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  1. Proceedings of the 2017 ACM SIGPLAN International Symposium on Memory Management (ISMM 2017)
  2. Analyzing memory management methods on integrated CPU-GPU systems
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Bridging the gap between memory performance and massive parallelism: the critical role of programming systems innovations (keynote)
NG2C: pretenuring garbage collection with dynamic generations for HotSpot big data applications
A marshalled data format for pointers in relocatable data blocks
Analyzing memory management methods on integrated CPU-GPU systems
"What's in a name?" going beyond allocation site names in heap analysis
Type-assisted automatic garbage collection for lock-free data structures
Flexible and efficient memory object metadata
Continuous checkpointing of HTM transactions in NVM
A refinement hierarchy for free list memory allocators
Shadow state encoding for efficient monitoring of block-level properties
RTHMS: a tool for data placement on hybrid memory system
Avoiding consistency exceptions under strong memory models

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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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