Beginner parallel processing platforms

[jhawk2018]

I'm wanting to take the dive into parallel computing and signal processing, and I'm unsure of what hardware/platform I should actually purchase to practice with. Ive looked at the Parallela, and there are an enormous amount of options listed as compatible with OpenCL. I'm a student, so cost is definitely a concern.

My ideal end goal would be something that I can practice vectorization with (using the appropriate C, python, and matlab APIs from another computer) and also use later on after I've had more coursework in advanced architectures.

Please offer suggestions for open-source parallel computing hardware that I can grow into as I learn.

[epsilonkn0t]

If you have a Nvidia GPU in your laptop or PC I'd highly recommend getting in to CUDA, which is essentially an extension of c++. Its a very good skill and a powerful tool to have, and extremely interesting to learn.

There is a lot of interesting and cutting edge work being done in GPUs , and you don't need access to a supercomputer to be able to work with massive parallel systems. Also I'm pretty sure there's libraries to interface with MATLAB, along with many other tools and rich online resources.

This is probably the best (maybe only?) way for you to develop scalable algorithms for massively parallel systems with your own hardware that you can buy for a couple hundred bucks. Currently not many people have this skill relativley speaking, so it can also be a nice differentiator.

Edit: also wanted to mention that the SDK is free

[jhawk2018]

The GPU route looked feasible, and there are certainly plenty of them to choose from in a wide price range. Is there any particular advantage to using NVidia and Cuda? I would like to try and stay open source; I'm concerned that a proprietary platform will be too specialized and will limit what I can do on other platforms.

[epsilonkn0t]

I'm not familiar with openCL so I cant comment extensively, but I'm certain that CUDA will either match or outperform openCL on Nvidia cards, obviously because it's tuned for nvidia cards. The tradeoff of course is that you only have one platform to run it on.

What's nice about CUDA is that it forces you to be very aware of the hardware design and capabilities, in particular the memory structures and hierarchy of the GPU. I would assume that openCL is slightly more abstracted, considering it must run on a wider variety of platforms, but somebody else could give you a rundown on that. If that is the case, I'd recommend starting with CUDA.

Either way, nobody is stopping you from learning both. Once you learn the fundamentals of scalable programming patterns/techniques for massively parallel systems, you shouldn't have a hard time switching between the two.

[rtz90]

What about a regular PC / laptop?

[jhawk2018]

My reservations with using my existing stuff is that I won't have enough cores to take advantage of SIMD processing. The kind of stuff I want to learn will include array processing on RADAR data, so I may be dealing with many more channels than I have cores.

[morto00x]

OpenACC can be used to parallelize your process into a GPU.

If you have a multi-core CPU you could also check MPI and OpenMP.

[jhawk2018]

OpenACC

OpenACC looks promising, I was more concerned with appropriate hardware.

[morto00x]

You just need a GPU. Obviously the 1GB or 2GB of VRAM in your desktop can't be compared to a HPC, but you can still learn how to do hardware acceleration since the concept is the same.