> They don't have anything to gain when they already have CUDA dominating the industry
Everyone working in it seems to agree there are good reasons for it dominating. Just easier to work with overall.
Everyone working in it seems to agree there are good reasons for it dominating. Just easier to work with overall.
Also, this isn't a monopoly, OpenCL exists.
There are 2 NVIDIA drivers. It turns out pinned memory transfers are 2x faster with the Tesla driver (vs the Geforce driver). This can matter a lot for some workloads. For some reason you can't use the faster driver with cheap consumer cards.
So, in my view NVIDIA is dumbing down performance for OpenCL and CUDA alike, and also slowing down the unavoidable OpenCL adoption.
Of course, while it's the cheap consumer garbage ^W^W pro-gaming hardware, the prices are okay, since these are kept in check by AMD. Once we get to the relabelled consumer garbage ^W^W^W professional compute hardware, you pay for it. Dearly. Why? No one that keeps them in check through competition.
EDIT: even the Titan Xp while being much more expensive than the 1080Ti is still vastly cheaper than Teslas
From my view, it's not just that CUDA > OpenCL. Rather, it's CUDA ecosystem >>> OpenCL ecosystem. The tools, the libraries, the documentation, the community - all of these are superior on the CUDA side. If you give all those up to use OpenCL, how do you make up for that cost?
I am sure their Windows driver actually worked. However that still meant that for my hardware OpenCL was about as portable as CUDA. Just with a Windows lock-in instead of a NVIDIA lock-in.
Edit: Just to note this was a few years ago. I have no idea what the current state is.
It's not perfect, but it's already quite good, and improving rapidly.
https://github.com/GPUOpen-ProfessionalCompute-Tools/HIP
So when is Intel going to join the party?
https://www.intelligentinvestor.com.au/2012/07/ykk-the-zippi...
Then I got tired of waiting 4 years for any of the GPU computing software I was interested in to support it and bought an Nvidia GPU and a PC to run it instead and became part of the problem.
Since SPIR-V allows you to effectively define your own Shader APIs, and OpenCL has more and more bindings with other platforms, including ones that support the web like Go, Javascript and a new support layer for WebGL, it is absolutely a game (no pun intended) for Nvidia to lose in the longrun.
When has a monopoly never not been caught frantically trying to buy out knockoff companies that spawned from teams developing on opensrouce alternatives in innovative places after they became comfortable and confident with being a closed source monopoly?
On top of this, graphics programmers, the games industry and advanced web support, especially now that AWS and I think google cloud integrate server hosting with GPUs (and companies like Blazing DB for easier integration) will only exponeniate the rate of advantage opensource has since this is a race with some of the best programmers in the world, with a ton of money behind it.
I am curious what NVIDIAS end game is here. Already they were trailing behind the after party with their releases post AMD Ryzen, and I havn't heard anything at all exciting on NVIDIAs part to counter since the Vulkan release and SPIR-V integration announcements, which are some of the most promising I've seen yet, and expansive for programmers who have otherwise avoided this platform due to the extremely indepth learning curve and requirements to be somewhat experienced C/C++ programmers.
It took me 8months to be able to write a good kernel and some good host code with opencl because I did the entire integration in C and C++ and I've been developing with it since 1.2, but with more languages a broader range of support applications, perspectives and contributions will go to the opensource dev.
So far, outside of currently dominating the industry and embedding some long term runway with large contracts and having a head on red tape involved with larger corporations they have contracts for hardware and support embedded with, I don't see what advantages NVIDIA is getting the industry excited about in the long run.
When it comes to savvy programmers in Silicon Valley, a friend of mine who works as a PhD programmer in Deep Learning with Google Scientists did an unofficial ad hoc poll on her twitter, asking people why so much opencl hate and so much CUDA in the valley? There were lots of responses from lots of people, but I only saw on repeated reason over and over again, the initial learning curve is lower with CUDA and its more convenient. There were no specs of performance advantages in discussion.
In the functionalities I apply OpenCL to, CUDA doesn't even offer the same functionality, and in many ways processes algorithms by simply parsing out algorithms CPUs utilized, but OpenCL never assumed this as an optimal path, and reconsiders the entire structure of the algorithm and data its working with, often times reorganizing formats for processing entirely different from CPUs that NVIDIA incorporates as a default.
It's more work to learn than CUDA, but the advantages are worth it in my experience so far.
