Controlling garbage collection and heap growth to reduce the execution time of Java applications

Brecht, Tim; Arjomandi, Eshrat; Li, Chang; Pham, Hang

doi:10.1145/504282.504308

Cited by 33 publications

(18 citation statements)

References 14 publications

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“…CMS uses simple heuristics to balance the tension between frequency of GC and heap size, similar to those described in by Brecht et al [Brecht et al 2001]. The primary parameter controlling heap size is the target heap utilization (targetUtil) ratio, used to resize the heap after each GC cycle.…”

Section: Dalvik Concurrent Mark-sweep (Cms)mentioning

confidence: 99%

Impact of GC design on power and performance for Android

Hussein

Payer

Hosking

et al. 2015

Proceedings of the 8th ACM International Systems and Storage Conference

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Small mobile devices have evolved to versatile computing systems. Android devices run a complete software stack, including a full Linux kernel, user land with several software daemons and a virtual machine to run applications. On these mobile systems energy is a scarce resource and needs to be handled carefully. Current systems rely on governors that adjust the frequency of individual cores depending on the system load. We measure energy consumption of different components of this complex software stack, including garbage collection (GC) of the Android virtual machine. Here we propose several extensions to the default GC configuration of Android, including a generational collector, across established dimensions of heap memory size and concurrency.Our evaluation shows that Android's asynchronous GC thread consumes a significant amount of energy. Therefore, varying the GC strategy can reduce total on-chip energy (by 20-30%) whilst slightly impacting application throughput (by 10-40%) and increasing worst-case pause times (by 20-30%). Our work quantifies the direct impact of GC on mobile system, enumerates the main factors and layers of this relationship, and offers a guide for analysis of memory behavior in understanding and tuning system performance.

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Section: Dalvik Concurrent Mark-sweep (Cms)mentioning

confidence: 99%

Impact of GC design on power and performance for Android

Hussein

Payer

Hosking

et al. 2015

Proceedings of the 8th ACM International Systems and Storage Conference

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“…We retain Dalvik's default heap sizing policies which are simple heuristics to balance the tension between frequency of garbage collection and heap size, similar to those described by Brecht et al [13]. The primary parameter controlling heap size and garbage collection is the target heap utilization ratio (targetutil), used to resize the heap after each GC cycle.…”

Section: Taming Vm Controlsmentioning

confidence: 99%

Don't race the memory bus: taming the GC leadfoot

et al. 2015

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Dynamic voltage and frequency scaling (DVFS) is ubiquitous on mobile devices as a mechanism for saving energy. Reducing the clock frequency of a processor allows a corresponding reduction in power consumption, as does turning off idle cores. Garbage collection is a canonical example of the sort of memory-bound workload that best responds to such scaling. Here, we explore the impact of frequency scaling for garbage collection in a real mobile device running Android's Dalvik virtual machine, which uses a concurrent collector. By controlling the frequency of the core on which the concurrent collector thread runs we can reduce power significantly. Running established multi-threaded benchmarks shows that total processor energy can be reduced up to 30 %, with end-to-end performance loss of at most 10 %.

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“…These data can include total memory size, heap size, GC efficiency, current memory footprint, real memory usage, memory pressure, empirically collected data about program behaviour, etc. Some of the algorithms rely on empirically tuned values [12,13] or on data collected by running benchmarks [11,14]. Other algorithms require changes in VM or OS or both [15].…”

Section: Mark-bits Organizationmentioning

confidence: 99%

Mark without much Sweep Algorithm for Garbage Collection

2014

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In this paper two simple improvements over traditional mark-sweep collector are proposed. The core idea is placing small objects of the same type in buckets. The buckets are organised in such way to eliminate the internal fragmentation, sweeping, and freeing inside them. The measured improvement of garbage collection time over traditional mark-sweep is 19%. Another proposed improvement is more general and is applicable to other garbage collection algorithms as well. It uses heuristics to control the heap growth. The regularities in behaviour of objects of particular types are used to determine whether the collection should be performed or avoided in favour of immediate heap expansion. The heap expansion algorithm reduces garbage collection time over traditional marksweep for 49% while keeping the heap size approximately the same. Key words: Mark-sweep, Garbage collection, Heap sizing, Memory managementAlgoritam za zbrinjavanje memorije s označavanjem i smanjenim oslobadanjem. U ovomčlanku opisana su dva jednostavna poboljšanja algoritma označi-oslobodi. Osnovna ideja jest smještanje malih objekata istog tipa u pretince. Pretinci su organizirani tako da se u njima ne pojavljuje unutarnja fragmentacija, a uklanja se i potreba za oslobadanjem blokova zauzetih nedohvatljivim objektima. Vrijeme provedeno u zbrinjavanju manje je za 19% u odnosu na klasični algoritam označi-oslobodi. Drugo poboljšanje je općenitije i moguće ga je primijeniti i na druge algoritme za zbrinjavanje memorije. U njemu rastom gomile upravlja heuristički algoritam koji koristi pravilnosti u ponašanju objekata različitih tipova. Na temelju njih, algoritam odlučuje hoće li gomila biti zbrinuta ili odmah proširena. Heuristička inačica algoritma smanjuje vrijeme provedeno u zbrinjavanju u odnosu na tradicionalni algoritam označi-oslobodi za 49%, a da pri tome zahtijeva približno istu količinu memorije.

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Controlling garbage collection and heap growth to reduce the execution time of Java applications

Cited by 33 publications

References 14 publications

Impact of GC design on power and performance for Android

Impact of GC design on power and performance for Android

Don't race the memory bus: taming the GC leadfoot

Mark without much Sweep Algorithm for Garbage Collection

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