Lag, drag, void and use—heap profiling and space-efficient compilation revisited

RöjemoNiklas,; RuncimanColin,

doi:10.1145/232629.232633

Cited by 18 publications

(17 citation statements)

References 8 publications

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“…RSjemo and Runciman performed similar measurements for Haakell, a lazy functional language [9]. Unfortunately, our results are not directly comparable because they measure wall clock time where we measure time in bytes allocated.…”

Section: Measuring Dragged Objectsmentioning

confidence: 86%

“…Following [9], we refer to the time interval from the last use Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made oi' distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. 'lb copy otherwise, to republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a lbe, of an object until its collection as the object's drag time and to object itself as a dragged object.…”

Section: Introductionmentioning

confidence: 99%

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On effectiveness of GC in Java

Shaham

Kolodner²,

Sagly

2000

SIGPLAN Not.

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We study the effectiveness of garbage collection (CC) algorithms by measuring the time difference between the actual collection time of an object and the potential earliest collection time for that object. Our ultimate goal is to use this study in order to develop static analysis techniques that can be used together with GC to allow earlier reclamation of objects. The results may also be used to pinpoint application source code that could be rewritten in a way that would allow more timely GC.Specifically, we compare the objects reachable from the root set to the ones that are actually used again. The idea is that GC could reclaim unused objects even if they are reachable from the root set. Thus, our experiments indicate a kind of upper bound on storage savings that could be achieved. We also try to characterize these objects in order to understand the potential benefits of various static analysis algorithms.The Java Virtual Machine (JVM) was instrumented to measure objects that are reachable, but not used again, and to characterize these objects. Experimental results are shown for the SPECjvm98 benchmark suite. The potential memory savings for these benchmarks range from 23% to 74%.

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Section: Measuring Dragged Objectsmentioning

confidence: 86%

Section: Introductionmentioning

confidence: 99%

On effectiveness of GC in Java

Shaham

Kolodner²,

Sagly

2000

SIGPLAN Not.

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“…Average liveness is monotone. by Röjemo and Runciman divides the lifetime and identifies the unnecessary parts as lag and drag [21]. Lifetime is defined by access.…”

Section: Related Workmentioning

confidence: 99%

Modeling heap data growth using average liveness

Ding

Luo

2014

Proceedings of the 2014 International Symposium on Memory Management

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Most of today's programs make use of a sizable heap to store dynamic data. To characterize the heap dynamics, this paper presents a set of metrics to measure the average amount of data live and dead in a period of execution. They are collectively called average liveness. The paper defines these metrics of average liveness, gives linear-time algorithms for measurement, and discusses their use in finding the best heap size. The algorithms are implemented in a Java tracing system called Elephant Tracks and evaluated using the DaCapo benchmarks running on the Oracle HotSpot and IBM J9 Java virtual machines.

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“…If space leaks could be detected earlier-ideally, as soon as they are introduced-they would be easier to fix and would never reach end users. Certain types of advanced profiling information can detect suspicious memory patterns, 9 and some experimental tools can annotate expected heap usage, 4 but nothing has reached mainstream use. The Haskell compiler does partition memory in such a way that some space leaks are detected-the sum example fails with a message about stack overflow for lists of length 508146 and above, but the other examples in this article use all available memory before failing.…”

Section: Detecting Space Leaksmentioning

confidence: 99%

Leaking space

Mitchell¹

2013

Commun. ACM

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practice a spaCe leaK occurs when a computer program uses more memory than necessary. In contrast to memory leaks, where the leaked memory is never released, the memory consumed by a space leak is released, but later than expected. This article presents example space leaks and how to spot and eliminate them.Let's first consider two "real-life" space leaks. Xena, the xylophone enthusiast, buys a 26-volume printed encyclopedia, but she only wants to read the article on xylophones. The encyclopedia occupies a lot of space on her bookshelf. Xena could throw away all but the X volume, reducing the shelf requirements; or she could cut out the xylophone article, leaving only a single piece of paper. In this example, Xena is storing lots of information but is interested in only a small subset of it.Xena's friend Shawn, the statistician, is curious about how many redundant pages Xena is storing. To determine the total number of pages in the encyclopedia Shawn buys a copy of the 26-volume encyclopedia, even though he is interested in only the number of pages per volume. Actually, Shawn does not need to know the sizes of 26 separate volumes but only the total size-information that could be written on the back of a stamp.In this example, Shawn is storing lots of information, and while each volume contains useful information, the result could be stored more compactly. Figure 1 depicts the memory layout Xena and Shawn might represent if they were computer programs. In both cases a solid blue arrow points to the encyclopedia, representing the memory being retained. A dotted red arrow points to the information that is actually useful.

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Lag, drag, void and use—heap profiling and space-efficient compilation revisited

Abstract: The context for this paper is functional computation by

Cited by 18 publications

References 8 publications

On effectiveness of GC in Java

On effectiveness of GC in Java

Modeling heap data growth using average liveness

Leaking space

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