blocks|key|880899|text|请看NVidia的数据自动化系统，它是最简单的GPU编程平台。有很多很酷的材料可读。home.html|type|unstyled|depth|inlineStyleRanges|entityRanges|offset|length|data|880900|Hello是用GPU做任何计算的。|880901|希望这能有所帮助。|880902|entityMap|0|LINK|mutability|MUTABLE|url|http://www.nvidia.com/object/cuda_home.html^0|16|9|0|0|0|0^^$0|@$1|2|3|4|5|6|7|P|8|@]|9|@$A|Q|B|R|1|S]]|C|$]]|$1|D|3|E|5|6|7|T|8|@]|9|@]|C|$]]|$1|F|3|G|5|6|7|U|8|@]|9|@]|C|$]]|$1|H|3|-4|5|6|7|V|8|@]|9|@]|C|$]]]|I|$J|$5|K|L|M|C|$N|O]]]]

Check out CUDA by NVidia, IMO it's the easiest platform to do GPU programming. There are tons of cool materials to read.
<a href="http://www.nvidia.com/object/cuda_home.html" rel="noreferrer">http://www.nvidia.com/object/cuda_home.html</a>
 

Hello world would be to do any kind of calculation using GPU.

Hope that helps.

blocks|key|885500|text|您可以获得可编程顶点和像素着色器，允许在GPU上直接执行代码以操作将要绘制的缓冲区。这些语言(即OpenGL的GL+Shader+Lang和高级Shader+Lang和DirectX的等价物)是C风格的语法，并且非常容易使用。在XNA游戏演播室和Direct中可以找到HLSL的一些例子。我没有任何合适的GLSL参考，但我相信这里有很多。这些着色器语言提供了巨大的力量来操作在每个顶点或每像素水平上绘制的东西，直接在显卡上绘制，使得阴影、照明和花期这样的东西很容易实现。|type|ordered-list-item|depth|inlineStyleRanges|entityRanges|data|885501|我想到的第二件事是使用openCL为通用GPU的新行编写代码。我不知道如何使用它，但我的理解是，openCL让您开始能够访问显卡和普通cpu上的处理器。这还不是主流技术，似乎是由苹果驱动的。|offset|length|885502|库达似乎是一个热门话题。CUDA是nVidia访问GPU电源的方式。这里是一些介绍|885503|unstyled|entityMap|0|LINK|mutability|MUTABLE|url|http://en.wikipedia.org/wiki/OpenCL|1|http://www.nvidia.com/object/cuda_home.html|2|http://www.gpgpu.org/asplos2008/^0|0|B|6|0|0|0|2|1|Y|2|2|0^^$0|@$1|2|3|4|5|6|7|U|8|@]|9|@]|A|$]]|$1|B|3|C|5|6|7|V|8|@]|9|@$D|W|E|X|1|Y]]|A|$]]|$1|F|3|G|5|6|7|Z|8|@]|9|@$D|10|E|11|1|12]|$D|13|E|14|1|15]]|A|$]]|$1|H|3|-4|5|I|7|16|8|@]|9|@]|A|$]]]|J|$K|$5|L|M|N|A|$O|P]]|Q|$5|L|M|N|A|$O|R]]|S|$5|L|M|N|A|$O|T]]]]

<ol>
<li>You get programmable vertex and
pixel shaders that allow execution 
of code directly on the GPU to
manipulate the buffers that are to
be drawn. These languages (i.e.
OpenGL's GL Shader Lang and High
Level Shader Lang and DirectX's equivalents
), are C style syntax, and really
easy to use. Some examples of HLSL
can be found <a href="http://www.riemers.net/" rel="noreferrer">here</a> for XNA game
studio and Direct X. I don't have
any decent GLSL references, but I'm
sure there are a lot around. These
shader languages give an immense
amount of power to
manipulate what gets drawn at a per-vertex
or per-pixel level, directly
on the graphics card, making things
like shadows, lighting, and bloom
really easy to implement. </li>
<li>The second thing that comes to mind is using
<a href="http://en.wikipedia.org/wiki/OpenCL" rel="noreferrer">openCL</a> to code for the new
lines of general purpose GPU's. I'm
not sure how to use this, but my
understanding is that openCL gives
you the beginnings of being able to
access processors on both the
graphics card and normal cpu. This is not mainstream technology yet, and seems to be driven by Apple.</li>
<li><a href="http://www.nvidia.com/object/cuda_home.html" rel="noreferrer">CUDA</a> seems to be a hot topic. CUDA is nVidia's way of accessing the GPU power. <a href="http://www.gpgpu.org/asplos2008/" rel="noreferrer">Here</a> are some intros</li>
</ol>

