On measuring garbage collection responsiveness

Printezis, Tony

doi:10.1016/j.scico.2006.02.004

Cited by 8 publications

(7 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, worst case and average mutator pause times do not adequately characterize the impact of GC on responsiveness because of the complexity of the system stacks. Thus, we use minimum mutator utilization (MMU) [20,39,51] over a range of time intervals. For each individual mutator we gather the pauses during the following events: (i) safepoint pauses, when a mutator stops in response to a suspension request (e.g., for marking mutator roots), (ii) foreground pauses, when a mutator performs a foreground GC cycle, and (iii) concurrent pauses, when a mutator waits for a concurrent GC cycle to finish.…”

Section: Methodology and Benchmarkingmentioning

confidence: 99%

One Process to Reap Them All

et al. 2017

View full text Add to dashboard Cite

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.

show abstract

Section: Methodology and Benchmarkingmentioning

confidence: 99%

One Process to Reap Them All

et al. 2017

View full text Add to dashboard Cite

show abstract

“…For some 'hard' real-time applications, for example safety critical systems, any failure to meet a deadline is unacceptable. However, 'soft' real-time applications can tolerate some deviation from their responsiveness goal, provided it is rare [Printezis 2006]. Sapphire is a fully concurrent, replicating collector, designed to support soft real-time applications running a large number of mutator threads on small-to medium-scale, shared memory, multiprocessors.…”

Section: The Original Sapphire Algorithmmentioning

confidence: 99%

Transactional Sapphire

Ugawa

Ritson

Jones

2018

ACM Trans. Program. Lang. Syst.

View full text Add to dashboard Cite

show abstract

“…Instead, minimum mutator utilization (MMU) over a range of timeframes yield a better understanding of the distribution and impact of pauses [20,30,41]. Our VM profiler records the pauses experienced by each mutator, classified into three categories: (i) GC-safepoint pauses, when a mutator stops in response to a suspension request (e.g., for marking mutator roots), (ii) foreground pauses, when a mutator performs a foreground GC cycle, and (iii) concurrent pauses, when a mutator waits for a concurrent GC cycle to finish.…”

Section: Responsivenessmentioning

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

View full text Add to dashboard Cite

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 %.

show abstract

On measuring garbage collection responsiveness

Cited by 8 publications

References 9 publications

One Process to Reap Them All

One Process to Reap Them All

Transactional Sapphire

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

Contact Info

Product

Resources

About