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Comment by phelm | Hacker News Reader

I am looking forward to OpenCL catching up with CUDA in maturity and adoption, so that NVidia's monopoly in Silicon for deep learning will come to an end.

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I'm at GPU Technology Conference, where this computer was announced this morning. The amount of "wood behind the arrow" NVidia has for AI is insane. Even though the current demographic of GPU development is full of HPC simulations, physics, graphics... it's obvious that their biggest thrust is in machine learning. I don't think OpenCL can compete with this amount of money and enthusiasm. NVidia is rich and their engineers are very good. Some big changes would need to happen before OpenCL catches up to CUDA.

I think it's just really bad management from AMD. Took them ages to wake up, and now they have what looks like a relatively small team on their Boltzmann initiative. Remains to be seen what happens to it.

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

For that to happen OpenCL has to be at the same level as CUDA in language support and tooling.

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?

What monopoly? You totally have a choice, it's just that NVIDIA made a large bet on GPGPU and it is paying off for them. You don't see AMD heavily pushing their cards for compute purposes or developing computational developer relations.

You often don't have a choice because a large amount of GPGPU software is written using CUDA, which is Nvidia-specific.

NVIDIA does not have a monopoly in the traditional sense. But yes, the have a de facto one because there is no viable competition.

It's like saying MATLAB has a monopoly in academic research because so much of the code is written in it. That is slowly changing and moving over to Python now, which is great. Maybe OpenCL will get there someday, but I don't see it happening any time soon.

this is wrong. No mainstream deep learning library uses openCL, and the non-mainstream ones that do are much much slower. I remember reading up to 10x slower, but I can't seem to find the reference right now.

You are correct. My initial response was a pedantic point about the semantic use of monopoly in this context, which isn't helpful.

I would love it if AMD would care more about GPGPU, but they don't, and NVIDIA has little incentive to make their OpenCl drivers equal to their CUDA ones.

clang now has a mostly-working CUDA frontend (disclaimer, I work on it). And it has an AMD GPU backend (whether this is in a good state I don't know). I don't expect that putting these pieces together would be a huge project.

Me too. I really want to see some benchmarks between cuda code and opencl code generated from cuda with AMDs compiler. Actually if anyone has a geforce/tesla get on this!

I haven't seen any recent benchmarks, but ones from 2011 all seemed to show CUDA and OpenCL on open footing in terms of performance when optimised properly.[1][2] CUDA simply had better library support, and a more well defined and uniform architecture to target. Whereas OpenCL is likely to require more programming to fill in the gaps for library support, and different optimisations depending on the architecture you wish to target. I'm guessing since then, the CUDA compiler may have improved somewhat in terms of optimisation based on some micro-benchmarking research I was looking the at the other day.

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...

[3] http://arxiv.org/abs/1311.0378

Would you mind trying the AMD compiler? http://gpuopen.com/compute-product/hip-convert-cuda-to-porta... I'd be interested in seeing a benchmark between some original cuda code and the opencl output of this compiler on the same gpu.

It doesn't look like AMD's HIP effort compiles to OpenCL, but to C++, that works on specific AMD GPUs (Fiji R9 Fury X, R9 Fury, R9 Nano).

Also, much of the speed gains for ML on NVidia hardware come from CuDNN - there is no equivalent for OpenCL or AMD hardware

I don't think CUDA vs OpenCL is the real issue. It's the libraries that come with them. It matters little in which language they were written, since they are closed source for the most part.

AMD's Boltzmann initiative won't solve the lack of libraries.

What hardware could OpenCL even run on that would come remotely close to what this system has to offer?

OpenCL runs on Nvidia GPUs, so you could do an apples to apples comparison on this system.

Fair enough but how does that further the goal of having alternatives to Nvidia? The truth is the problem isn't cuda vs. opencl. There simply aren't good alternatives to the hardware Nvidia provides. If the hardware was there, you'd see people switch to OpenCL.

You can, but IIRC Nvidia actually compiles OpenCL to CUDA in the Driver.

The problem is that no one[1] uses OpenCL because the performance isn't there. There is little sign of that changing, too.

Another reasons are language support and tooling.

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.

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