Algorithmic profiling

Zaparanuks, Dmitrijs; Hauswirth, Matthias

doi:10.1145/2345156.2254074

Cited by 30 publications

(23 citation statements)

References 21 publications

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“…Static [14] and dynamic [12,42] analyses for finding the computational complexity of a function is related to the cost plots produced by EventBreak. These approaches analyze the computational complexity of individual functions, whereas we consider event sequences.…”

Section: Related Workmentioning

confidence: 99%

EventBreak

et al. 2014

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Event-driven user interface applications typically have a single thread of execution that processes event handlers in response to input events triggered by the user, the network, or other applications. Programmers must ensure that event handlers terminate after a short amount of time because otherwise, the application may become unresponsive. This paper presents EventBreak, a performance-guided test generation technique to identify and analyze event handlers whose execution time may gradually increase while using the application. The key idea is to systematically search for pairs of events where triggering one event increases the execution time of the other event. For example, this situation may happen because one event accumulates data that is processed by the other event. We implement the approach for JavaScriptbased web applications and apply it to three real-world applications. EventBreak discovers events with an execution time that gradually increases in an unbounded way, which makes the application unresponsive, and events that, if triggered repeatedly, reveal a severe scalability problem, which makes the application unusable. The approach reveals two known bugs and four previously unknown responsiveness problems. Furthermore, we show that EventBreak helps in testing that event handlers avoid such problems by bounding a handler's execution time.

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Section: Related Workmentioning

confidence: 99%

EventBreak

et al. 2014

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“…A similar kind of dynamic analysis is needed for Algorithmic Profiling [18], an approach capable of discovering algorithms in Java byte code and inferring their time complexity by observing a program run. For this, it needs to keep track of the data the algorithms are processing.…”

Section: Recursive Data Structure Analysismentioning

confidence: 99%

ShadowData

Vitásek

Binder

Hauswirth

2013

Proceedings of the 11th ACM SIGPLAN-SIGSOFT Workshop on Program Analysis for Software Tools and Engineering

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In this paper we compare different approaches to maintain shadow state for heap objects in Java. We identify dynamic analyses that need to shadow heap objects, and we consider their requirements. We describe three very different approaches in detail: using a hash map, heap tagging, and injecting shadow fields.We discuss the tradeoffs between the different approaches, and we empirically evaluate the viability and performance of the hash map and heap tagging approaches, the two approaches that work on Hotspot, the most prevalent JVM. We find that both approaches work reliably, but that -with the contemporary JVM implementation of heap tagging -the simpler hash map approach has far superior performance.

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“…Recent works [10,24] have proposed to extend the traditional profiling technique by determining an empirical cost function. Such a cost function is found automatically and it captures an approximate cost of the program with respect to different inputs (and in particular, with respect to different input sizes).…”

Section: Related Workmentioning

confidence: 99%

“…Such a cost function is found automatically and it captures an approximate cost of the program with respect to different inputs (and in particular, with respect to different input sizes). Since the approaches in [10,24] are based on a few training runs of the program, they can only capture an approximate cost of the program for a fixed input size. On the other hand, since our approach is based on program analysis, we can provide a sound lower and upper bound of cache performance.…”

Section: Related Workmentioning

confidence: 99%

“…On the other hand, since our approach is based on program analysis, we can provide a sound lower and upper bound of cache performance. Moreover, the work of [10,24] do not guarantee to capture the performance signature of the entire program. Our approach does the same by partitioning the input domain, in the form of representative path programs.…”

Section: Related Workmentioning

confidence: 99%

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Program performance spectrum

2013

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Real-time and embedded applications often need to satisfy several non-functional properties such as timing. Consequently, performance validation is a crucial stage before the deployment of real-time and embedded software. Cache memories are often used to bridge the performance gap between a processor and memory subsystems. As a result, the analysis of caches plays a key role in the performance validation of real-time, embedded software. In this paper, we propose a novel approach to compute the cache performance signature of an entire program. Our technique is based on exploring the input domain through different path programs. Two paths belong to the same path program if they follow the same set of control flow edges but may vary in the iterations of loops encountered. Our experiments with several subject programs show that the different paths grouped into a path program have very similar and often exactly same cache performance.Our path program exploration can be viewed as partitioning the input domain of the program. Each partition is associated with its cache performance and a symbolic formula capturing the set of program inputs which constitutes the partition. We show that such a partitioning technique has wide spread usages in performance prediction, testing, debugging and design space exploration.

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Algorithmic profiling

Cited by 30 publications

References 21 publications

EventBreak

EventBreak

ShadowData

Program performance spectrum

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