An energy efficient garbage collector for java embedded devices

Griffin, Paul A.; Srisa-an, Witawas; Chang, Ji-Woong

doi:10.1145/1065910.1065943

Cited by 15 publications

(5 citation statements)

References 15 publications

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“…GC costs for restricted memory environments have been studied [16,17,32], but the precise relationship between the GC tradeoffs and energy consumption and illuminat-ing the way GC is affected by the system layers were beyond these studies. Recently, the impact of memory management on modern mobile devices has been redefined on (i) the page-level [41], and (ii) the managed heap by the VM runtime [34].…”

Section: Related Workmentioning

confidence: 99%

One Process to Reap Them All

et al. 2017

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Ubiquitous mobile platforms such as Android rely on managed language run-time environments, also known as language virtual machines (VMs), to run a diverse range of user applications (apps). Each app runs in its own private VM instance, and each VM makes its own private local decisions in managing its use of processor and memory resources. Moreover, the operating system and the hardware do not communicate their low-level decisions regarding power management with the high-level app environment. This lack of coordination across layers and across apps restricts more effective global use of resources on the device. We address this problem by devising and implementing a global memory manager service for Android that optimizes memory usage, run-time performance, and power consumption globally across all apps running on the device. The service focuses on the impact of garbage collection (GC) along these dimensions, since GC poses a significant overhead within managed run-time environments. Our prototype collects system-wide statistics from all running VMs, makes centralized decisions about memory management across apps and across software layers, and also collects garbage centrally. Furthermore, the global memory manager coordinates with the power manager to tune collector scheduling. In our evaluation, we illustrate the impact of such a central memory management service in reducing total energy consumption (up to 18%) and increasing throughput (up to 12%), and improving memory utilization and adaptability to user activities.

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Section: Related Workmentioning

confidence: 99%

One Process to Reap Them All

et al. 2017

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“…Otherwise, when allocation succeeds without exceeding softlimit, but the new allocation exceeds CSB, then the allocating mutator signals the GC daemon to start a new background GC cycle. Of course, heap sizing decisions have an effect on energy consumption [18,19,25].…”

Section: Taming Vm Controlsmentioning

confidence: 99%

“…Chen et al [18,19] tune the collector to enable shutting down memory banks that hold only garbage objects. Griffin et al [25] implement a hybrid mark-sweep/reference-counting collector to reduce power consumption. Sartor and Eeckhout [43] explored tradeoffs with separating JVM threads (e.g., garbage collector) and its effect on performance for a multi-socket server environment (8-core Intel Nehalem).…”

Section: Related Workmentioning

confidence: 99%

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

Hussein

Hosking

Payer

et al. 2015

Proceedings of the 2015 International Symposium on Memory Management

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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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“…There is also research on offloading compilation to a remote machine [42] to save energy and on power-saving hybrid garbage collection schemes [24].…”

Section: Background On Energy Managementmentioning

confidence: 99%

An experimental survey of energy management across the stack

Kambadur

Kim

2014

Proceedings of the 2014 ACM International Conference on Object Oriented Programming Systems Languages &Amp; Applications

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Modern demand for energy-efficient computation has spurred research at all levels of the stack, from devices to microarchi-tecture, operating systems, compilers, and languages. Unfortunately , this breadth has resulted in a disjointed space, with technologies at different levels of the system stack rarely compared, let alone coordinated. This work begins to remedy the problem, conducting an experimental survey of the present state of energy management across the stack. Focusing on settings that are exposed to software, we measure the total energy, average power, and execution time of 41 benchmark applications in 220 configurations , across a total of 200,000 program executions. Some of the more important findings of the survey include that effective parallelization and compiler optimizations have the potential to save far more energy than Linux's frequency tuning algorithms; that certain non-complementary energy strategies can undercut each other's savings by half when combined; and that while the power impacts of most strategies remain constant across applications, the runtime impacts vary, resulting in inconsistent energy impacts.

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An energy efficient garbage collector for java embedded devices

Cited by 15 publications

References 15 publications

One Process to Reap Them All

One Process to Reap Them All

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

An experimental survey of energy management across the stack

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