I just upgraded to Adobe CS6. The Mercury playback engine and most of the GPU accelerated effects are written for Cuda enabled cards. CUDA has been developed specifically for NVIDIA GPUs. Hence, CUDA can not work on AMD GPUs. Internally, your CUDA program will be go through a. CUDA has a lot of appeal thanks to its high-level runtime API, but as of its benefits become hurdles, and you start approaching the driver API.
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Note however that this still does not mean that CUDA runs on AMD GPUs. You can run NVIDIA® CUDA™ code on Mac, and indeed on OpenCL GPUs in. How to get AMD's “GPUOpen” or "Boltzmann Initiative" to convert “CUDA” for AMD's “MSI Radeon R9 X LIGHTNING” to enable GPU. Do you have a computer with AMD GPUs? If so, please ensure that you never install NVIDIA CUDA drivers to your system unless a CUDA card.
That would be very much beneficial for you.. Extra irony points for chairman of OpenCL working group being an Nvidia department head. Log in. I have to forget Solidworks Visualize altogether…. What Should I Do? We need to have both on this new machine and we need them to both work properly.
If so, contact support here: March 21, at 1: So… what to do here? Albin says: March 23, at March 28, at 9: Thanks, Kevin. Theodore Athans says: March 21, at March 24, at 6: Elvin Ho says: March 23, at 5: Bill says: MarkS says: March 23, at 8: March 25, at 5: March 28, at 8: Evan says: March 27, at 8: Rod Cole says: March 27, at 9: I may try rolling CUDA back to 5. Best, Kevin.
OpenCL vs. CUDA: Which Has Better Application Support? | Create Pro
March 29, at 3: March 29, at 6: Thanks so much for reporting your findings. Cheers, Kevin. Phil Gibson says: March 28, at 1: March 29, at 1: Well there is this as well: Hello, Meh — where did you hear about such a hack? There was an old driver hack called hybrid physx, where you could have an amd card in your system and you could stick in an nvidia card and use it for dedicated physx. I successfully did it, however the upkeep and managing updates, its really not worth it. Both have their pros and cons.
But who knows what the future holds.
Avoid installing NVIDIA CUDA drivers on computers with AMD GPUs
As for the compatibility list, that is simply comprised of what the official websites offer by way of information. Blackmagic etc all support more OpenCL than Cuda now. Maya is having OpenCL features. GPGPU wont affect the entire application, only what features have been written into the program.
This is the most practical and best explained article on this topic that I have found. Thank you! Could I please make a request? Also, Opencl is a standard supported by Intel too, quicksync rendering and all of that, support for OpenCl can only grow.
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This is absolutely huge.
AMD keeps attacking nV's proprietary techs and destroying their barrier holds one by one. I have nothing but respect for GPU Open, keep it coming red team.
If OpenCL wasn't such a shit show, they would already have a bigger market share, but as it stands CUDA is just easier on the developer.
The tools are much better, OpenCL does not even have a real maintained debugger right now. Besides the OpenCL docs are just utter shit as well. So the best thing they could do now is make their documentation and tool chain up to par with nvidia, otherwise this is just going to be a build step in the CUDA software project to build for a different target.
One day Well, they're promoting it, SyCL builds on top of it but I don't like it. With 2. Support of OpenCL in their new toolchain is literally coming out any day now before as a developer preview. Next year it'll be a proper release and the situation will start being rectified. HIP is effectively cuda, and limited by the model cuda presents - openCL did some things better than cuda in terms of queues and device programming - it's easier and a more solid foundation for scaling programs than the close-but-different constructs cuda has.
It totally makes sense to go that route. Honestly, I see APU's as the way of the future regardless since everyone is competing for "smallest, thinnest, most efficient" ect. Our main problem was that our platform was Tesla Kepler based cards on a remote linux based machine. Naught at least somewhat provides tools for CUDA in this scenario. Maybe not a fault of the standard, but of the body pushing that standard, a spec could include mandatory cross platform tools.