blocks|key|1236818|text|我想其他人已经回答了你的第二个问题。至于第一个，CUDA的"Hello“，我不认为有固定的标准，但就我个人而言，我推荐一个并行加法器(即一个与N个整数相加的程序)。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|1236819|如果您查看NVIDIA+SDK中的“还原”示例，则可以将表面上简单的任务扩展到演示大量的CUDA考虑事项，例如合并读取、内存库冲突和循环展开。|1236820|有关更多信息，请参见此演示文稿：|1236821|Harris.pdf|offset|length|1236822|entityMap|0|LINK|mutability|MUTABLE|url|http://www.gpgpu.org/sc2007/SC07_CUDA_5_Optimization_Harris.pdf^0|0|0|0|0|A|0|0^^$0|@$1|2|3|4|5|6|7|R|8|@]|9|@]|A|$]]|$1|B|3|C|5|6|7|S|8|@]|9|@]|A|$]]|$1|D|3|E|5|6|7|T|8|@]|9|@]|A|$]]|$1|F|3|G|5|6|7|U|8|@]|9|@$H|V|I|W|1|X]]|A|$]]|$1|J|3|-4|5|6|7|Y|8|@]|9|@]|A|$]]]|K|$L|$5|M|N|O|A|$P|Q]]]]

I think the others have answered your second question. As for the first, the "Hello World" of CUDA, I don't think there is a set standard, but personally, I'd recommend a parallel adder (i.e. a programme that sums N integers).

If you look the "reduction" example in the NVIDIA SDK, the superficially simple task can be extended to demonstrate numerous CUDA considerations such as coalesced reads, memory bank conflicts and loop unrolling.

See this presentation for more info:

<a href="http://www.gpgpu.org/sc2007/SC07_CUDA_5_Optimization_Harris.pdf" rel="noreferrer">http://www.gpgpu.org/sc2007/SC07_CUDA_5_Optimization_Harris.pdf</a>

blocks|key|880996|text|OpenCL是一种使跨平台库能够编程适用于GPU的代码的努力。它允许一个人写代码，而不知道它将运行什么样的GPU，从而使它更容易使用GPU的一些功能，而不是专门针对几种类型的GPU。我怀疑它不像本地GPU代码那样具有性能(或者GPU制造商允许的本地性能)，但是对于某些应用程序来说，这种权衡是值得的。|type|unstyled|depth|inlineStyleRanges|entityRanges|offset|length|data|880997|它还处于相对早期的阶段(截至这个答案为1.1)，但在业界得到了一些支持--例如，它在OSX10.5和更高版本上得到了本机支持。|880998|entityMap|0|LINK|mutability|MUTABLE|url|http://www.khronos.org/opencl/^0|0|6|0|0|0^^$0|@$1|2|3|4|5|6|7|N|8|@]|9|@$A|O|B|P|1|Q]]|C|$]]|$1|D|3|E|5|6|7|R|8|@]|9|@]|C|$]]|$1|F|3|-4|5|6|7|S|8|@]|9|@]|C|$]]]|G|$H|$5|I|J|K|C|$L|M]]]]

<a href="http://www.khronos.org/opencl/" rel="noreferrer">OpenCL</a> is an effort to make a cross-platform library capable of programming code suitable for, among other things, GPUs. It allows one to write the code without knowing what GPU it will run on, thereby making it easier to use some of the GPU's power without targeting several types of GPU specifically. I suspect it's not as performant as native GPU code (or as native as the GPU manufacturers will allow) but the tradeoff can be worth it for some applications.

It's still in its relatively early stages (1.1 as of this answer), but has gained some traction in the industry - for instance it is natively supported on OS X 10.5 and above.

