FGC 2010 NVIDIA Best Paper Awards

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FGC 2010 NVIDIA Best Paper Awards

Two NVIDIA Best Paper Awards will be presented to the two most outstanding
papers presented at FGC 2010.
Each winner will receive a Fermi graphics card sponsored by NVIDIA.


************************************************************************
CALL FOR PAPERS

FGC 2010  -  The First International Workshop on Frontier of GPU Computing

To be held in conjunction with CIT 2010

Bradford, UK, 29 June - 01 July, 2010

http://www.comp.hkbu.edu.hk/~chxw/fgc2010/index.php

************************************************************************
Journal Special Issues

Distinguished selected papers accepted and presented in FGC 2010, after
further extension and revisions, will be published in special issues of the
following prestigious SCI-Indexed Journals:
- Journal of Supercomputing ?Springer
- Journal of Computer and System Sciences ?Elsevier
- Concurrency and Computation: Practice and Experience - John Wiley & Sons

Scope

We are undergoing a new revolution on parallel processor technologies,
especially the Graphic Processing Units. GPUs have become widely used
nowadays to accelerate a broad range of applications, including
computational finance, numerical computing, image/video processing,
engineering simulations, quantum chemistry, just to name a few.

The goal of this workshop is to provide a forum for researchers and
practitioners to discuss and share their research and development
experiences and outputs on the massively parallel GPU platforms, software
development tools, optimization techniques, parallel algorithm design, and
all kinds of successful applications. We solicit original and previously
unpublished papers addressing research challenges and advances towards the
design, implementation and evaluation of massively parallel GPU computing.
Topics of interest include, but are not limited to:

- Applications on GPU Computing
- Performance Modeling and Benchmarking
- Processor Architectures
- Programming Languages and Compilers
- Middleware and Libraries
- GPU Clusters
- Self-configuration and Fault-tolerance

Paper Submission

Authors are invited to submit manuscripts reporting original unpublished
research and recent developments in the topics related to the workshop. The
length of the papers should not exceed 6 pages + 2 pages for overlength
charges (IEEE Computer Society Proceedings Manuscripts style: two columns,
single-spaced), including figures and references, using 10 fonts, and number
each page. All papers will be peer reviewed and the comments will be
provided to the authors. The accepted papers will be published together with
those of other workshops by the IEEE Computer Society Press.

To submit your paper, please access EasyChair through
http://www.easychair.org/conferences/?conf=fgc2010.
If you do not have an EasyChair account, please obtain one from:
http://www.easychair.org/conferences/account_apply.cgi?iid=21166.

Important Dates

- Submission Deadline:  12 March, 2010
- Author Notification:  05 April, 2010
- Final Manuscript Due: 30 April, 2010
- Registration Due: 05 May, 2010
- Conference Date: 29 June - 01 July, 2010

Organization:

Workshop Co-chairs:

Xiaowen Chu, Hong Kong Baptist University, Hong Kong, China Yangdong Deng,
Tsinghua University, Beijing, China Wei Ge, Chinese Academy of Science,
Beijing, China

Program Committee:

Tor Aamodt, University of British Columbia, Canada
David Bader, Georgia Institute of Technology, USA
Lorena Barba, Boston University, USA
John Cavazos, University of Delaware, USA
Yifeng Chen, Peking University, China
Jack Dongarra, University of Tennessee, USA
Geoffrey Fox, Indiana University, USA
Markus Hadwiger, King Abdullah University of Science and Technology, Saudi
Arabia
Zhiyi Huang, University of Otago, New Zealand
Yixin Jiang, Tsinghua University, China
Hai Jin, Huazhong University of Science and Technology, China
Xiaolong Jin, University of Bradford, UK
David Kaeli, Northeastern University, USA
Naga Kandasamy, Drexel University, USA
Volodymyr Kindratenko, University of Illinois Urbana-Champaign, USA
Miriam Leeser, Northeastern University, USA
Zongpeng Li, University of Calgary, Canada
Jiangchuan Liu, Simon Fraser University, Canada
Shijun Liu, Shandong University, China
Youquan Liu, Chang'an University, China
Qiong Luo, Hong Kong University of Science and Technology, Hong Kong
John Michalakes, National Center for Atmospheric Research, USA
Hanspeter Pfister, Harvard University, USA
P. Sadayappan, Ohio State University, USA
Rainer Spurzem, Chinese Academy of Sciences, China, and Zentrum fur
Astronomie Univ. Heidelberg, Germany
Robert Strzodka, Max Planck Institut Informatik, Germany

