Matrix Computations on the GPU in Clojure

http://neanderthal.uncomplicate.org/articles/tutorial_opencl.html

by dragandj • 11 years ago

116 17 11 years ago

neanderthal.uncomplicate.org

savodj• 11 years ago

Finally! This looks awesome. But then, now i have to find an AMD graphics card. Any chance that this works on NVIDIA?

dragandj5• 11 years ago

In principle, it works on Nvidia, but Nvidia still does not support 1.2... But, new pluggable engine for OpenCL 1.2 is in plan, and it will have optimizations for Nvidia, so, it is not something that is out of reach.

silja1• 11 years ago

So the library does not depend on OpenCL version or does?

e12e• 11 years ago

How about Intel/AMD on-die GPUs? (And any thoughts on if that's likely to help -- now, or with future generations -- as opposed to just sticking to the CPU side?)

hellofunk• 11 years ago

This is really exciting, thanks for sharing. OpenCL is probably the future of everything. AMD is going to be an important part of it for sure. Fantastic to see this explained in the Clojure context; it feels right at home!

dragandj5• 11 years ago

If you are interested in programming your own GPU algorithms in clojure and opencl, also look at http://clojurecl.uncomplicate.org

rfeague• 11 years ago

What are a few good practical examples of the kinds of problems I'd solve with this?

dragandj5• 11 years ago

Whenever you need to implement an algorithm that is described using matrices it is much easier and faster to implement it by calling linear algebra functions instead of coding loops yourself. Neanderthal comes into picture when you have lots of numbers and still need it to be fast (nano, micro, milli)seconds instead of minutes and hours (or perhaps days). So, actually any kind of numerical software written in Clojure (and Java) can hugely benefit from this.

tomjen3• 11 years ago

I wrote my master thesis on General purpose GPU programming. The only two reasons you want to code for the GPU is that it can be much faster for some workloads (basically identical computation which can be trivially parralized) and (in some cases) because it can use much less power.

In the particular case of matrix multiplications, lots of problems can be described as matrices (say googles website ranking) and doing calculations on huge matrices is a thing the GPU is great at. That said it is unlikely to be something most people want.

dzhiurgis• 11 years ago

So what is the most practical language currently for GPU processing?

hendzen• 11 years ago

C++.

dragandj5• 11 years ago

Or, hopefully, Clojure ;)

sabalaba• 11 years ago

Python with Theano is surprisingly expressive and performant.

14113• 11 years ago

It entirely depends on how you want to program your GPU. If you want fine grained control, you should really use raw OpenCL/CUDA. If however you need something higher level, it may be better to choose one of a number of DSLs that are available. E.g. Haskell's accellerate (for array programming), a DSL from the Delite/Forge project.

For general purpose high level programming however, there isn't really anything acceptable. Skeletal/pattern based programming is covered fairly well with frameworks such as SkelCL or SkePU, however (from what I've seen) there doesn't seem to be a nice functional, or even procedural general purpose language for GPU programming.

Mikera• 11 years ago

This is great stuff, but it should really be designed as a core.matrix implementation rather than providing it's own API.

The last thing the Clojure community needs is fragmentation with more incompatible libraries.

Matrix Computations on the GPU in Clojure

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