Welcome to MLC@Home, a new project dedicated to understanding and explaining machine learning models. It's based at the CORAL lab at the University of Maryland, Baltimore County (UMBC). It supports Linux/amd64 and Windows/x64.

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System requirements (64 bit CPU only):
Linux
Windows

Future support:
OSX
Linux / arm
Linux / ppc64le

Not supported:
GPU

Too bad, this kind of project could gain so much traction if GPUs were used...

Too bad, this kind of project could gain so much traction if GPUs were used...

I was wondering about that. You could probably raise that point more effectively than I could on their forum.
Maybe some volunteers could help? It is a nice project.

System requirements (64 bit CPU only):
Not supported:
GPU

Too bad, this kind of project could gain so much traction if GPUs were used...

If it is similar to CPDN where each calculation depends on the previous one then it is possible GPU won't give much of a boost if any. Of course it may well be able to make use of parallel processing. I am just putting forward a possible reason.

Too bad, this kind of project could gain so much traction if GPUs were used...

We are at the beginning of the project...

System requirements (64 bit CPU only):
Not supported:
GPU

Too bad, this kind of project could gain so much traction if GPUs were used...

If it is similar to CPDN where each calculation depends on the previous one then it is possible GPU won't give much of a boost if any. Of course it may well be able to make use of parallel processing. I am just putting forward a possible reason.

Only if the project depends on Double Precision.
Then an RTX2080 @ 300W is about 10x faster than a 15W dual core Celeron CPU at the same frequency.
GPUs can use VRAM to store the result, which they re-read, and re-process through the shaders/cores, without needing to pass through the PCIE bus again.

If a project can run on Single Precision (or 32bit FPP), an RTX 2080 Ti is about 1.000-1.500x faster (in terms of Flops) than a dual core celeron running at the same frequency.

Long WUs may require a lot of VRAM, especially if tailored to the GPU they're feeding.
An RTX 2080 Ti has ~4350 cores and 11GB of VRAM, that means each WU gets less than 2,5MB of VRAM, or there must be some parallel optimizations allowing blocks of cores (shaders) to run simultaneously sharing VRAM memory blocks.

If Half precision (16bit) worked, Nvidia and AMD are working on doubling their half precision process rates, by processing 2 of them in their cores. Rather than running 1 instruction per core, they can fit 2 of them through an algorithm.
Not sure if this process will be implemented in current line of GPUs.
But to give you an idea, current AI runs at Quarter (8 bit) or half(16bit) precision, and uses between 10 to 300 cores.
Can you imagine the processing power if ran through an RTX 2080 Ti (with over 4000 cores)?
Should boost learning algorithms and AI to near real time.

The source code of the project is open.
So, if someone wants to help the development....