NVIDIA vs AMD: GPGPU performance

Question

I\'d like to hear from people with experience of coding for both. Myself, I only have experience with NVIDIA.

NVIDIA CUDA seems to be a lot more popular than the competi

Answer 1

I would like to add to the debate. For us in the business of software, we can compromise raw single-precision performance to productivity but even that I do not have to compromise since, as already pointed out, you cannot achieve as much performance on on ATI's hardware using OpenCL as you can achieve if you write in CUDA on NVIDIA's hardware.

And yes, with PGI's announcement of x86 compiler for CUDA, there won't be any good reason to spend more time and resources writing in OpenCL :)

P.S: My argument might be biased since we do almost all our GPGPU work on CUDA. We have an Image Processing/Computer Vision library CUVI (CUDA for Vision and Imaging) which accelerates some core IP/CV functionality on CUDA.

Answer 2

I am new to GPGPU but I have some experience in scientific computing (PhD in Physics). I am putting together a research team and I want to go towards using GPGPU for my calculations. I had to choose between the available platforms. I decided on Nvidia, for a couple of reasons: while ATI might be faster on paper, Nvidia has a more mature platform and more documentation so it will be possible to get closer to the peak performance on this platform.

Nvidia also has an academic research support program, one can apply for support, I just received a TESLA 2075 card which I am very happy about. I don't know if ATI or Intel supports research this way.

What I heard about OpenCL is that it's trying to be everything at once, it is true that your OpenCL code will be more portable but it's also likely to not exploit the full capabilities of either platform. I'd rather learn a bit more and write programs that utilize the resources better. With the TESLA K10 that just came out this year Nvidia is in the 4.5 TeraFlops range so it is not clear that Nvidia is behind ... however Intel MICs could prove to be a real competitor, especially if they succeed in moving the GPGPU unit to the motherboard. But for now, I chose Nvidia.

Answer 3

The main difference between AMD's and NVIDIA's architectures is that AMD is optimized for problems where the behavior of the algorithm can be determined at compile-time while NVIDIA is optimized for problems where the behavior of the algorithm can only be determined at run-time.

AMD has a relatively simple architecture that allows them to spend more transistors on ALU's. As long as the problem can be fully defined at compile-time and be successfully mapped to the architecture in a somewhat static or linear way, there is a good chance that AMD will be able to run the algorithm faster than NVIDIA.

On the other hand, NVIDIA's compiler is doing less analysis at compile time. Instead, NVIDIA has a more advanced architecture where they have spent more transistors on logic that is able to handle dynamic behavior of the algorithm that only emerges at run-time.

I believe the fact that most supercomputers that use GPUs go with NVIDIA is that the type of problem that scientists are interested in running calculations on, in general map better to NVIDIA's architecture than AMD's.

Answer 4

Having spent some time with OpenCL for GCN cards after a few years of CUDA for Fermi and Kepler, I still prefer CUDA as a programming language and would choose AMD hardware with CUDA if I had an option.

Main differences of NVIDIA and AMD (OpenCL):

For AMD:

• Even with Maxwell, NVidia still has longer command latencies and complex algorithms are likely to be 10 faster on AMD(assuming same theoretical Tflops) after easy optimizations for both. The gap was up to 60% for Kepler VS GCN. It's harder to optimize complex kernels for NVidia in this sense.

• Cheap cards.

• OpenCL is open standard with other vendors available.

For Nvidia:

• Has the Tesla line of hardware that's suitable for reliable high server loads.

• New Maxwell is way more power efficient.

• Compiler and tools are way more advanced. AMD still can't get to implement maxregcout parameter, so you can easily control occupancy on various hardware and their compiler has a lot of random ideas of what is an optimal code that change with every version, so you may need to revisit old code every half a year because it suddenly became 40% slower.

At this point if GPGPU is your goal, CUDA is the only choice, since opencL with AMD is not ready for server farm and it's significantly harder to write efficient code for AMD due to the fact that the compiler always seems to be "in beta".

Answer 5

My experience in evaluating OpenCL floating point performance tends to favor NVIDIA cards. I've worked with a couple of floating point benchmarks on NVIDIA cards ranging from the 8600M GT to the GTX 460. NVIDIA cards consistently achieve about half of theoretical single-precisino peak on these benchmarks.
The ATI cards I have worked with rarely achieve better than one third of single-precision peak. Note that my experience with ATI is skewed; I've only been able to work with one 5000 series card. My experience is mostly with HD 4000 series cards, which were never well supported. Support for the HD 5000 series cards is much better.

Answer 6

Cuda is certainly popular than OpenCL as of today, as it was released 3 or 4 years before OpenCL. Since OpenCL been has released, Nvidia has not contributed much for the language as they concentrate much on CUDA. They have not even released openCL 1.2 version for any driver.

As far as heterogenous computing as well as hand held devices as concerned OpenCl will surely gain more popularity in near future. As of now biggest contributor to OpenCL is AMD, It's visible on their site.