Paper Info
Title
Understanding co-run performance on CPU-GPU integrated processors: observations, insights, directions.
Abstract
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.
Year
DOI
Venue
2017
10.1007/s11704-016-5468-8
Frontiers of Computer Science
Keywords
Field
DocType
performance analysis, GPGPU, co-run degradation, fused processor, program transformation
Program transformation,Computer science,Copying,Artificial intelligence,Context switch,Power management,Central processing unit,Computer architecture,Parallel computing,Chip,General-purpose computing on graphics processing units,Shared resource,Machine learning
Journal
Volume
Issue
ISSN
11
1
2095-2236
Citations 
PageRank 
References 
1
0.36
30
Authors
6
Name
Order
Citations
PageRank
Qi Zhu181.52
Bo Wu226618.07
Xipeng Shen32025118.55
Kai Shen447532.68
Li Shen59515.58
Zhi-Ying Wang6870127.04