Assertion Based Parallel Debugging

Dinh, Minh Ngoc; Abramson, David; Kurniawan, Donny; Jin, Chao; Moench, Bob; DeRose, Luiz

doi:10.1109/ccgrid.2011.44

Cited by 12 publications

(2 citation statements)

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“…Recent work addressed scalability of parallel assertionbased debugging [11] but does not suit localization of performance faults. Differential debugging [4] provides a semiautomated approach to understand programming errors; it dynamically compares correct and incorrect runs.…”

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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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: 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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“…Even though these techniques are quite promising, it is difficult to immediately apply those to debug parallel applications at large scale. Some formal verification based tools [33] and assertion based techniques [20] can overcome scalability challenges and adapt to parallel applications. However, these tools are mainly suited for debugging accuracy problems and are complementary to our approach.…”

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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