[TriEmbed] Personal supercomputing

Fri Feb 6 18:59:06 CST 2015

I do know that 9 of the top 10 Green supercomputers have GPUs attached (8 of those are Nvidia GPUs and one is AMD).

The one without GPUs is not a CPU only system.  It has PEZY-SC coprocessors.

http://www.green500.org/lists/green201411

The newest Nvidia GPUs (Maxwell architecture) have significantly lower power requirements than previous generations.

With high performance computing, you have to run your workloads to see if GPUs are the way to go.  If you are willing

to play with the code, most applications can be tweaked to run much faster on GPUs than on CPUs.

The effort to program large parallel machines should not be underestimated.  But, it is the way of the future.

If you would like to try out GPU computing, Nvidia has a “free trial” offer at the moment.

http://www.nvidia.com/object/gpu-test-drive.html?2

---> Paul

From: TriEmbed [mailto:triembed-bounces at triembed.org] On Behalf Of Nathan Yinger
Sent: Friday, February 06, 2015 5:02 PM
Cc: TriEmbed
Subject: Re: [TriEmbed] Personal supercomputing

Bitcoin miners have collected a fair number of performance comparisons for SHA1 hashes (https://en.bitcoin.it/wiki/Non-specialized_hardware_comparison#CPUs.2FAPUs). Just from skimming there doesn't seem to be a big difference in energy efficiency, but there is a big difference in capacity.

What sort of time frame are you looking at? Depending on when you get 'sufficient experience', the costs could be very different. Also, the ease of development for FPGAs could change. I'm not knowledgeable, but I saw Adafruit selling an FPGA development board, so it looks like at least some barriers are coming down there.

Incidentally, I experimented with doing bitcoin mining during winters, but it seemed to increase my energy bill over the previous year. I don't know if that was the mining or because of the weather though.

~Nathan

On Thu, Feb 5, 2015 at 1:13 PM, Charles West <crwest at ncsu.edu> wrote:

Hello,

I'm looking into machine learning and it seems like some of the methods could potentially just keep getting better the more data/computational power you throw at them.  I'm not really skilled enough to do to much with them yet, but I wanted to go ahead and see what sort of setup it might be good to build once I have sufficient experience in the area.

Wikipedia has a list of the most power/price computers in the world (http://en.wikipedia.org/wiki/Flops).  It is interesting to note that the last two entries are made from commodity PC parts (the latest coming in at $902.57 and delivering 11.5 TFLOPS).  It would seem that one way to go would be just build one of these servers with a top of the line GPU.

The complicating factor is that the GPU is really power hungry and takes something like > .5 kilowatts to keep running.  At normal utility rates, this means that power is going to cost more than the system if it is kept operating continuously for more than a year.

The other possible alternatives are building large clusters of Odroid C1s or Raspberry Pi 2.0s, each of which have quadcore arm processors and only take ~2.5 watts to run (equivalent power at 200 units).  At the same time, you could probably only have about 18 units at equivalent cost not counting energy.

Lastly, you could just build a decked out CPU server.  I don't really know how they clock in in terms of power efficiency.

A few questions:

What would you build if you had something that would happily eat as many parallel flops as you could deliver (with correspondingly increasing performance)?

Does anyone know how GPUs compare to CPUs in terms of power consumption per FLOP?

At what point does the power cost dominate the computer cost (timescale, hours of expected operation, etc)?  

Also, should this be our new standard way to heat the house during the winter?

Thanks,

Charlie West

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