Fault Detection in Multi-Threaded C++ Server Applications

Mühlenfeld, Arndt; Wotawa, Franz

doi:10.1016/j.entcs.2007.04.004

Cited by 13 publications

(8 citation statements)

References 17 publications

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“…It remembers which locks are held when each memory location is accessed, and can thus detect when a memory location is accessed without a consistent lock-set, which may imply a data race. VisualThreads [5] and Helgrind [10] are similar. DRD [19] is another race detector that uses a different race-detection algorithm.…”

Section: Shadow Memory Is Usefulmentioning

confidence: 62%

How to shadow every byte of memory used by a program

Nethercote

Seward

2007

Proceedings of the 3rd International Conference on Virtual Execution Environments

188

130

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Several existing dynamic binary analysis tools use shadow memory-they shadow, in software, every byte of memory used by a program with another value that says something about it. Shadow memory is difficult to implement both efficiently and robustly. Nonetheless, existing shadow memory implementations have not been studied in detail. This is unfortunate, because shadow memory is powerful-for example, some of the existing tools that use it detect critical errors such as bad memory accesses, data races, and uses of uninitialised or untrusted data.In this paper we describe the implementation of shadow memory in Memcheck, a popular memory checker built with Valgrind, a dynamic binary instrumentation framework. This implementation has several novel features that make it efficient: carefully chosen data structures and operations result in a mean slow-down factor of only 22.2 and moderate memory usage. This may sound slow, but we show it is 8.9 times faster and 8.5 times smaller on average than a naive implementation, and shadow memory operations account for only about half of Memcheck's execution time. Equally importantly, unlike some tools, Memcheck's shadow memory implementation is robust: it is used on Linux by thousands of programmers on sizeable programs such as Mozilla and OpenOffice, and is suited to almost any memory configuration. This is the first detailed description of a robust shadow memory implementation, and the first detailed experimental evaluation of any shadow memory implementation. The ideas within are applicable to any shadow memory tool built with any instrumentation framework.

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Section: Shadow Memory Is Usefulmentioning

confidence: 62%

How to shadow every byte of memory used by a program

Nethercote

Seward

2007

Proceedings of the 3rd International Conference on Virtual Execution Environments

188

130

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“…Our artificial data races should also work with different thread checking tools like the SUN Thread Analyzer [11] or Helgrind [16]. Furthermore, this topic also has consequences for the deadlock detection provided by Umpire [6].…”

Section: Related Workmentioning

confidence: 99%

MPI Correctness Checking for OpenMP/MPI Applications

Hilbrich

Müller

Krammer

2009

Int J Parallel Prog

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The MPI interface is the de-facto standard for message passing applications, but it is also complex and defines several usage patterns as erroneous. A current trend is the investigation of hybrid programming techniques that use MPI processes and multiple threads per process. As a result, more and more MPI implementations support multi-threading, which are restricted by several rules of the MPI standard. In order to support developers of hybrid MPI applications, we present extensions to the MPI correctness checking tool Marmot. Basic extensions make it aware of Open-MP multi-threading, while further ones add new correctness checks. As a result, it is possible to detect errors that actually occur in a run with Marmot. However, some errors only occur for certain execution orders, thus, we present a novel approach using artificial data races, which allows us to employ thread checking tools, e.g., Intel Thread Checker, to detect MPI usage errors.

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“…This makes the development and debugging of a dynamic analysis framework and of its derived tools easier. Serialization should not be seen as a crucial limitation of our implementation: for instance Helgrind [19,28] and DRD [28], two popular tools for detecting synchronization errors in programs that use the POSIX pthreads primitives, are both based on Valgrind. Serialization implies that our profiler does not need to perform tie breaking of events (see Section 4).…”

Section: Instrumentationmentioning

confidence: 99%

Input-sensitive profiling

2012

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Input-sensitive profiling is a recent performance analysis technique that makes it possible to estimate the empirical cost function of individual routines of a program, helping developers understand how performance scales to larger inputs and pinpoint asymptotic bottlenecks in the code. A current limitation of input-sensitive profilers is that they specifically target sequential computations, ignoring any communication between threads. In this paper we show how to overcome this limitation, extending the range of applicability of the original approach to multithreaded applications and to applications that operate on I/O streams. We develop new metrics for automatically estimating the size of the input given to each routine activation, addressing input produced by nondeterministic memory stores performed by other threads as well as by the OS kernel (e.g., in response to I/O or network operations). We provide real case studies, showing that our extension allows it to characterize the behavior of complex applications more precisely than previous approaches. An extensive experimental investigation on a variety of benchmark suites (including the SPEC OMP2012 and the PARSEC benchmarks) shows that our Valgrind-based input-sensitive profiler incurs an overhead comparable to other prominent heavyweight analysis tools, while collecting significantly more performance points from each profiling session and correctly characterizing both thread-induced and external input.

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Fault Detection in Multi-Threaded C++ Server Applications

Cited by 13 publications

References 17 publications

How to shadow every byte of memory used by a program

How to shadow every byte of memory used by a program

MPI Correctness Checking for OpenMP/MPI Applications

Input-sensitive profiling

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