Accelerating Data Race Detection Utilizing On-Chip Data-Parallel Cores

Mekkat, Vineeth; Holey, Anup; Zhai, Antonia

doi:10.1007/978-3-642-40787-1_12

Cited by 6 publications

(9 citation statements)

References 24 publications

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“…Some runtime error checking approaches require support built into hardware [4,7,14,16,30]. Probabilistic methods allow to deal with uncertainty resulting from less frequent program observation [11].…”

Section: Related Workmentioning

confidence: 99%

Runtime checking C programs

Milewicz

Vanka

Tuck

et al. 2015

Proceedings of the 30th Annual ACM Symposium on Applied Computing

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The C Programming Language is known for being an efficient language that can be compiled on almost any architecture and operating system. However the absence of dynamic safety checks and a relatively weak type system allows programmer oversights that are hard to spot. In this paper, we present RTC, a runtime monitoring tool that instruments unsafe code and monitors the program execution. RTC is built on top of the ROSE compiler infrastructure. RTC finds memory bugs and arithmetic overflows and underflows, and run-time type violations. Most of the instrumentations are directly added to the source file and only require a minimal runtime system. As a result, the instrumented code remains portable. In tests against known error detection benchmarks, RTC found 98% of all memory related bugs and had zero false positives. In performance tests conducted with well known algorithms, such as binary search and MD5, we determined that the unoptimized overhead rate is between a factor of 1.8 and a factor of 77 respectively.

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Section: Related Workmentioning

confidence: 99%

Runtime checking C programs

Milewicz

Vanka

Tuck

et al. 2015

Proceedings of the 30th Annual ACM Symposium on Applied Computing

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“…false negatives). Most of the techniques (34 out of 43) in this survey belong to this class , . For more detail on how each technique constructs an execution model, see Tables –. Static techniques (Seven out of 43 techniques)These techniques utilize static analysis methods such as alias analysis , data flow analysis , dependency analysis and type systems to obtain the information necessary to construct an execution model.…”

Section: Execution Model Of Multithreaded Programmesmentioning

confidence: 99%

“…The data race bug definition in this paper coincides with the definition of ‘data race’ in Netzer et al ., . Many race bug detection techniques , target to detect the data race bugs and implement various methods to detect them efficiently (Section 4.3). The definition of the data race bug using the formal execution model is as follows: Definition A programme has a data race bug if an execution model of the programme 〈Σ, Thread , Mem ,→ S , conflict , S OB , S DA 〉 satisfies all of the following conditions: There exist two operations p , q ∈Σ such that DR1: thread ( p ) ≠ thread ( q ) DR2: operand ( p ) = operand ( q ) DR3: conflict ( p , q ) DR4: p ↛ S q ∧ q ↛ S p …”

Section: Data Race Bug Detection Techniquesmentioning

confidence: 99%

“…The four columns (labelled DR1 through DR4 in the top row) are as follows: The second column describes mechanisms/methods to obtain thread information of an operation p to check the DR1 condition. The techniques with ‘runtime thread identifier’ utilize thread identifiers observed in monitored runtime traces to check if two operations are executed by different threads. The techniques with ‘static approximation’ obtain thread identifiers statically, which may produce imprecise results due to the lack of thread sensitivity in static analyses.…”

Section: Data Race Bug Detection Techniquesmentioning

confidence: 99%

“…For this purpose, Chord exploits data flow analyses, and RELAY utilizes symbolic executions. RccJava exploits a type system to group operations that may access to the same object.The techniques with ‘runtime memory address’ use memory locations observed in runtime traces as operands to check if o p e r a n d ( p ) is the same as o p e r a n d ( q ). Many techniques try to reduce the runtime overhead involved in monitoring all operations.…”

Section: Data Race Bug Detection Techniquesmentioning

confidence: 99%

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A survey of race bug detection techniques for multithreaded programmes

Hong

Kim

2014

Software Testing Verif & Rel

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SummaryAs multithreaded programmes become popular to fully utilize multicore CPUs, many race bug detection techniques have been developed to find concurrency errors in multithreaded programmes effectively. Because these techniques have different views on target programme execution and detect race bugs of various types, it is difficult to characterize, compare and improve race bug detection techniques. This paper presents a formal execution model, which can uniformly represent various views of race bug detection techniques on target programme execution. Then, this paper classifies 43 race bug detection techniques according to their target race bugs. We classify race bugs on whether or not a bug violates operation block specification and/or data association specification. This survey provides researchers with a clear top‐down view of various race bug detection techniques. In addition, the concrete examples of various race bugs in this survey can help field engineers avoid race bugs in their multithreaded programmes. Copyright © 2014 John Wiley & Sons, Ltd.

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