The landscape of GPGPU performance modeling tools-Reference-Cited by-同舟云学术

The landscape of GPGPU performance modeling tools

Published:2016-08 Issue: Volume:56 Page:18-33
ISSN:0167-8191
Container-title:Parallel Computing
language:en
Short-container-title:Parallel Computing

Author:

Madougou Souley^ORCID,Varbanescu Ana,de Laat Cees,van Nieuwpoort Rob

Publisher

Elsevier BV

Subject

Artificial Intelligence,Computer Graphics and Computer-Aided Design,Computer Networks and Communications,Hardware and Architecture,Theoretical Computer Science,Software

Reference39 articles.

1. Press Release: AMD Delivers Enthusiast Performance Leadership With the Introduction of the ATI Radeon 3870 X2;Advanced Micro Devices (AMD) Inc.,2008

2. An adaptive performance modeling tool for GPU architectures;Baghsorkhi;SIGPLAN Not.,2010

3. Analyzing CUDA workloads using a detailed GPU simulator;Bakhoda,2009

4. An idiom-finding tool for increasing productivity of accelerators;Carrington,2011

5. AMD CodeXL. http://developer.amd.com/tools-and-sdks/opencl-zone/codexl (accessed 19.10.14).

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