blocks|key|885544|text|看一看ATI流计算SDK。它以斯坦福大学开发的BrookGPU为基础。|type|unstyled|depth|inlineStyleRanges|entityRanges|offset|length|data|885545|今后，所有GPU的工作都将使用OpenCL进行标准化。这是一项由苹果公司赞助的计划，它将成为图形卡供应商的中性产品。|885546|entityMap|0|LINK|mutability|MUTABLE|url|http://ati.amd.com/technology/streamcomputing/sdkdwnld.html|1|http://graphics.stanford.edu/projects/brookgpu/|2|http://en.wikipedia.org/wiki/OpenCL^0|3|9|0|N|8|1|0|F|6|2|0^^$0|@$1|2|3|4|5|6|7|R|8|@]|9|@$A|S|B|T|1|U]|$A|V|B|W|1|X]]|C|$]]|$1|D|3|E|5|6|7|Y|8|@]|9|@$A|Z|B|10|1|11]]|C|$]]|$1|F|3|-4|5|6|7|12|8|@]|9|@]|C|$]]]|G|$H|$5|I|J|K|C|$L|M]]|N|$5|I|J|K|C|$L|O]]|P|$5|I|J|K|C|$L|Q]]]]

Take a look at the <a href="http://ati.amd.com/technology/streamcomputing/sdkdwnld.html" rel="noreferrer">ATI Stream Computing SDK</a>. It is based on <a href="http://graphics.stanford.edu/projects/brookgpu/" rel="noreferrer">BrookGPU</a> developed at Stanford.

In the future all GPU work will be standardized using <a href="http://en.wikipedia.org/wiki/OpenCL" rel="noreferrer">OpenCL</a>. It's an Apple-sponsored initiative that will be graphics card vendor neutral.

blocks|key|237632|text|首先，CUDA是一个很好的框架。它允许您在C中编写GPGPU内核。编译器将从您的代码中生成GPU微代码，并将在CPU上运行的所有内容发送给您的常规编译器。它只是NVIDIA，但只在8系列卡上工作或更好。您可以查看库达带，看看它能做些什么。在库达SDK中有一些很棒的演示。SDK附带的文档对于实际编写代码来说是一个很好的起点。它将引导您编写矩阵乘法内核，这是一个很好的起点。|type|unstyled|depth|inlineStyleRanges|entityRanges|offset|length|data|237633|entityMap|0|LINK|mutability|MUTABLE|url|http://www.nvidia.com/object/cuda_home.html|1|http://www.nvidia.com/object/cuda_get.html^0|2Y|3|0|3C|5|1|0^^$0|@$1|2|3|4|5|6|7|N|8|@]|9|@$A|O|B|P|1|Q]|$A|R|B|S|1|T]]|C|$]]|$1|D|3|-4|5|6|7|U|8|@]|9|@]|C|$]]]|E|$F|$5|G|H|I|C|$J|K]]|L|$5|G|H|I|C|$J|M]]]]

CUDA is an excellent framework to start with. It lets you write GPGPU kernels in C. The compiler will produce GPU microcode from your code and send everything that runs on the CPU to your regular compiler. It is NVIDIA only though and only works on 8-series cards or better. You can check out <a href="http://www.nvidia.com/object/cuda_home.html" rel="noreferrer">CUDA zone</a> to see what can be done with it. There are some great demos in the <a href="http://www.nvidia.com/object/cuda_get.html" rel="noreferrer">CUDA SDK</a>. The documentation that comes with the SDK is a pretty good starting point for actually writing code. It will walk you through writing a matrix multiplication kernel, which is a great place to begin.

blocks|key|237776|text|另一个进入GPU编程的简单方法，不需要进入数据自动化系统(+CUDA+)或OpenCL，是通过OpenACC实现的。|type|unstyled|depth|inlineStyleRanges|entityRanges|offset|length|data|237777|OpenACC的工作方式类似于OpenMP，编译器指令(如#pragma+acc+kernels)将工作发送到GPU。例如，如果您有一个大循环(只有更大的循环才能真正受益)：|style|CODE|237778|int+i;
float+a+=+2.0;
float+b[10000];
#pragma+acc+kernels
for+(i+=+0;+i+<+10000;+%2B%2Bi)+b[i]+=+1.0f;
#pragma+acc+kernels
for+(i+=+0;+i+<+10000;+%2B%2Bi)+{
++b[i]+=+b[i]+*+a;
}|code-block|syntax|javascript|237779|编辑:不幸的是，只有PGI编译器真正支持OpenACC现在，为NVIDIA卡。|237780|entityMap|0|LINK|mutability|MUTABLE|url|http://www.openacc-standard.org/^0|1B|7|0|0|T|J|0|0|0^^$0|@$1|2|3|4|5|6|7|W|8|@]|9|@$A|X|B|Y|1|Z]]|C|$]]|$1|D|3|E|5|6|7|10|8|@$A|11|B|12|F|G]]|9|@]|C|$]]|$1|H|3|I|5|J|7|13|8|@]|9|@]|C|$K|L]]|$1|M|3|N|5|6|7|14|8|@]|9|@]|C|$]]|$1|O|3|-4|5|6|7|15|8|@]|9|@]|C|$]]]|P|$Q|$5|R|S|T|C|$U|V]]]]

Another easy way to get into GPU programming, without getting into CUDA or OpenCL, is to do it via <a href="http://www.openacc-standard.org" rel="noreferrer">OpenACC</a>.

