadata to reduce storage overheads. Parsnip also adopts new race detection metadata encodings that accelerate the common case while preserving soundness and completeness. When bounded hardware resources are exhausted, Parsnip falls back to a software race detector to preserve correctness. Parsnip does not assume that target programs are type safe, and is thus suitable for race detection on arbitrary code. Our evaluation of Parsnip on several PARSEC benchmarks shows that performance overheads range from negligible to 2.6x, with an average overhead of just 1.5x. Moreover, Parsnip outperforms the state-of-the-art Radish hardware race detector by 4.6x.
ISBN:1450349528
9781450349529
ISSN:2379-3155
DOI:10.1145/3123939.3123946