Idle-time garbage-collection scheduling

Degenbaev, Ulan; Eisinger, Jochen; Ernst, Manfred; McIlroy, Ross; Payer, Hannes

doi:10.1145/2948991

Cited by 2 publications

(5 citation statements)

References 3 publications

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“…This happens not only because of its explicit overhead on CPU and memory cycles, but also because of implicit scheduling decisions by the OS and hardware with respect to CPU cores. Therefore, a potential approach to optimize GC cost per single VM is to take advantage of GC idleness and control the frequency of the core on which the concurrent collector thread is running [23,34]. Distributed Controls.…”

Section: Motivationmentioning

confidence: 99%

“…The latter is the trace of GC events throughout the program execution that produces the lowest GC cost [36]. How-ever, with the introduction of other system components into the cost equation (i.e., scheduling and CPU throttling), the GC scheduling can be tuned by hiding expensive GC operations inside of small, otherwise unused idle portions of application execution, which results in better overall execution [23]. For mobile devices, tuning the GC implementation to meet performance and power goals is exceptionally difficult, because per-app GC cost is defined as a function of several controls [23,34] such as: (i) the power manager reacting to CPU idle-time during memory-bound GC phases; (ii) VM configurations, GC algorithm and the heuristics controlling the heap size; and (iii) the workload and memory stress at run time.…”

Section: Global Collector and Energy Optimizationmentioning

confidence: 99%

“…The reduction of GC energy cost while allowing for better responsiveness and throughput. This can be achieved either (i) by coordinating between the system scheduler and the VM [23], or (ii) by capping the frequency of the core during GC phases [34].…”

Section: Global Collector and Energy Optimizationmentioning

confidence: 99%

“…This allows the governor policy to account for distinct phases of GC behavior in the application workload. By monitoring the workload the GC service makes informed decisions to schedule background tasks with lower GC costs [23].…”

Section: Energy Optimizationmentioning

confidence: 99%

“…Several fine-grained profile tools [12,47,49,54] show that apps still have room for significant improvements by reducing idle time and power leaks caused by inefficient software subcomponents [5,18,49,50]. This knowledge fueled the interest in tuning the performance and energy by direct coordination of the application layer and the power managers [23,34,37,40].…”

Section: Related Workmentioning

confidence: 99%

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One Process to Reap Them All

Hussein

Payer

Hosking

et al. 2017

Proceedings of the 13th ACM SIGPLAN/SIGOPS International Conference on Virtual Execution Environments

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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: Motivationmentioning

confidence: 99%

Section: Global Collector and Energy Optimizationmentioning

confidence: 99%

Section: Global Collector and Energy Optimizationmentioning

confidence: 99%

Section: Energy Optimizationmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 3 more Smart Citations

One Process to Reap Them All

Hussein

Payer

Hosking

et al. 2017

Proceedings of the 13th ACM SIGPLAN/SIGOPS International Conference on Virtual Execution Environments

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Project snowflake: non-blocking safe manual memory management in .NET

Parkinson

Vytiniotis

Vaswani

et al. 2017

Proc. ACM Program. Lang.

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Garbage collection greatly improves programmer productivity and ensures memory safety. Manual memory management on the other hand often delivers better performance but is typically unsafe and can lead to system crashes or security vulnerabilities. We propose integrating safe manual memory management with garbage collection in the .NET runtime to get the best of both worlds. In our design, programmers can choose between allocating objects in the garbage collected heap or the manual heap. All existing applications run unmodified, and without any performance degradation, using the garbage collected heap. Our programming model for manual memory management is flexible: although objects in the manual heap can have a single owning pointer, we allow deallocation at any program point and concurrent sharing of these objects amongst all the threads in the program. Experimental results from our .NET CoreCLR implementation on real-world applications show substantial performance gains especially in multithreaded scenarios: up to 3x savings in peak working sets and 2x improvements in runtime.

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Idle-time garbage-collection scheduling

Abstract: Taking advantage of idleness to reduce dropped frames and memory consumption.

Cited by 2 publications

References 3 publications

One Process to Reap Them All

One Process to Reap Them All

Project snowflake: non-blocking safe manual memory management in .NET

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