OpenACC works like OpenMP, with compiler directives (like <code>#pragma acc kernels</code>) to send work to the GPU. For example, if you have a big loop (only larger ones really benefit):

<pre><code>int i;
float a = 2.0;
float b[10000];
#pragma acc kernels
for (i = 0; i &lt; 10000; ++i) b[i] = 1.0f;
#pragma acc kernels
for (i = 0; i &lt; 10000; ++i) {
 b[i] = b[i] * a;
}
</code></pre>

Edit: unfortunately, only the PGI compiler really supports OpenACC right now, for NVIDIA GPU cards.

blocks|key|237557|text|试试GPU%2B%2B和libSh|type|unstyled|depth|inlineStyleRanges|entityRanges|offset|length|data|237558|LibSh链接很好地描述了它们如何将编程语言绑定到图形原语(显然，原语本身也是如此)，GPU%2B%2B用代码示例描述了它们的全部内容。|237559|entityMap|0|LINK|mutability|MUTABLE|url|http://blog.beef.de/thesis/|1|http://libsh.org/wiki/index.php/How_Sh_Works^0|2|5|0|8|5|1|0|0^^$0|@$1|2|3|4|5|6|7|P|8|@]|9|@$A|Q|B|R|1|S]|$A|T|B|U|1|V]]|C|$]]|$1|D|3|E|5|6|7|W|8|@]|9|@]|C|$]]|$1|F|3|-4|5|6|7|X|8|@]|9|@]|C|$]]]|G|$H|$5|I|J|K|C|$L|M]]|N|$5|I|J|K|C|$L|O]]]]

Try <a href="http://blog.beef.de/thesis/" rel="nofollow noreferrer">GPU++</a> and <a href="http://libsh.org/wiki/index.php/How_Sh_Works" rel="nofollow noreferrer">libSh</a>

LibSh link has a good description of how they bound the programming language to the graphics primitives (and obviously, the primitives themselves), and GPU++ describes what its all about, both with code examples.

blocks|key|885627|text|如果您使用MATLAB，就很容易使用GPU进行技术计算(矩阵计算和繁重的数学/数字运算)。我发现它有用的GPU卡以外的游戏使用。请查看下面的链接：|type|unstyled|depth|inlineStyleRanges|entityRanges|data|885628|http://www.mathworks.com/discovery/matlab-gpu.html|offset|length|885629|entityMap|0|LINK|mutability|MUTABLE|url^0|0|0|1E|0|0^^$0|@$1|2|3|4|5|6|7|M|8|@]|9|@]|A|$]]|$1|B|3|C|5|6|7|N|8|@]|9|@$D|O|E|P|1|Q]]|A|$]]|$1|F|3|-4|5|6|7|R|8|@]|9|@]|A|$]]]|G|$H|$5|I|J|K|A|$L|C]]]]

If you use MATLAB, it becomes pretty simple to use GPU's for technical computing (matrix computations and heavy math/number crunching). I find it useful for uses of GPU cards outside of gaming. Check out the link below:

<a href="http://www.mathworks.com/discovery/matlab-gpu.html" rel="nofollow">http://www.mathworks.com/discovery/matlab-gpu.html</a>

Everyone has this huge massively parallelized supercomputer on their desktop in the form of a graphics card GPU.

<ul>
<li>What is the "hello world" equivalent of the GPU community?</li>
<li>What do I do, where do I go, to get started programming the GPU for the major GPU vendors?</li>
</ul>

-Adam

Intro to GPU programming

翻译质量差，导致语言生硬或混乱。

没有提供实际的解决方法或示例。

解答不清晰，无法理解或解决问题。

页面排版不美观，阅读体验差。

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每个人都在他们的桌面上以图形卡GPU的形式拥有这台大规模并行化的超级计算机。什么是相当于GPU社区的"hello world“？为了开始为主要GPU供应商编写GPU程序，我应该做些什么，去哪里？-Adam

问GPU编程简介
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