问题描述
我想听听有编码经验的人。我自己，我只有NVIDIA的经验。
I'd like to hear from people with experience of coding for both. Myself, I only have experience with NVIDIA.
NVIDIA CUDA似乎比竞争对手更受欢迎。 （只是在这个论坛上计数问题标签，'cuda'优于'opencl'3：1，'nvidia'优于'ati'15：1，并且没有'ati-stream'的标签。
NVIDIA CUDA seems to be a lot more popular than the competition. (Just counting question tags on this forum, 'cuda' outperforms 'opencl' 3:1, and 'nvidia' outperforms 'ati' 15:1, and there's no tag for 'ati-stream' at all).
另一方面，根据维基百科，ATI / AMD卡应该有更多的潜力，特别是每一美元。作为今天市场上最快的NVIDIA卡，GeForce 580（500美元），被评为1.6单精度TFlops。 AMD Radeon 6970可以拥有$ 370，它的评级为2.7 TFlops。 580具有在772MHz的512个执行单元。 6970在880 MHz具有1536个执行单元。
On the other hand, according to Wikipedia, ATI/AMD cards should have a lot more potential, especially per dollar. The fastest NVIDIA card on the market as of today, GeForce 580 ($500), is rated at 1.6 single-precision TFlops. AMD Radeon 6970 can be had for $370 and it is rated at 2.7 TFlops. The 580 has 512 execution units at 772 MHz. The 6970 has 1536 execution units at 880 MHz.
AMD相对于NVIDIA的纸张优势有多现实？它是否可能在大多数GPGPU任务中实现？整数任务会发生什么？
How realistic is that paper advantage of AMD over NVIDIA, and is it likely to be realized in most GPGPU tasks? What happens with integer tasks?
推荐答案
与nvidia相比，隐喻说ati有一个很好的引擎。
但是nvidia有一个更好的车：D
Metaphorically speaking ati has a good engine compared to nvidia.But nvidia has a better car :D
这主要是因为nvidia投入了大量的资源（金钱和人力）用于科学计算（BLAS，FFT），然后再次很好地推广它。这可能是因为CUDA在这里比ati（或OpenCL）主导了标签
This is mostly because nvidia has invested good amount of its resources (in money and people) to develop important libraries required for scientific computing (BLAS, FFT), and then a good job again in promoting it. This may be the reason CUDA dominates the tags over here compared to ati (or OpenCL)
对于GPGPU任务中实现的优势，在其他问题（取决于应用程序），如内存传输带宽，良好的编译器，甚至可能是驱动程序。 nvidia有一个更成熟的编译器，一个更稳定的驱动程序在linux（linux，因为它的使用在科学计算中广泛使用），倾斜的平衡有利于CUDA（至少现在）。
As for the advantage being realized in GPGPU tasks in general, it would end up depending on other issues (depending on the application) such as, memory transfer bandwidth, a good compiler and probably even the driver. nvidia having a more mature compiler, a more stable driver on linux (linux because, its use is widespread in scientific computing), tilt the balance in favor of CUDA (at least for now).
编辑 2013年1月12日
自从我做这个职位已经两年了，它仍然似乎有时吸引观点。所以我决定澄清一些事情。
It's been two years since I made this post and it still seems to attract views sometimes. So I have decided to clarify a few things
- AMD已经加强了游戏。他们现在有BLAS和FFT库。
- 英特尔推出了Xeon Phi，支持OpenMP和OpenCL。它还具有使用现有x86代码的能力。
- NVIDIA和CUDA在图书馆范围内仍有优势。然而，他们可能没有像以前一样专注于OpenCL。
简而言之，OpenCL已经缩小了过去两年的差距。这个领域有新的玩家。但是CUDA仍然领先一步。
In short OpenCL has closed the gap in the past two years. There are new players in the field. But CUDA is still a bit ahead of the pack.
这篇关于NVIDIA与AMD：GPGPU性能的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持！