Static Detection of Loop-Invariant Data Structures

Xu, Guoqing; Yan, Dacong; Rountev, Atanas

doi:10.1007/978-3-642-31057-7_32

Cited by 32 publications

(19 citation statements)

References 38 publications

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“…JOLT [30] is a VM-based tool that uses a new metric to quantify object churn and identify regions that make heavy use of temporary objects, in order to guide aggressive method inlining. Work by Xu et al [35,36,37,38,34] finds copy-and container-related inefficiencies. Jin et al from [17] studies performance bugs in real-world software systems.…”

Section: Related Workmentioning

confidence: 98%

Cachetor: detecting cacheable data to remove bloat

Nguyen

2013

Proceedings of the 2013 9th Joint Meeting on Foundations of Software Engineering

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Modern object-oriented software commonly suffers from runtime bloat that significantly affects its performance and scalability. Studies have shown that one important pattern of bloat is the work repeatedly done to compute the same data values. Very often the cost of computation is very high and it is thus beneficial to memoize the invariant data values for later use. While this is a common practice in real-world development, manually finding invariant data values is a daunting task during development and tuning. To help the developers quickly find such optimization opportunities for performance improvement, we propose a novel run-time profiling tool, called Cachetor, which uses a combination of dynamic dependence profiling and value profiling to identify and report operations that keep generating identical data values. The major challenge in the design of Cachetor is that both dependence and value profiling are extremely expensive techniques that cannot scale to large, real-world applications for which optimizations are important. To overcome this challenge, we propose a series of novel abstractions that are applied to run-time instruction instances during profiling, yielding significantly improved analysis time and scalability. We have implemented Cachetor in Jikes Research Virtual Machine and evaluated it on a set of 14 large Java applications. Our experimental results suggest that Cachetor is effective in exposing caching opportunities and substantial performance gains can be achieved by modifying a program to cache the reported data.

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Section: Related Workmentioning

confidence: 98%

Cachetor: detecting cacheable data to remove bloat

Nguyen

2013

Proceedings of the 2013 9th Joint Meeting on Foundations of Software Engineering

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“…2 that contain certain relatively expensive operations, such as temporary-object creation/destruction [37], file I/O, and UI updates.…”

Section: Operations In Responsive Actionsmentioning

confidence: 99%

Context-sensitive delta inference for identifying workload-dependent performance bottlenecks

Xiao

Han

Zhang

et al. 2013

Proceedings of the 2013 International Symposium on Software Testing and Analysis

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Software hangs can be caused by expensive operations in responsive actions (such as time-consuming operations in UI threads). Some of the expensive operations depend on the input workloads, referred to as workload-dependent performance bottlenecks (WDPBs). WDPBs are usually caused by workload-dependent loops (i.e., WDPB loops) that contain relatively expensive operations. Traditional performance testing and single-execution profiling may not reveal WDPBs due to incorrect assumptions of workloads. To address these issues, we propose the ∆Infer approach that predicts WDPB loops under large workloads via inferring iteration counts of WDPB loops using complexity models for the workload size. ∆Infer incorporates a novel concept named context-sensitive delta inference that consists of two parts: temporal inference for inferring the complexity models of different program locations, and spatial inference for identifying WDPB loops as WDPB candidates. We conducted evaluations on two popular open-source GUI applications, and identified impactful WDPBs that caused 10 performance bugs.

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“…While this is similar to the detection of reusable instances in our work, we can find more opportunities such as reusable shapes and reusable data. Recent work such as [6,46] uses static analysis to identify reusable data structures created in a loop. However, in a large-scale application, reuse opportunities may be located in methods far away from a loop, limiting significantly the real-world usefulness of these analyses.…”

Section: Related Workmentioning

confidence: 99%

Finding reusable data structures

XuGuoqing

2012

SIGPLAN Not.

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A big source of run-time performance problems in largescale, object-oriented applications is the frequent creation of data structures (by the same allocation site) whose lifetimes are disjoint, and whose shapes and data content are always the same. Constructing these data structures and computing the same data values many times is expensive; significant performance improvements can be achieved by reusing their instances, shapes, and/or data values rather than reconstructing them. This paper presents a run-time technique that can be used to help programmers find allocation sites that create such data structures to improve performance. At the heart of the technique are three reusability definitions and novel summarization approaches that compute summaries for data structures based on these definitions. The computed summaries are used subsequently to find data structures that have disjoint lifetimes, and/or that have the same shapes and content. We have implemented this technique in the Jikes RVM and performed extensive studies on large-scale, real-world programs. We describe our experience using six case studies, in which we have achieved large performance gains by fixing problems reported by our tool.

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Static Detection of Loop-Invariant Data Structures

Cited by 32 publications

References 38 publications

Cachetor: detecting cacheable data to remove bloat

Cachetor: detecting cacheable data to remove bloat

Context-sensitive delta inference for identifying workload-dependent performance bottlenecks

Finding reusable data structures

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