Inferred call path profiling

Mytkowicz, Todd; Coughlin, Devin; Diwan, Amer

doi:10.1145/1639949.1640102

Cited by 9 publications

(14 citation statements)

References 27 publications

(34 reference statements)

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“…Some techniques trade accuracy for performance, but their accuracy does not scale well with program complexity [13,28,35]. In contrast, precise calling context encoding (PCCE) losslessly encodes each calling context as an integer value or values [48].…”

Section: Alternatives To Cct-based Approachesmentioning

confidence: 99%

“…Mytkowicz et al and Inoue and Nakatani propose to reconstruct contexts from existing run-time values such as program counters and stack depth [28,35]. These approaches add virtually no overhead, but the values they use have significantly less entropy than Breadcrumbs' probabilistically unique values.…”

Section: Related Workmentioning

confidence: 99%

“…Walk-ing the stack whenever context is needed is expensive unless it is rare [16,24,37,44,50]. Recent approaches reconstruct calling context when needed from limited information that is cheap to collect, but these techniques are often probabilistic; their accuracy does not scale well with program complexity; they are unsuitable for bug detection analyses; and/or they cannot handle virtual method dispatch and dynamic class loading [13,28,35,48]. Notably, precise calling context encoding (PCCE) [48] provides efficient calling context encoding and profiling but cannot handle bug detection analyses, virtual method dispatch, or dynamic class loading.…”

Section: Introductionmentioning

confidence: 99%

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Efficient context sensitivity for dynamic analyses via calling context uptrees and customized memory management

Huang

Bond

2013

SIGPLAN Not.

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State-of-the-art dynamic bug detectors such as data race and memory leak detectors report program locations that are likely causes of bugs. However, programmers need more than static program locations to understand the behavior of increasingly complex and concurrent software. Dynamic calling context provides additional information, but it is expensive to record calling context frequently, e.g., at every read and write. Context-sensitive dynamic analyses can build and maintain a calling context tree (CCT) to track calling context-but in order to reuse existing nodes, CCT-based approaches require an expensive lookup.This paper introduces a new approach for context sensitivity that avoids this expensive lookup. The approach uses a new data structure called the calling context uptree (CCU) that adds low overhead by avoiding the lookup and instead allocating a new node for each context. A key contribution is that the approach can mitigate the costs of allocating many nodes by extending tracing garbage collection (GC): GC collects unused CCU nodes naturally and efficiently, and we extend GC to merge duplicate nodes lazily.We implement our CCU-based approach in a high-performance Java virtual machine and integrate it with a stalenessbased memory leak detector and happens-before data race detector, so they can report context-sensitive program locations that cause bugs. We show that the CCU-based approach, in concert with an extended GC, provides a compelling alternative to CCT-based approaches for adding context sensitivity to dynamic analyses.

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Section: Alternatives To Cct-based Approachesmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Efficient context sensitivity for dynamic analyses via calling context uptrees and customized memory management

Huang

Bond

2013

SIGPLAN Not.

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“…Breadcrumbs is most closely related to inferred call path profiling, which uses the program counter and stack depth (64 bits together) to identify a calling context [Mytkowicz et al 2009]. The advantage of this approach is that it has essentially no runtime overhead.…”

Section: Related Workmentioning

confidence: 99%

Breadcrumbs

2010

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Calling context-the set of active methods on the stack-is critical for understanding the dynamic behavior of large programs. Dynamic program analysis tools, however, are almost exclusively context insensitive because of the prohibitive cost of representing calling contexts at run time. Deployable dynamic analyses, in particular, have been limited to reporting only static program locations. This paper presents Breadcrumbs, an efficient technique for recording and reporting dynamic calling contexts. It builds on an existing technique for computing a compact (one word) encoding of each calling context that client analyses can use in place of a program location. The key feature of our system is a search algorithm that can reconstruct a calling context from its encoding using only a static call graph and a small amount of dynamic information collected at cold (infrequently executed) callsites. Breadcrumbs requires no offline training or program modifications, and handles all language features, including dynamic class loading.We use Breadcrumbs to add context sensitivity to two dynamic analyses: a data-race detector and an analysis for diagnosing null pointer exceptions. On average, it adds 10% to 20% runtime overhead, depending on a tunable parameter that controls how much dynamic information is collected. Collecting less information lowers the overhead, but can result in a search space explosion. In some cases this causes reconstruction to fail, but in most cases Breadcrumbs produces non-trivial calling contexts that have the potential to significantly improve both the precision of the analyses and the quality of the bug reports.

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“…In this chapter, we present a couple of use cases in which our approach would be appropriate to be applied. Previous work have found the call path profiles, among other things, to be useful in performance analysis [30,17]. A call path profile captures nested sequence of call encountered at execution time.…”

Section: Runtime Behaviormentioning

confidence: 99%

Monitoring software using property-aware program sampling

Narayanappa

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Inferred call path profiling

Cited by 9 publications

References 27 publications

Efficient context sensitivity for dynamic analyses via calling context uptrees and customized memory management

Efficient context sensitivity for dynamic analyses via calling context uptrees and customized memory management

Breadcrumbs

Monitoring software using property-aware program sampling

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