Folding@Home Tips and Tricks

Introduction

Since I started the SQLFamily Folding@Home Team on March 8, 2020, I have gotten quite a few emails and Twitter DMs asking me questions. This is great, since it shows there is still curiosity and interest about this distributed computing project. As I have used Folding@Home on a number of machines, I have learned a few tips and tricks that I wanted to pass along.

Why Aren’t You Getting Work Units?

With the current pandemic, and the increased interest in Folding@Home, they have been overwhelmed with increased traffic and work unit requests. Because of this, whatever machines you have running Folding@Home may spend some or even most of their time idle.

Folding@Home Web Client with CPU and GPU work Units

Obviously, this is frustrating. The Folding@Home organization is fully aware of this, and they are working hard to generate more work units and add more infrastructure capacity. In the meantime, there are some things you can do to mitigate this issue:

  • Periodically run the “Stop Folding”, and “Stop Now” commands with the FAH Web client
    • Then run the “Start Folding” command
    • This will reset the delay between work unit requests
  • Periodically restart the machine where Folding@Home is running
    • This is different than explicitly stopping and starting Folding
    • It also seems to help in some cases
  • Change your Windows Power Plan to Balanced
    • This will reduce your power usage when it is idle

What Kind of Hardware Should I Use for Folding?

This depends on what your objectives and budget are. Unless, you are extremely dedicated or independently wealthy, I would not go out and spend a great deal of money on new hardware to run Folding@Home. Many people (especially people who read my blog) may have some existing computer hardware that could be suitable for Folding.

Here are my recommendations, in order of effectiveness.

Dedicated Folding Rig ($$$)

A well ventilated, desktop tower machine, with a modern AMD or Intel 6C/12T processor. A great choice would be an AMD Ryzen 5 3600. You want a machine that is power efficient and will support multiple modern NVidia graphics cards. I would use two 8GB sticks of RAM, one 250GB M.2 NVMe SSD, and an 80 PLUS Platinum or Titanium power supply.

80 Plus Ratings
80 PLUS Ratings

I would use as many NVidia graphics cards as the system will support. The higher end the card the better! For example, an NVidia GeForce RTX 2080 Ti is the current performance king. The more graphics cards you use, the higher your power requirements will be. The model card(s) you are using also has a big effect on power usage. This will determine what size power supply you will need.

The FAHCore software runs much better on modern NVidia GPUs compared to AMD GPUs or any CPU. By default, the FAH client will grab one of your logical CPU cores to manage each GPU that it recognizes. That is why I think a 6C/12T CPU is a good choice.

If you want to really get in the weeds, you can scour motherboard, CPU, video card and fan reviews, to determine the performance/watt of each component. There can actually be pretty big differences in power usage across different brand and model components. You also want your system to run as cool as possible under a heavy load, so that it is not thermally throttled.

Building a System From Existing Parts

You may have old systems or old parts lurking in your basement or garage. Depending on their age and quality, they may or may not be worth using for Folding. For example, you might have an old laptop or desktop that has a quad-core or lower processor, with integrated graphics. A system like this might be perfectly adequate for general purpose light usage, but it will be terrible for folding.

Folding@Home will not use integrated graphics in most cases. A quad-core processor from several years ago is also going to struggle with FAH work units. Older systems will also be much less power efficient than newer systems.

Using an old gaming rig (or cast-off components from a gaming rig) would be a better choice. Your objective is to have the most GPU processing power at the lowest energy usage possible. Newer generation components are simply much more energy efficient. Anything more than 3-4 years old is probably not really worth using from a performance/watt perspective. If you decide to use old existing parts, I urge you to measure the total power usage of the system at idle and under a full load. This way, you will understand what you are doing from an efficiency perspective.

Buying Some New or Used Components

If you have an existing desktop system or some existing older parts, it might make sense to make some intelligent upgrades. Getting a newer generation NVidia graphics card or adding a second one would be a top priority. You may be able to find some bargains in the used market, perhaps from old cyber mining rigs. If you have an old, inefficient power supply, upgrading that to something with an 80 PLUS Gold rating would be helpful. Removing extra parts, like secondary storage devices will reduce power usage.

If you don’t want to spend any money, but you still want to do some Folding for a good cause, then you should carefully evaluate your systems and parts. You want as much GPU performance as you can get with the lowest power usage. With some research and by measuring the power usage from the wall, you can pick the system or components that you want to use.

Other Posts About Folding@Home

I have written quite a few posts about this recently. Probably too many! Here they are:

Final Words

I spent much of last weekend “optimizing” my Folding@Home herd of machines. This included swapping 80 PLUS Gold power supplies for less efficient 80 PLUS Bronze power supplies, and swapping in several NVidia graphics cards that I had laying around.

The Folding@Home Stats Report has been stuck with old data for about three days now, which makes it harder for people to track their progress and stay motivated. You can still see your actual Credits from the Folding@Home Web Client though.

You can also reduce your power usage by disabling CPU work units, and only doing GPU work units. This will reduce your overall output, but will be more efficient.

I would love to hear more details about the system(s) you are using for Folding@Home. I also want to thank everyone who has pitched in on this effort!

Categories Folding@Home

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