本文评估了低功耗GPU在非图形工作负载中的潜力，特别是针对电池供电的移动设备。研究发现，除了提供加速性能外，低功耗GPU还具有显著的能效优势。通过对比不同优化策略，文章指出适用于高性能GPU的方法并不完全适用于低功耗GPU，并提出了一些克服这些挑战的技术。
Abstract:
In this paper we evaluate the promise held by lowpower GPUs for non-graphic workloads that arise in embedded systems. Towards this, we map and implement 5 benchmarks, that find utility in very different application domains, to an embedded GPU. Our results show that apart from accelerated performance, embedded GPUs are promising also because of their energy efficiency which is an important design goal for battery-driven mobile devices. We show that adopting the same optimization strategies as those used for programming high-end GPUs might lead to worse performance on embedded GPUs. This is due to restricted features of embedded GPUs, such as, limited or no user-defined memory, small instruction-set, limited number of registers, among others. We propose techniques to overcome such challenges, e.g., by distributing the workload between GPUs and multi-core CPUs, similar to the spirit of heterogeneous computation.
(Arian Maghazeh, Unmesh D. Bordoloi, Petru Eles and Zebo Peng: “General Purpose Computing on Low-Power Embedded GPUs: Has It Come of Age?”, 13th International Conference on Embedded Computer Systems: Architectures, Modeling, and Simulation, Samos, Greece, July 15-18, 2013. [Preprint])
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