Understanding co-run performance on CPU-GPU integrated processors: observations, insights, directions
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- 作者：Qi Zhu ; Bo Wu ; Xipeng Shen ; Kai Shen ; Li Shen…
- 关键词：performance analysis ; GPGPU ; co ; run degradation ; fused processor ; program transformation
- 刊名：Frontiers of Computer Science
- 出版年：2017
- 出版时间：February 2017
- 年：2017
- 卷：11
- 期：1
- 页码：130-146
- 全文大小：
- 刊物类别：Computer Science
- 刊物主题：Computer Science, general;
- 出版者：Higher Education Press
- ISSN：2095-2236
- 卷排序：11
文摘
Recent years have witnessed a processor development trend that integrates central processing unit (CPU) and graphic processing unit (GPU) into a single chip. The integration helps to save some host-device data copying that a discrete GPU usually requires, but also introduces deep resource sharing and possible interference between CPU and GPU. This work investigates the performance implications of independently co-running CPU and GPU programs on these platforms. First, we perform a comprehensive measurement that covers a wide variety of factors, including processor architectures, operating systems, benchmarks, timing mechanisms, inputs, and power management schemes. These measurements reveal a number of surprising observations.We analyze these observations and produce a list of novel insights, including the important roles of operating system (OS) context switching and power management in determining the program performance, and the subtle effect of CPU-GPU data copying. Finally, we confirm those insights through case studies, and point out some promising directions to mitigate anomalous performance degradation on integrated heterogeneous processors.