Concurrent Deadlock Detection In Parallel Programs

Haque, Waqar

doi:10.1080/1206212x.2006.11441784

Cited by 14 publications

(7 citation statements)

References 4 publications

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“…PDG Versus Other Dependency Graphs: A PDG is different from the dependency graph used in MPI deadlock detection [17,18,32]. A PDG hierarchically describes the execution progress of MPI tasks.…”

Section: Progress Dependence Graphmentioning

confidence: 99%

“…AutomaDeD [23] and Mirgorodskiy et al [24] monitor the application's timing behaviors and focus the developer on tasks and code regions that are unusual. Other tools target specific error types, such as memory leaks [16] or MPI coding errors [13,14,17,18,32]. These tools are complimentary to AutomaDeD as they can detect a problem and trigger AutomaDeD's diagnosis mechanisms.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Probabilistic diagnosis of performance faults in large-scale parallel applications

Laguna

Ahn

Supinski

et al. 2012

Proceedings of the 21st International Conference on Parallel Architectures and Compilation Techniques

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Debugging large-scale parallel applications is challenging. Most existing techniques provide mechanisms for process control but little information about the causes of failures. Most debuggers also scale poorly despite continued growth in supercomputer core counts. Our novel, highly scalable tool helps developers to understand and to fix performance failures and correctness problems at scale. Our tool probabilistically infers the least progressed task in MPI programs using Markov models of execution history and dependence analysis. This analysis guides program slicing to find code that may have caused a failure. In a blind study, we demonstrate that our tool can isolate the root cause of a particularly perplexing bug encountered at scale in a molecular dynamics simulation. Further, we perform fault injections into two benchmark codes and measure the scalability of the tool. Our results show that it accurately detects the least progressed task in most cases and can perform the diagnosis in a fraction of a second with thousands of tasks.

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Section: Progress Dependence Graphmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Probabilistic diagnosis of performance faults in large-scale parallel applications

Laguna

Ahn

Supinski

et al. 2012

Proceedings of the 21st International Conference on Parallel Architectures and Compilation Techniques

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“…DMTracker [22] uses statistical technique to find bugs in MPI applications by identifying anomalous data movements. There are other techniques [21,24] that target general MPI coding errors and deadlock detection. These tools are also complimentary to our approach and can be used to detect a problem and trigger PRODOMETER for further analysis.…”

Section: Related Workmentioning

confidence: 99%

Accurate application progress analysis for large-scale parallel debugging

Mitra

Laguna

Ahn

et al. 2014

Proceedings of the 35th ACM SIGPLAN Conference on Programming Language Design and Implementation

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Debugging large-scale parallel applications is challenging. In most HPC applications, parallel tasks progress in a coordinated fashion, and thus a fault in one task can quickly propagate to other tasks, making it difficult to debug. Finding the least-progressed tasks can significantly reduce the effort to identify the task where the fault originated. However, existing approaches for detecting them suffer low accuracy and large overheads; either they use imprecise static analysis or are unable to infer progress dependence inside loops. We present a loop-aware progress-dependence analysis tool, PRODOMETER, which determines relative progress among parallel tasks via dynamic analysis. Our fault-injection experiments suggest that its accuracy and precision are over 90% for most cases and that it scales well up to 16,384 MPI tasks. Further, our case study shows that it significantly helped diagnosing a perplexing error in MPI, which only manifested at large scale.

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“…Parallel debuggers operate similarly as sequential debuggers [36,31,30,28,4], but can be difficult to use effectively when there are hundreds of processes. Automatic checking tools address the drawbacks of manual checking in parallel debuggers [38,12,19,7,37]. These tools use similar rules as static analysis, but they verify the rules at runtime rather than compile time.…”

Section: Related Workmentioning

confidence: 99%

MPIWiz

Xue

Liu

et al. 2009

Proceedings of the 14th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming

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Message Passing Interface (MPI) is a widely used standard for managing coarse-grained concurrency on distributed computers. Debugging parallel MPI applications, however, has always been a particularly challenging task due to their high degree of concurrent execution and non-deterministic behavior. Deterministic replay is a potentially powerful technique for addressing these challenges, with existing MPI replay tools adopting either data-replay or order-replay approaches. Unfortunately, each approach has its tradeoffs. Data-replay generates substantial log sizes by recording every communication message. Order-replay generates small logs, but requires all processes to be replayed together. We believe that these drawbacks are the primary reasons that inhibit the wide adoption of deterministic replay as the critical enabler of cyclic debugging of MPI applications.This paper describes subgroup reproducible replay (SRR), a hybrid deterministic replay method that provides the benefits of both data-replay and order-replay while balancing their trade-offs. SRR divides all processes into disjoint groups. It records the contents of messages crossing group boundaries as in data-replay, but records just message orderings for communication within a group as in order-replay. In this way, SRR can exploit the communication locality of traffic patterns in MPI applications. During replay, developers can then replay each group individually. SRR reduces recording overhead by not recording intra-group communication, and reduces replay overhead by limiting the size of each replay group. Exposing these tradeoffs gives the user the necessary control for making deterministic replay practical for MPI applications.We have implemented a prototype, MPIWIZ, to demonstrate and evaluate SRR. MPIWIZ employs a replay framework that allows transparent binary instrumentation of both library and system calls. As a result, MPIWIZ replays MPI applications with no source code modification and relinking, and handles nondeterminism in both MPI and OS system calls. Our preliminary results show that MPIWIZ can reduce recording overhead by over a factor of four relative to data-replay, yet without requiring the entire application to be replayed as in order-replay. Recording increases execution time by 27% while the application can be replayed in just 53% of its base execution time.

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Concurrent Deadlock Detection In Parallel Programs

Cited by 14 publications

References 4 publications

Probabilistic diagnosis of performance faults in large-scale parallel applications

Probabilistic diagnosis of performance faults in large-scale parallel applications

Accurate application progress analysis for large-scale parallel debugging

MPIWiz

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