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Musings on Machine Learning…

Benchmarking with Folding@Home

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Benchmarking with Folding@Home

Introduction

There are a lot of different benchmarks to rate the performance of computers, but I’m finding it interesting watching the performance of the various computers around the house running Folding@Home which I blogged about last time. Folding@Home provides all sorts of interesting statistics that I’ll talk about as well. The main upshot is how much processing power a GPU has compared to a CPU.

My Benchmarks

Here are some of the specs and the Folding@Home statistics for a number of the computers lying around my house:

Brand CPU # CPUs Memory Points per Day
MSI Intel i7 9750H

10

16Gig

42,337

MSI nVidia GTX 1650

896

4Gig

262,389

HP Intel i3 6300U 

4

4Gig

16,178

Apple Core 2 Duo

2

4Gig

2,073

Dell Celeron N2840

2

4Gig

565

 

These are all laptop computers. The Dell is a Chromebook that is running GalliumOS. Here are a few takeaways from these benchmarks:

  • Intel Celerons that are used in many cheap Chromebooks are terribly underpowered.
  • The Core 2 Duo does not contain the newer AVX SIMD instructions added with the Intel Core line of processors and that is why the 2008 era MacBooks do so poorly.
  • GPUs are far more powerful than CPUs for this sort of calculation.
  • On the MSI Gaming Laptop there are 12 logical CPUs, but one is controlling the GPU and one is being used by myself to write this article.

Folding@Home Operating System Statistics

Here are the operating system statistics as of May 1, 2020 taken from Folding@Home statistics page.

OS AMD GPUs NVidia GPUs CPUs CPU cores TFLOPS x86 TFLOPS
Windows

113,145

399,642 1,018,463 6,415,885 911,141 1,832,063
Linux

12,045

127,665 2,001,996 14,315,265 425,332

681,347

macOSX

136

0 96,503 477,058 5,537

5,753

Totals

125,326

527,307

3,116,962 21,208,208 1,342,010

2,519,163

 

Here are some takeaways from these numbers:

  • There are twice as many Linux CPUs at Windows CPUs. I think this is partly because technically advanced users that are more likely to use Folding@Home prefer Linux.
  • NVidia GPUs outnumber AMD GPUs by 3 to 1.
  • Most people with GPUs run Windows. Shows the draw of most gaming being on Windows.
  • It’s sad that Apple doesn’t offer GPUs on very models.

No ARM Support

It’s sad that Folding@Home doesn’t support the ARM processor. I’d love to add my various Raspberry Pis and nVidia Jetsons to this table. I think this would also greatly increase the number of Linux CPUs shown. Newer ARM CPUs all have NEON SIMD coprocessors and there could be support for common ARM GPUs.

Summary

It would be nice if computer reviews start adding Folding@Home benchmarks to all the other benchmarks they publish. I find it interesting comparing how various CPUs and GPUs do when running this algorithm.

Written by smist08

May 1, 2020 at 11:30 am

Posted in Business

Tagged with , ,

2 Responses

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  1. Rosetta@home and Folding@home on ARM
    https://www.neocortix.com/coronablog

    Marcelo

    May 1, 2020 at 7:42 pm


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