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Managing gpu buffers for caching more apps in mobile systems

机译:管理GPU缓冲区,用于缓存移动系统中的更多应用程序

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Modern mobile systems cache apps actively to quickly respond to a user's call to launch apps. Since the amount of usable memory is critical to the number of cacheable apps, it is important to maximize memory utilization. Meanwhile, modern mobile apps make use of graphics processing units (GPUs) to accelerate their graphic operations and to provide better user experience. In resource-constrained mobile systems, GPU cannot afford its private memory but shares the main memory with CPU. It leads to a considerable amount of main memory to be allocated for GPU buffers which are used for processing GPU operations. These GPU buffers are, however, not managed effectively so that inactive GPU buffers occupy a large fraction of the memory and decrease memory utilization. This paper proposes a scheme to manage GPU buffers to increase the memory utilization in mobile systems. Our scheme identifies inactive GPU buffers by exploiting the state of an app from a user's perspective, and reduces their memory footprint by compressing them. Our sophisticated design approach prevents GPU-specific issues from causing an unpleasant overhead. Our evaluation on a running prototype with realistic workloads shows that the proposed scheme can secure up to 215.9 MB of extra memory from 1.5 GB of main memory and increase the average number of cached apps by up to 31.3%.
机译:现代移动系统积极缓存应用程序,以便快速响应用户呼叫启动应用程序。由于可用内存量对可缓存应用程序的数量至关重要,因此最大化内存利用率是重要的。同时,现代移动应用程序利用图形处理单元(GPU)加速其图形操作并提供更好的用户体验。在资源受限的移动系统中,GPU不能承担其私有内存,但与CPU共享主内存。它导致可用于用于处理GPU操作的GPU缓冲区的大量主存储器。然而,这些GPU缓冲区是没有有效管理的,以便非活动的GPU缓冲器占据了大部分存储器并降低内存利用率。本文提出了一种管理GPU缓冲区来增加移动系统中的内存利用率的方案。我们的方案通过从用户的角度来利用应用程序的状态来识别非活动GPU缓冲区,并通过压缩它们来减少其内存占用空间。我们复杂的设计方法可防止特定于GPU的问题导致令人不快的开销。我们对具有现实工作负载的运行原型的评估表明,该方案可以从1.5 GB的主内存中获得高达215.9 MB的额外内存,并将高速缓存应用程序的平均数增加到31.3%。

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