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整理几年前写的文章，或许对初学CUDA编程的朋友有帮助：CUDA 线程执行模型分析（一）招兵------ GPU的革命CUDA 线程执行模型分析（二）大军未动粮草先行------GPU的革命CUDA硬件实现分析（一）------安营扎寨-----GPU的革命CUDA硬件实现分析（二）------规行矩步------GPU的革命CUDA编程接口（一）------一十八般武器------GPU的革命C

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9. CUDA shared memory使用------GPU的革命

9. CUDA shared memory使用------GPU的革命 序言：明年就毕业了，下半年就要为以后的生活做打算。这半年，或许就是一个抉择的时候，又是到了一个要做选择的时候。或许是自己的危机意识比较强，一直都觉得自己做得不够好，还需要积累和学习。或许是知足常乐吧，从小山沟，能到香港，一步一步，自己都比较满足，只是心中一直抱着一个理想，坚持做一件事情，坚持想做点事情，踏踏实实，曾经失败过，

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CUDA VS2005 Wizard

经过两天的开发，终于写好了CUDA 在VS2005上的project 生成向导。两天前还不知道怎么生成VS2005的生成向导，昨天晚上的时候想放弃～想想，既然弄到一半了，就坚持下来了～1.创建Custom Wizard 然后添加自己的内容。里面涉及html编程、js编程、VC custom rule、default.vsproj，vsdir，vsz，ico制作（>_差一点就晕了，还好坚持下来，其实

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CUDA编程接口（一）------一十八般武器------GPU的革命

  CUDA编程接口（一）------一十八般武器------GPU的革命序言：所谓一十八般武器，不同的年代又有不同的说法，最早的汉武年间的：矛、镗、刀、戈、槊、鞭、锏、剑、锤、抓、戟、弓、钺、斧、牌、棍、枪、叉。到三国的：九长：刀、矛、戟、槊、镗、钺、棍、枪、叉；九短：斧、戈、牌、箭、鞭、剑、锏、锤、抓。再到明清的：弓、弩、枪、刀、剑、矛、盾、斧、钺、戟、黄、锏、挝、殳（棍）、叉、耙

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Visual Assist 添加支持*.cu文件

 Visual Assist 添加支持*.cu文件1.       打开注册表，在HKEY_LOCAL_MACHINE/SOFTWARE/Microsoft/VisualStudio/8.0/Languages/File Extensions/ 下面添加子键 .cu 然后copy .cpp的键值到.cu。这样才能表示cu也是VS下的VC的工程文件。2.       打开注册表HKE

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并行计算中的粒度

并发和并行：今天和师妹讨论问题，师妹说到了现在要做的一个项目。要做大规模的并行模拟事件的模拟。问我是否可以在GPU上实现。我们这里拿一群人来分一盒饼干来做例子。一群人要怎么来分这些饼干啦？这里有M个饼干，有N个人要分这个饼干。并发，就是每一个哪一个小饼干，然后各自吃各自的饼干，然后每一轮N个人就分掉了N个饼干。当然这里假设M比N大。并行，就是N个人同时先把一个饼干分了，吃完以后再接着分下面的M-1

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推荐几本书---GPU，并行算法，多核

《并行算法导论》《快速算法与并行型号处理》《并行程序设计C、MPI与OpenMPI》《多核程序设计》 《多核程序设计技术---通过软件多线程提升性能》《软件优化技术---IA-32平台的高性能手册》《Win32的多线程程序设计》《GPU编程精粹1》《GPU编程精粹2》《GPU编程精粹3》《Cg tutorial》

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怎么样得到显卡（GPU）的运行状态啦？

现在主流的显卡有两种，一类是Nvidia的GPU，一种是AMD的GPU。很多人都在做GPU的时候，都想知道GPU的特性，想知道使用时候的状态，有问题，频率，内存使用情况，占有率，很多人问道了，是否可以使用系统底层的API得到？其实系统给的API并不完善，经常得不到完全的信息。--------------------------------------------------------------

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