XUnified: A Framework for Guiding Optimal Use of GPU Unified Memory-Reference-Cited by-同舟云学术

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XUnified: A Framework for Guiding Optimal Use of GPU Unified Memory

Published:2022 Issue: Volume:10 Page:82614-82625
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Xu Hailu¹^ORCID,Lin Pei-Hung²,Emani Murali³^ORCID,Hu Liting⁴,Liao Chunhua²

Affiliation:

1. Department of Computer Engineering and Computer Science, California State University, Long Beach, CA, USA

2. Lawrence Livermore National Laboratory, Livermore, CA, USA

3. Argonne National Laboratory, Lemont, IL, USA

4. Department of Computer Science and Engineering, University of California at Santa Cruz, Santa Cruz, CA, USA

Funder

U.S. Department of Energy by Lawrence Livermore National Laboratory

National Science Foundation

Argonne Leadership Computing Facility, which is a DOE Office of Science User Facility supported

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering

Link

https://ieeexplore.ieee.org/ielam/6287639/9668973/9848789-aam.pdf

Reference32 articles.

1. A GPGPU compiler for memory optimization and parallelism management

2. Machine Learning Guided Optimal Use of GPU Unified Memory

3. Implementing the OpenACC Data Model

4. Rodinia: A benchmark suite for heterogeneous computing

5. Optimizing Data Placement on GPU Memory: A Portable Approach

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