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When it comes to GPGPU there is one big problem with the Mac Pro: Lack of CUDA support. CUDA is the de facto standard for GPGPU programming in HPC. Software sup

When it comes to GPGPU there is one big problem with the Mac Pro: Lack of CUDA support. CUDA is the de facto standard for GPGPU programming in HPC. Software support for it is years ahead of OpenCL. I doubt the new Mac is going to change that, if anything it might give some impulse into new workstation design for the likes of Dell and HP, sporting NVIDIA cards. Of course there is still hope that Apple recognizes this and that it offers an NVIDIA configuration. A dual Titan Mac Pro could be a very compelling HPC workstation for those who prefer this to working on a cluster. Even more so since Unix/Linux is the standard OS for HPC and most software tools therefore have OSX/darwin support.

levesque 13y ago
Apart from the initialization code which I'd agree is significantly less painful for CUDA, I haven't seen that much difference in terms of exploiting a GPU's compute units. I never wrote complex applications though, so am I missing something? The bulky initialization is no longer a problem with a library like PyOpenCl - everything becomes much more elegant...
- qb45 13y ago
  First of all, nobody is going to port few KLOC of CUDA to OpenCL or vice versa because of one developer's whim. Hence no choice of GPU is bad for developers. And there are some differences which may be relevant for large projects: CUDA supports ahead-of-time compilation of GPU code down to the lowest level assembly (compilation of several KLOC project takes minutes), direct data transfers between GPUs or even GPUs and network adapters, some subset of C++ (classes without virtual methods, function/class templates, operator overloading) and probably few more useful features I haven't listed.
qb45 13y ago
I think that the "target audience" of this new Mac Pro is snobs, not HPC developers. With almost everything soldered down it's hardly a good machine.

levesque 13y ago

Apart from the initialization code which I'd agree is significantly less painful for CUDA, I haven't seen that much difference in terms of exploiting a GPU's compute units. I never wrote complex applications though, so am I missing something? The bulky initialization is no longer a problem with a library like PyOpenCl - everything becomes much more elegant...

qb45 13y ago
First of all, nobody is going to port few KLOC of CUDA to OpenCL or vice versa because of one developer's whim. Hence no choice of GPU is bad for developers. And there are some differences which may be relevant for large projects: CUDA supports ahead-of-time compilation of GPU code down to the lowest level assembly (compilation of several KLOC project takes minutes), direct data transfers between GPUs or even GPUs and network adapters, some subset of C++ (classes without virtual methods, function/class templates, operator overloading) and probably few more useful features I haven't listed.

qb45 13y ago

I think that the "target audience" of this new Mac Pro is snobs, not HPC developers. With almost everything soldered down it's hardly a good machine.