GPU在计算中有什么未来？ [英] What future does the GPU have in computing?

问题描述

您的CPU可能是四核，但是你知道一些显卡目前有超过200个内核吗？我们已经看到了当今图形卡的GPU可以做什么，当谈到图形。现在他们也可以用于非图形任务，在我看来，结果是什么没有惊人的。一个适合并行处理的算法在GPU上的速度比CPU上的速度要快得多。

Your CPU may be a quad-core, but did you know that some graphics cards today have over 200 cores? We've already seen what GPU's in today's graphics cards can do when it comes to graphics. Now they can be used for non-graphical tasks as well, and in my opinion the results are nothing short of amazing. An algorithm that lends itself well to parallelism has the potential to be much, much faster on a GPU than it could ever be on a CPU.

有几种技术使所有这一切成为可能：

There are a few technologies that make all of this possible:

1。） CUDA 。它似乎是最知名和有据可查的。不幸的是，它只能在NVidia显卡上工作。我已经下载了SDK，尝试了一些样品，并有一些真棒的东西，在CUDA中完成。但是，它限制在NVidia卡的事实使我怀疑它的未来。

1.) CUDA by NVidia. It seems to be the most well-known and well-documented. Unfortunately, it'll only work on NVidia video cards. I've downloaded the SDK, tried out some of the samples, and there's some awesome stuff that's being done in CUDA. But the fact that it's limited to NVidia cards makes me question its future.

2。） Stream 。 ATI等同于CUDA。如您所料，它只能在ATI卡上使用。

2.) Stream by ATI. ATI's equivalent to CUDA. As you might expect, it will only work on ATI cards.

3） OpenCL - Khronos集团已经制定了这一标准，但仍处于初级阶段。我喜欢OpenCL的想法。希望它应该得到大多数视频卡制造商的支持，并应该使交叉视频卡开发更容易。

3.) OpenCL - The Khronos Group has put together this standard but it's still in its infancy stages. I like the idea of OpenCL though. The hope is that it should be supported by most video card manufacturers and should make cross-video card development that much easier.

但是其他技术的非图形GPU编程来了，什么显示最有希望？你会看到或者你想看到这些技术被内置到一些主流开发框架（如.NET），使它更容易吗？

But what other technologies for non-graphical GPU programming are coming and what shows the most promise? And do you see or would you like to see these technologies being built into some of the mainstream development frameworks like .NET to make it that much easier?

推荐答案

我预见这种技术将成为流行和主流，但这需要一些时间。我的猜测是大约5到10年。

I foresee that this technology will become popular and mainstream, but it will take some time to do so. My guess is of about 5 to 10 years.

正如你正确地指出的，采用该技术的一个主要障碍是缺乏一个通用库适配器 - ATI和nVidia。直到这个问题解决到可接受的程度，这项技术将不会进入主流，并且将停留在运行在特定硬件上的定制应用程序的利基。

As you correctly noted, one major obstacle for the adoption of the technology is the lack of a common library that runs on most adapters - both ATI and nVidia. Until this is solved to an acceptable degree, the technology will not enter mainstream and will stay in the niche of custom made applications that run on specific hardware.

至于集成使用C＃和其他高级管理语言 - 这将需要更长时间，但XNA已经表明自定义着色器和管理环境可以混合在一起 - 在一定程度上。当然，着色器代码仍然不在C＃中，并且有这样做的几个主要障碍。

As for integrating it with C# and other high-level managed languages - this will take a bit longer, but XNA already demonstrates that custom shaders and managed environment can mix together - to a certain degree. Of course, the shader code is still not in C#, and there are several major obstacles to doing so.

GPU代码的快速执行的主要原因之一是它对代码可以和不能做什么有严格的限制，它使用VRAM而不是通常的RAM。这使得很难将CPU代码和GPU代码组合在一起。虽然解决方法是可能的，但它们实际上会抵消性能提升。

One of the main reasons for fast execution of GPU code is that it has severe limitations on what the code can and cannot do, and it uses VRAM instead of usual RAM. This makes it difficult to bring together CPU code and GPU code. While workarounds are possible, they would practically negate the performance gain.

我看到的一个可能的解决方案是为C＃创建一个有其局限性的子语言，到GPU代码，并且具有与使用C＃代码通信的严格定义的方式。然而，这不会有什么不同，我们已经 - 只是更舒服的写，因为一些语法糖和标准库函数。不过，现在这个年龄还是很老了。

One possible solution that I see is to make a sub-language for C# that has its limitations, is compiled to GPU code, and has a strictly defined way of communicating with the ususal C# code. However, this would not be much different than what we have already - just more comfortable to write because of some syntactic sugar and standard library functions. Still, this too is ages away for now.

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