NDLI: Analyzing memory management methods on integrated CPU-GPU systems

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Analyzing memory management methods on integrated CPU-GPU systems

Content Provider	ACM Digital Library
Author	Dashti, Mohammad Fedorova, Alexandra
Abstract	Heterogeneous systems that integrate a multicore CPU and a GPU on the same die are ubiquitous. On these systems, both the CPU and GPU share the same physical memory as opposed to using separate memory dies. Although integration eliminates the need to copy data between the CPU and the GPU, arranging transparent memory sharing between the two devices can carry large overheads. Memory on CPU/GPU systems is typically managed by a software framework such as OpenCL or CUDA, which includes a runtime library, and communicates with a GPU driver. These frameworks offer a range of memory management methods that vary in ease of use, consistency guarantees and performance. In this study, we analyze some of the common memory management methods of the most widely used software frameworks for heterogeneous systems: CUDA, OpenCL 1.2, OpenCL 2.0, and HSA, on NVIDIA and AMD hardware. We focus on performance/functionality trade-offs, with the goal of exposing their performance impact and simplifying the choice of memory management methods for programmers. .
Starting Page	59
Ending Page	69
Page Count	11
File Format	PDF
ISBN	9781450350440
DOI	10.1145/3092255.3092256
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2017-06-18
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Heterogeneous systems Apu Gpgpu
Content Type	Text
Resource Type	Article

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