Locality‐protected cache allocation scheme with low overhead on GPUs

Author:

Zhang Yang1,Xing Zuocheng1,Tang Chuan1,Liu Cang1

Affiliation:

1. National Laboratory for Parallel and Distributed ProcessingNational University of Defense Technology109 Deya RoadChang ShaPeople's Republic of China

Funder

National Natural Science Foundation of China

Specialized Research Fund for the Doctoral Program of Higher Education of China

Publisher

Institution of Engineering and Technology (IET)

Subject

Electrical and Electronic Engineering,Hardware and Architecture,Software

Reference31 articles.

1. NVIDIA CUDA C programming guide v7.5 2015 2015. Available athttp://developer.nvidia.com/nvidia‐gpu‐computing‐documentation. NVIDIA accessed date 2016

2. NVIDIA corp.:NVIDIA's next generation CUDA compute architecture: Fermi 2009.v.1.1. NVIDIA 2009

3. Maxwell:The most advanced CUDA GPU ever made 2014. Available athttps://devblogs.nvidia.com/parallelforall/maxwell‐most‐advanced‐cuda‐gpu‐ever‐made/.NVIDIA accessed date 2015

4. Chen X. Chang L.‐W. Rodrigues C.I. et al.: ‘Adaptive cache management for energy‐efficient GPU computing’.Proc. 47th Annual IEEE/ACM Int. Symp. Microarchitecture 2014 pp.343–355

5. Xie X. Liang Y. Wang Y. et al.: ‘Coordinated static and dynamic cache bypassing for GPUs’.2015 IEEE 21st Int. Symp. High Performance Computer Architecture (HPCA) 2015 pp.76–88

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