#Cuda emulator mac pro#
#Cuda emulator mac software#
#Cuda emulator mac code#

The PowerPC chip couldnвЂ™t keep up with Intel and was at a dead end. When Apple went from the PowerPC to the Intel chip, the Mac platform was just renewed with OS X. I figured that Apple was more likely making a desktop based iPad that would be less locked down than iPadOS. When I first heard about Apple running Macs on this chip, it didnвЂ™t sound right.

#Cuda emulator mac software#

Imagine the speed when running software compiled for ARM on a chip thatвЂ™s designed to be used on a desktop machine. So, running in emulation on a non-optimized emulator on a chip not designed for a Mac and is a generation behind the current chip is almost as fast as running on actual Intel hardware. Apple will have a special Mac version of the A14 chip just like they have a more powerful A14 version for the iPad over the iPhone.

#Cuda emulator mac pro#

This chip was built specifically for the iPad Pro and not a Mac.

And, of course, Apple will soon introduce a new A14 chip thatвЂ™s probably even 20% to 25% faster.

This chip is last yearвЂ™s model and isnвЂ™t the current A13 chip that is about 20% faster.This is beta software that has not been optimized.

This is Intel emulation on an ARM chip.

IвЂ™ve talked with developers, and they are all saying that running Intel based software on the A12Z chip with Rosetta 2 software runs almost as fast as a new MacBook Air. But of course so are those mixing rap music videos on $40,000 workstations that require $1000 wheel kits. Personally, I really like Linux and spend a lot of time using Linux tools, but I would definitely prefer I could do it all on my Mac rather than having to run several boxes. These people have less and less reason to stay with Macs. My corner of UC Berkeley is becoming quite a Google shop these days so common tools like Word or Keynote have effectively been replaced by Google docs anyway and they run in the same Chrome browser on any platform. IвЂ™d say about half of my colleagues have now migrated to doing all of that on Linux boxes simply because Apple has been being dicky about GPU support. Already before we did lots of computation on clusters, but there was always a fare amount of вЂlocalвЂ™ work, primarily testing and benchmarking. If there are highly efficient CUDA libraries around or if we have ML tools nicely tailored to a small farm of Teslas thatвЂ™s what weвЂ™ll want to use.Д¬ould it be done in OpenCL or on AMD? Probably. So we tend to use whatвЂ™s already available, especially if that stuff is really good. We have scientific problems we are attempting to solve and our interest lies in the results, not the tools to get there. People in the field I work in are for the most part not coders.

#Cuda emulator mac code#

You could attempt to write OpenCL code yourself and if you were really interested you might be able to make it perform almost as good. The remaining problem being CUDA having robust ecosystem of software libraries so you donвЂ™t have to write nearly so much yourself.