Ooh well, one can dream. I have a lot of grievances with OpenCL, but I find your judgment to be excessively harsh. CUDA has a lot of appeal thanks to its high-level runtime API, but as soon as your code grows sufficiently complex you soon discover that all of its benefits become hurdles, and you start approaching the driver API, which is not that different from OpenCL, and sometimes worse. The same holds for runtime device code compilation. Moreover, the ease of approach offered by the CUDA runtime API obfuscates a lot of the details of what goes behind the scenes that are actually extremely important to produce code even remotely sensible.
And let's not forget that CUDA sometimes violates the streaming processing paradigm in subtle ways ranging from the need to specify a block size to the silent failures for kernels issued with too large a grid. I guess it is quite harsh, but for the goal I had, speed up an existing CPU implementation or a certain long winded operation until it was "fast enough".
As soon as we got about times performance increase we were done, so the lower level stuff wasn't even necessary. OpenCL most probably due to the lack of nice tooling for my platform, printf is about what was usable took much longer to even get into the x.
How to get “CUDA” on AMD graphic cards?
And the boilerplate for OpenCL, damn it's huge. And the docs, well they could have made them much more clear and easier to navigate. It seems like some of the more open sourcey projects like to make their stuff as arcane as possible, but something can be said for ease of use of documentation.
Just plain specifications without any implementation detail or other help to use some arcane function or another. This was a buisiness strategic decision on their part. Extra irony points for chairman of OpenCL working group being an Nvidia department head.
Also, OpenCL is typically a much faster compiler for the same performance as cuda, but you can do JIT compilation which is a very useful feature. First, the C API is much more fun. Shame it's such a pain to search through it with google, versions are all over the place and you lose all the UI too. I like how your perfectly logical and levelheaded comment was, yet it was down voted so hard.
The community is part of the problem. With such rig we can theoretically provide warmth to scientific bases in Antarctica, future colonies on Mars, and even melt steel beams: Filthy casual!
I have my 6ghz on the non wraith stock cooler. Four gtx s are for peasants. I was freezing in my 90 degree room with the stock blower so I went ahead and used the RX fanless cooler combined with my 4 leaf blowers to dissipate the heat into my room.
In terms of dissipating heat, yeah. Sure, it's loud, but it's dealing with a w GPU vs one almost w less. It has a vapor chamber, it has a MUCH larger fin area, etc. It's a major factor that keeps these companies from switching even if AMD offers a superior product, higher performance GPU at a lower price. Because they can't switch, since their software is built with CUDA. Think of it as GSync vendor lock-in, but much more potent, because the software ecosystem is often priceless to these corporations.
AMD's solution to have a tool that can port CUDA to an open language means they have destroyed a major vendor lock-in scheme and open the doors to big potential gains. If Vega is competitive and price well, we could see major marketshare gains from AMD in the next 2 years in this sector of high-end computing. Just wanted to add, it's even more significant than the above. Intel would be so happy with this development.
That comparision doesn't make much sense though. If this happened, then the monitor manufacturer would have still paid the license fee to Nvidia, and the consumer the premium on the monitor to cover that license fee. It would have been more like a free tool that made your Nvidia card capable of using Freesync monitors so Nvidias artificial chokehold on the consumer by forcing them to buy G-Sync monitors was broken.
Well in my example, the monitor equates to the software ecosystem built on CUDA. NV already got their cut because companies invested into this heavily already. So they are stuck with NV So it's akin to GSync in that way, people invested in that monitor already so NV got their cut.
Both example are ok. The diff.
OpenCL vs. CUDA: Which Has Better Application Support?
Since AMD has made the tool, library and the horse power, depending on the industry to make the jump. Actually it makes sense. You start by buying an nvidia card. You follow by buying a g-sync monitor. Now as a consumer your next purchase would probably be a new GFX card and most certainly an nvidia. AMD is not an option unless they manage to make Gsync compatible cards.