At the other end, even a single GTX 960 would make it onto the list, placing in the 200s.
Sure, you can say that deep learning doesn't need FP64, but it is REALLY unfair to compare this to anything on the TOP500 list, especially when you consider the fact that this is not balanced in terms of memory size or bandwidth (in relation to the number of FLOPs) when you compare it to any real supercomputer class system.
*http://www.anandtech.com/show/10222/nvidia-announces-tesla-p...
though I'm most curious about what motherboard is in there to support NVLink and NVHS.
Good overview of Pascal here: https://devblogs.nvidia.com/parallelforall/inside-pascal/
1 question: will we see NVLink become an open standard for use in/with other coprocessors?
1 gripe: they give relative performance data as compared to a CPU -- of course its faster than a CPU
(Of course, a better metric is that it's getting ~56x the performance at probably ~10x the TDP, but that's not surprising for a GPU with the current state of deep learning code.)
To their credit, the thermal and power engineering needed to get that dense a compute deployment is challenging. (bt, dt, have the corpses of power supplies to show for it.) But the price means that it's going to be limited to hyper-dense HPC deployments by companies that don't have the resources to engineer their own for substantially less money, such as Facebook's Big Sur design: https://code.facebook.com/posts/1687861518126048/facebook-to... . And, of course, the academics and hobbyists will continue to use consumer GPUs , which give much better performance/$ but aren't nearly as HPC-friendly.
What I was getting more at was: I want to know the relative performance compared to another 8 Tesla box. I know comparing apples isn't good marketing, but c'mon.
How much do you think it would really cost to develop an OpenCL equivalent of CuDNN (even a stripped down version, just fast)? I know AMD are struggling but we are talking about allocating a handful of talented engineers
Having C only wasn't a good idea. NVidia was quite clever in giving first class treatment to C++, Fortran and any compiler vendor that wished to target PTX.
Also the visual debugging tools are quite good.
Khronos apparently needed to be hit hard to realise that not everyone wants to be stuck with C for HPC in the 21st century.
Also although Apple is the creator of OpenCL, they don't seem to give much love to it.
Then you have Google caring about it's Renderscript dialect, which doesn't help to the overall uptake in OpenCL.
There isn't a monopoly, rather vendors that lacked the perception to appeal to the developers wanted to have as tooling and performance.
Anyone is free to go use OpenCL, use C or a language with a compiler with a C target, do printf debugging and feel free.
Are any vendors already doing SPIR support?
There's also Intel's MIC to consider now to, although that has a vastly different architecture to GPU. Again performance was similar between MIC and GPU in 2013[3], each performing better where their architecture was more suited, GPUs were capable of providing double the bandwidth for random access data.
In terms of AMD vs NVIDIA, I've not looked into it, I doubt AMD has anything to really compete with NVIDIAs current GPU accelerated compute lines. However again there was always that distinction (re bitcoin?) that AMD cards have better integer arithmetic and NVIDIA better float arithmetic.
Disclaimer: I use CUDA in my research, never tried OpenCL.
[1] http://arxiv.org/abs/1005.2581
[2] http://ieeexplore.ieee.org/xpl/login.jsp?tp=&arnumber=604719...
CUDA had Fortran and C++ since day one and thanks to PTX was quite easy to add support for other languages.
Whereas OpenCL was stuck on "C only" model from Khronos, which forced everyone to use C or generate C code and be constrained to the device drivers.
This has been seen as such a big issue that SPIR and C++ SPIR got introduced with OpenCL 2.0.
Another very important one is debugging support. Last time I checked no one had visual tooling at the same level as NVidia's one.
3.2 KILOwatts sounded insane to me, but I suppose you'll have your own server rack to put it in if you can afford to buy one of these.
3.2KW is less than a dishwasher.
I'd love to show it to my father.
Couple that with the fact that they want you to use their compilers (extremely expensive), on a specialized system that can support the card, and you get a platform that nobody other than supercomputer companies can reasonably use. Meanwhile any developer who want to try something with cuda can drop $200 dollars on a GPU and go, then scale accordingly. I think intel somewhat acknowledged this by having a firesale on phi cards and dev licenses last year but it was only for a passively cooled model (really only works well in servers, not workstations).
Intel do this:
- Offer a $200-400 XEON PHI CARD
- Include whatever compiler needed to use it with the card
- Make this easily buyable
- Contribute ports of Cuda-based frameworks over to Xeon Phi
If you're AMD, do the same thing but replace the phrase Xeon Phi with Radeon/Firepro
The GP100/P100 with the 16nm process probably gives a considerable performance/power advantage over the Tesla... but this gives me the feeling that we may not see consumer or workstation-level Pascal boards for a while.
[1] https://aws.amazon.com/machine-learning/ [2] https://azure.microsoft.com/en-us/services/machine-learning/
Time to use better models like kernel ensembles, maybe they are not that accurate, but they are easier to train on a single CPU.
-unreformed box builder
