Boosting mobile GPU performance with a decoupled access/execute fragment processor

J. Arnau, Joan-Manuel Parcerisa, Polychronis Xekalakis
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引用次数: 32

Abstract

Smartphones represent one of the fastest growing markets, providing significant hardware/software improvements every few months. However, supporting these capabilities reduces the operating time per battery charge. The CPU/GPU component is only left with a shrinking fraction of the power budget, since most of the energy is consumed by the screen and the antenna. In this paper, we focus on improving the energy efficiency of the GPU since graphical applications consist an important part of the existing market. Moreover, the trend towards better screens will inevitably lead to a higher demand for improved graphics rendering. We show that the main bottleneck for these applications is the texture cache and that traditional techniques for hiding memory latency (prefetching, multithreading) do not work well or come at a high energy cost. We thus propose the migration of GPU designs towards the decoupled access-execute concept. Furthermore, we significantly reduce bandwidth usage in the decoupled architecture by exploiting inter-core data sharing. Using commercial Android applications, we show that the end design can achieve 93% of the performance of a heavily multithreaded GPU while providing energy savings of 34%.
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通过解耦的访问/执行片段处理器提升移动GPU性能
智能手机是增长最快的市场之一,每隔几个月就会有重大的硬件/软件改进。然而,支持这些功能减少了每次电池充电的操作时间。CPU/GPU组件只剩下越来越少的功率预算,因为大部分能量都被屏幕和天线消耗了。在本文中,我们专注于提高GPU的能源效率,因为图形应用程序是现有市场的重要组成部分。此外,更好的屏幕的趋势将不可避免地导致对改进图形渲染的更高需求。我们表明,这些应用程序的主要瓶颈是纹理缓存,而传统的隐藏内存延迟的技术(预取、多线程)不能很好地工作,或者需要很高的能量成本。因此,我们建议将GPU设计迁移到解耦的访问-执行概念。此外,我们通过利用核间数据共享显著降低了解耦架构中的带宽使用。使用商业Android应用程序,我们表明最终设计可以实现93%的高性能多线程GPU,同时提供34%的节能。
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