61–70 of 79 posts
I don't understand why people keep writing new code in legacy hardware that most people don't own. Its written in CUDA, not OpenCL. Stop that.
Earlier quoted context omitted.
So why would you go with OpenCL? Is it portability? For a while I thought that was worth it but I am having a hard time remaining convinced of that.
OpenCL is a standards compliant compute API that is supported by Nvidia, AMD, Intel, IBM, Sony, Apple, and several other companies. Nvidia is in the slow process of eventually discontinuing further CUDA support, and it is recommended to write new code in OpenCL only.
[Citation needed]
Their OpenCL support is still limited to v1.1 (released in 2010), while just few months ago they've released a new major version of CUDA with tons of features nowhere to be seen in (any vendor's) OpenCL.
Earlier quoted context omitted.
So why would you go with OpenCL? Is it portability? For a while I thought that was worth it but I am having a hard time remaining convinced of that.
OpenCL is a standards compliant compute API that is supported by Nvidia, AMD, Intel, IBM, Sony, Apple, and several other companies. Nvidia is in the slow process of eventually discontinuing further CUDA support, and it is recommended to write new code in OpenCL only.
NVidia's CUDA only (for now?) Anyone can explain why opensource projects embrace CUDA over OpenCL? As I understand OpenCL is more generic API which could be potentially used with CPUs and GPUs.
You're preaching to the choir. Looks like NEC funded this. NVidia seems to be the preferred hardware for institutions/big companies. I'm not sure if this is because NVidia's architecture is better for supercomputers or if they're simply better at marketing to those types of customers NVidia funds a lot of academics in my space, and I've found academia to be very anti-open source for those reasons, which amuses me gre…
In the world of electrical engineering Matlab can do things that other packages can't.
From personal experience, I have spent many hours looking at the results of a atan2 function in C++ and Matlab and trying to get them to agree. After a day of work I was able to get them to agree by precisely controlling the rounding modes and using my own atan2 function. This was not fun, and I would rather give somebody 1k to take care of it for me.
Earlier quoted context omitted.
> "Because it's hard" is never a good excuse to not do something. Well that's certainly not true in the general case.
On the contrary, I'd argue that it's not true in specific cases . "Because it's hard" is a cop-out. "It's too hard to accomplish given constraint [X]" where X is a deadline, financial constraints, or other real/tangible resource limitations might be one thing. But if you're working on your own timeline on some sort of open-source project, or there is nothing external preventing you from acquiring the expertise/resour…
Earlier quoted context omitted.
So why would you go with OpenCL? Is it portability? For a while I thought that was worth it but I am having a hard time remaining convinced of that.
You go with OpenCL so that you can use AMD's Fusion processor that will start will soon allow a GPU and CPU to share main memory.
Earlier quoted context omitted.
NVidia cards do not efficiently implement bit rotation, while AMD cards do, and it happens to be the core part of the SHA algorithm used for bitcoin. In general for an arbitrary task they're fairly close in performance.
AMD cards dominate Nvidia in pretty much all password hash bruteforcing algos, even those that do not rely on bit rotation (bit_align). See http://golubev.com/gpuest.htm for example. It is true that another instruction helps in more cases (bfi_int which I talked about at http://blog.zorinaq.com/?e=43 ) but in general, AMD cards have a lot more raw integer and floating point compute resources (execution units) than Nv…
Earlier quoted context omitted.
Nvidia software has always been much better than AMD. AMD on Linux is a complet disaster. So if you work on GPU it's natural to go Nvidia.
>AMD on Linux is a complet disaster. So is nvidia. Both companies produce absolutely horrible drivers. Not just for linux either, tons of bluescreens, crashes and other windows instability issues are video driver bugs. That is what happens when the sole concern is speed, and stability is totally ignored.
NVidia's CUDA only (for now?) Anyone can explain why opensource projects embrace CUDA over OpenCL? As I understand OpenCL is more generic API which could be potentially used with CPUs and GPUs.
Nvidia uses ECC RAM on their GPU compute cards, an important consideration for serious HPC computing.
PgOpenCL takes a different approach- instead of a Foreign Data Wrapper (FDW) that exposes tables, it's a language for writing postgres functions in, where the language is just opencl. http://www.slideshare.net/3dmashup/pgopencl Alas, Tim's been talking about this for two years now, and as far as we know he's the only one whose ever seen the code.