Fine-Grained Characterization of Faults Causing Long Latency Crashes in Programs

Li, Guanpeng; Lu, Qining; Pattabiraman, Karthik

doi:10.1109/dsn.2015.36

Cited by 19 publications

(10 citation statements)

References 27 publications

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“…The instructions used to check a loop conditional increases the segmentation fault latency slightly before the induction variable is used as part of a load/store during the subsequent iteration. Segmentation fault latencies for these applications are consistent with those reported for the Linux kernel [20] and other benchmarks [30]. Figure 6 shows the bit locations that generated a segmentation fault.…”

Section: Micro-level Propagationsupporting

confidence: 81%

“…Prior work has explored how deviations from bit-flips in floatingpoint computation impact on convergence properties [16,17]. Other work has used corruption propagation in the development of lowlevel instruction based detection schemes that check invariants or create SDC detectors inside the compiler [23,38] or utilize code replication to detect corruption in instructions that are likely lead to and propagate corrupted state [18,27,28], and understanding long latency crashes [30,45]. This paper combines the latency of programmatic systems of corruption -e.g., segfaults, detection, control flow divergence, with corruption propagation in state variables to discuss the impact of detection latency on recovery options.…”

mentioning

confidence: 99%

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Towards a More Complete Understanding of SDC Propagation

Calhoun

Snir

Olson

et al. 2017

Proceedings of the 26th International Symposium on High-Performance Parallel and Distributed Computing

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With the rate of errors that can silently effect an application's state/output expected to increase on future HPC machines, numerous application-level detection and recovery schemes have been proposed. Recovery is more efficient when errors are contained and affect only part of the computation's state. Containment is usually achieved by verifying all information leaking out of a statically defined containment domain, which is an expensive procedure. Alternatively, error propagation can be analyzed to bound the domain that is affected by a detected error. This paper investigates how silent data corruption (SDC) due to soft errors propagates through three HPC applications: HPCCG, Jacobi, and CoMD. To allow for more detailed view of error propagation, the paper tracks propagation at the instruction and application variable level. The impact of detection latency on error propagation is shown along with an application's ability to recover. Finally, the impact of compiler optimizations are explored along with the impact of local problem size on error propagation.

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Section: Micro-level Propagationsupporting

confidence: 81%

mentioning

confidence: 99%

Towards a More Complete Understanding of SDC Propagation

Calhoun

Snir

Olson

et al. 2017

Proceedings of the 26th International Symposium on High-Performance Parallel and Distributed Computing

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“…Mutation testing has a long history in the context of fault injection tools [15]. Mutations typically target the source code, while other fault injection techniques target the compiler [16]. The developers of the Devil device driver interface definition language used mutations in the device driver interface to show that their language checker was able to detect erroneous specifications before they were deployed into an operating system kernel [17].…”

Section: Related Workmentioning

confidence: 99%

JMake: Dependable Compilation for Kernel Janitors

Lawall¹,

Muller²

2017

2017 47th Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN)

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The Linux kernel is highly configurable, and thus, in principle, any line of code can be included or excluded from the compiled kernel based on configuration operations. Configurability complicates the task of a kernel janitor, who cleans up faults across the code base. A janitor may not be familiar with the configuration options that trigger compilation of a particular code line, leading him to believe that a fix has been compile-checked when this is not the case. We propose JMake, a mutation-based tool for signaling changed lines that are not subjected to the compiler. JMake shows that for most of the 12,000 file-modifying commits between Linux v4.3 and v4.4 the configuration chosen by the kernel allyesconfig option is sufficient, once the janitor chooses the correct architecture. For most commits, this check requires only 30 seconds or less. We then characterize the situations in which changed code is not subjected to compilation in practice.

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“…Relyzer [45] aims to evaluate the effectiveness of software symptom-based error detection techniques and to identify all the SDCs [46,47]. Relyzer injects faults only at the software level (architectural registers and output of load/store address generation units), without considering microarchitecture level masking and its features (flushes, store forwarding, dead instructions, cache write backs etc.)…”

Section: Related Workmentioning

confidence: 99%

“…Control-equivalence: Software analysis using basic blocks tracks the control flow paths of all the dynamic instances of all the static instructions to separate Masked from SDC faults [46]. For each path Relyzer randomly chooses only one pilot.…”

Section: Analysis Of Relyzer's Heuristicsmentioning

confidence: 99%

MeRLiN

Kaliorakis

Gizopoulos

Canal

et al. 2017

SIGARCH Comput. Archit. News

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Early reliability assessment of hardware structures using microarchitecture level simulators can effectively guide major error protection decisions in microprocessor design. Statistical fault injection on microarchitectural structures modeled in performance simulators is an accurate method to measure their Architectural Vulnerability Factor (AVF) but requires excessively long campaigns to obtain high statistical significance. We propose MeRLiN 1 , a methodology to boost microarchitecture level injection-based reliability assessment by several orders of magnitude and keep the accuracy of the assessment unaffected even for large injection campaigns with very high statistical significance. The core of MeRLiN is the grouping of faults of an initial list in equivalent classes. All faults in the same group target equivalent vulnerable intervals of program execution ending up to the same static instruction that reads the faulty entries. Faults in the same group occur in different times and entries of a structure and it is extremely likely that they all have the same effect in program execution; thus, fault injection is performed only on a few representatives from each group. We evaluate MeRLiN for different sizes of the physical register file, the store queue and the first level data cache of a contemporary microarchitecture running MiBench and SPEC CPU2006 benchmarks. For all our experiments, MeRLiN is from 2 to 3 orders of magnitude faster than an extremely high statistical significant injection campaign, reporting the same reliability measurements with negligible loss of accuracy. Finally, we theoretically analyze MeR-LiN's statistical behavior to further justify its accuracy.

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Fine-Grained Characterization of Faults Causing Long Latency Crashes in Programs

Cited by 19 publications

References 27 publications

Towards a More Complete Understanding of SDC Propagation

Towards a More Complete Understanding of SDC Propagation

JMake: Dependable Compilation for Kernel Janitors

MeRLiN

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