As a last consideration, When it comes to NVIDIA trying to make their products compatible with OpenCL and OpenGL, I predict that promise of that to be delayed at best, as it took NVIDIA 5 years to make their hardware std OpenCl 1.2 compatible. Vulkan is extremely large in terms of the source code, and SPIR-V is nontrivial, OpenCL is standard 2.0 now, so if getting OpenCL 1.2 was hard for them to integrate in under 5 years, we can expect a lot longer given the new releases.
The learning curve is now going away with the recent releases, so whats the next step with NVIDIA? Good question. The only advantage they seem to have is that they have an advantage right now, but that becomes circular and perhaps backwards going forward.
This strangely reminds me of Microsoft getting ubuntu to run in a Windows partition checksum for checksum, to offer cross compatibility with other operating systems as an advantage to stay competitive in the demographic that produces the most advancement within their own industry.
Can you explain what you mean? Reads like gibberish.
OpenCL does something similiar with the null values, but redesigns the entire compression format reading in matrix data and the entire computation of accessing the tiles of data differently based on the format they have for parsing and accessing the data.
I am trying to find a good explanation online, but I can only find what I have in the OpenCL 1.2 Book Chapter 22 on SPMV, where they visualize and explain CUDAs format, and then OpenCLs format, and provide performance comparisons between the two methods using the standard 22 matrix sets provided by the University of Florida.
OpenCLs performance was better on every metric, the smallest increase in performance cutting computation time by one half, the rest were closer to a third. They ran their own CUDA testing, but also provide the whitepaper results from CUDAs testing, and use NVIDIAs reported results as their official comparison.
For an influx of repetitive real time data reading in millions of data points every few seconds, this kind of advantage is not of a negligible significance.
It took a long time to work with but of course now I can reap the benefits every few seconds on large datasets forever so I found it worth diving into the performance specs on this case.
While I cant find an online visual of the designs for CUDA vs OpenCL explained in CH22, the source code for the std SPMV I speak of here is on one of the authors github: "bgaster" is the username. You should be able to download the code working cross platform and read in matrices of your choice, and compare performance on your own if you have datasets you want to look at.
The OpenCL book I highly recommend. It explains how to use opencl and provides comprehensive examples, walking through concepts with code for 22 chapters. The source code for that book is there with the rest of bgasters stuff. It's definitely not a trivial language to take on. The learning curve is steep. However, I learned GPUs through OpenCL, so I don't know if it would be easier coming from CUDA, biases about what to expect because the learner is already reaping the benefits of CUDA aside.
I took time initially to read through the entire 1.2 openCL API over a couple of weeks. It's one of the most comprehensive and detailed API's I've ever read and I find myself dissapointed with documentation of other groups in comparison. Once you are familiar with the scope of functionality available to you, I typically keep the PDF API open and search F for concepts and find whats available to me as I code through things. The most recent API is just as good and can be found here:https://www.khronos.org/registry/OpenCL/specs/opencl-2.2.pdf
Also, one of the authors of OpenCL works fulltime at Apple now, and opencl comes installed and simply working on any OSX operating system. So if you happen to have a mac, it should be relatively easy to download some working source code and try out.
I have OSX, and was also able to download an SDK for my NVIDIA graphics card on an asus zenbook running fedora 24, and got it up and running just fine in an hour or so, for total install and testing.
I think the whole point of Vulkan and particularly SPIR-V, was to offer support for broadened languages including but not limited to Javascript.
OpenCL even has go bindings now.
SPIR-V allows you to define your own SHADER APIs.
Right now, it's alot to learn, but going forward, it hard to ignore the advantages in the functionalities and the broadened support to lower the barrier for learning by increasing the number of languages you can work with OpenCL in, particularly ones that predominately support web dev as we move to cloud developing and hosting AWS and Google Cloud with GPUs.
The cloud is a big one here to consider for potential shift in market share because if one group hosting the cloud finds OpenCL to be particularly beneficial, all they have to do is offer support ontop of a stack like Blazing DB, which I imagine a GPU server support stack will be engulfed within cloud platforms backend within 5 years anyways, and suddenly thousands and thousands of companies rely on it and it becomes and industry standard.
The shift in a market where cloud hosting exists on GPUs, becomes more based on what large companies who have the money and talent to go in depth and scale out support for OpenCL based functionalities moreso than the barrier every single developer or hip web dev hosting on the cloud is wanting to overcome to invest in low level kernel writing and hardware optimization.
Throw the games industry in with Vulkan, with opensource games on PCS always competing with console based games, and I see alot more support in the long run with OpenCL.
Is it broken in any way?