Predicting Access to Persistent Objects Through Static Code Analysis

Touma, Rizkallah; Queralt, Anna; Cortes, Toni; Pérez, Marı́a S.

doi:10.1007/978-3-319-67162-8_7

Cited by 1 publication

(4 citation statements)

References 14 publications

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“…The work reported here extends our previous work [17] in several directions. First, after presenting the theoretical grounds, we present the design and implementation of a complete prefetching system, based on static code analysis, and integrate it into an independent POS.…”

Section: Contributionssupporting

confidence: 86%

“…The purpose of this evaluation is to analyse how CAPre reduces application execution time, which is the ultimate goal of our prefetching technique. For other indicators such as the true positive or the false negative rates, we refer the reader to [17], where these metrics were analysed in detail.…”

Section: Discussionmentioning

confidence: 99%

“…By contrast, our previous work has a detailed study indicating that including prefetching hints of overridden methods sharply increases the false positives rate in some cases [17]. Based on the results of this study, in the implementation of CAPre, we will not include the prefetching hints of overridden methods when generating P H m of a particular method m.…”

Section: Runtime Application Behaviormentioning

confidence: 96%

“…In case of branch-dependent navigations, we will include the prefetching hints of all the branches, both taken and not taken, since it was shown in Section 4.4 to be the best option. On the other hand, we will not include any hints to prefetch the objects accessed by overridden methods, because it was shown to be a significant source of false positives in our previous work [17].…”

Section: Generating Prefetching Hintsmentioning

confidence: 99%

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CAPre: Code-Analysis based Prefetching for Persistent Object Stores

Touma

Queralt

Cortés

2020

Future Generation Computer Systems

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Data prefetching aims to improve access times to data storage systems by predicting data records that are likely to be accessed by subsequent requests and retrieving them into a memory cache before they are needed. In the case of Persistent Object Stores, previous approaches to prefetching have been based on predictions made through analysis of the store's schema, which generates rigid predictions, or monitoring access patterns to the store while applications are executed, which introduces memory and/or computation overhead.In this paper, we present CAPre, a novel prefetching system for Persistent Object Stores based on static code analysis of object-oriented applications. CAPre generates the predictions at compile-time and does not introduce any overhead to the application execution. Moreover, CAPre is able to predict large amounts of objects that will be accessed in the near future, thus enabling the object store to perform parallel prefetching if the objects are distributed, in a much more aggressive way than in schema-based prediction algorithms. We integrate CAPre into a distributed Persistent Object Store and run a series of experiments that show that it can reduce the execution time of applications from 9% to over 50%, depending on the nature of the application and its persistent data model.

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Section: Contributionssupporting

confidence: 86%

Section: Discussionmentioning

confidence: 99%

Section: Runtime Application Behaviormentioning

confidence: 96%

Section: Generating Prefetching Hintsmentioning

confidence: 99%

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