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Record W4247047556 · doi:10.1145/2345156.2254091

Parallelizing top-down interprocedural analyses

2012· article· en· W4247047556 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueACM SIGPLAN Notices · 2012
Typearticle
Languageen
FieldComputer Science
TopicSoftware Testing and Debugging Techniques
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsComputer scienceReachabilityScalabilityCall graphGraphModular designContext (archaeology)Parallelism (grammar)Programming languageTop-down and bottom-up designStatic analysisTheoretical computer scienceParallel computingDatabase

Abstract

fetched live from OpenAlex

Modularity is a central theme in any scalable program analysis. The core idea in a modular analysis is to build summaries at procedure boundaries, and use the summary of a procedure to analyze the effect of calling it at its calling context. There are two ways to perform a modular program analysis: (1) top-down and (2) bottomup. A bottom-up analysis proceeds upwards from the leaves of the call graph, and analyzes each procedure in the most general calling context and builds its summary. In contrast, a top-down analysis starts from the root of the call graph, and proceeds downward, analyzing each procedure in its calling context. Top-down analyses have several applications in verification and software model checking. However, traditionally, bottom-up analyses have been easier to scale and parallelize than top-down analyses. In this paper, we propose a generic framework, BOLT, which uses MapReduce style parallelism to scale top-down analyses. In particular, we consider top-down analyses that are demand driven, such as the ones used for software model checking. In such analyses, each intraprocedural analysis happens in the context of a reachability query. A query Q over a procedure P results in query tree that consists of sub-queries over the procedures called by P . The key insight in BOLT is that the query tree can be explored in parallel using MapReduce style parallelism -- the map stage can be used to run a set of enabled queries in parallel, and the reduce stage can be used to manage inter-dependencies between queries. Iterating the map and reduce stages alternately, we can exploit the parallelism inherent in top-down analyses. Another unique feature of BOLT is that it is parameterized by the algorithm used for intraprocedural analysis. Several kinds of analyses, including may analyses, mustanalyses, and may-must-analyses can be parallelized using BOLT. We have implemented the BOLT framework and instantiated the intraprocedural parameter with a may-must-analysis. We have run BOLT on a test suite consisting of 45 Microsoft Windows device drivers and 150 safety properties. Our results demonstrate an average speedup of 3.71x and a maximum speedup of 7.4x (with 8 cores) over a sequential analysis. Moreover, in several checks where a sequential analysis fails, BOLT is able to successfully complete its analysis.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.241
Threshold uncertainty score0.533

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.088
GPT teacher head0.364
Teacher spread0.276 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it