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11 June 2020

Machine Learning on mining hardware - hidden potential

by Mathieu Poliquin

How to leverage mining hardware for Machine Learning?

On second hand market there is lots of cheap GPUs and motherboards intended for mining but they come with some challenges:

Mining Hardware setup

Hardware specs:

Software:

Benchmarks and profiling

CUDA-Z PCIE bandwith test

PCIE slot GPU HtoD bandwidth
1 P106-100 ~3100 MB/s
2 None N/A
3 P106-090 ~190 MB/s
4 P106-100 ~190 MB/s
5 None N/A
6 P106-100 ~190 MB/s

Let’s do a basic OpenAI baselines test on the 4th PCIE slot (P106-100):

python3 -m baselines.run --alg=a2c --env=PongNoFrameskip-v4 --num_timesteps=2e7 --num_env=6

Results: ~ 500 fps

nv profiler

Overcoming the bottlenecks with NVIDIA CuLE

As you can see from the profiling screenshot above the bottlenecks are clearly the Host to Device transfers and CPU. Interestingly these bottlenecks are simular to high end GPUs as discussed in NVIDIA Research’s paper on CuLE

So the question is: can CuLE also be used to leverage mining hardware?

Let’s find out!

using the recommened parameters from CuLE’s github readme

python vtrace_main.py --env-name PongNoFrameskip-v4 --normalize --use-cuda-env --num-ales 1200 --num-steps 20 --num-steps-per-update 1 --num-minibatches 20 --t-max 8000000 --evaluation-interval 200000

cule test 01

Much better than the 500 fps we get without CuLE

If you want these tests in action you can watch in the video below. Also I made one with all 4 GPUs where a total of near 10 000 fps is reached:

Conclusion

Using NVIDIA CuLE it’s certainly possible to leverage cheap mining hardware for Machine Learning. That said only the Atari 2600 emulator has a 100% GPU port so you would be stuck with that for now. Moreover even if other emulators gets ported, the performance might not be as advantageous since the more complex the code (branching) the less GPUs performs well. Even amongst Atari games there are high performance differences, as some are more complex to emulate. For example in CuLE paper they mentioned Riveraid runs at 134K while Boxing runs at 34K on their GPUs.

Obiviously NVIDIA CuLE is using CUDA so this system doesn’t work on AMD card and to my knowledge AMD research doesn’t offer alternative for now.

tags: machine learning - mining - p106-100 - p106-090 - p104-100