I ran across an article Fold for COVID which got me thinking about the old hardware I have literally just lying around. I thought maybe I could put it to use and so far so good. This is a form of community distributed computing where you allocate some of your computer resources to working on various problems. A few projects are specific to working on COVID-19 related issues, either in part or in whole.
There are dozens of distributed computing projects out there. Many are worthwhile, though some are not. BOINC is one platform used by many projects. When you start the BOINC client you can add one or more projects and those official projects are worthwhile. I’ve experimented with Rosetta@home and World Community Grid. Another project I found was Folding@home which doesn’t use the BOINC platform, but is doing specific COVID related work among other sub-projects.
The BOINC client is the software that actually runs the work units that get allocated to your machine. It has versions for many operations systems. I’ve tried Windows, Linux and Android. There’s a BOINC manager program that shows you what the client is working on and is where you can configure many things such as, number of CPUs to use, how much memory or disk to use, whether to run in background when the computer is not in use, etc. Most of my devices are dedicated to these projects, though I run one in the background of my main desktop.
By running Rosetta@home on your computer when you’re not using it you will speed up and extend our efforts to design new proteins and to predict their 3-dimensional shapes. Proteins are the molecular machines and building blocks of life. You can read more about protein folding and design here.
Rosetta@home has added COVID-19 work units into their project. One issue I noticed is that it requests 1700MB of RAM, so you might not be able to run it on some devices, or might have to increase the default memory limit used by the BOINC client (which is 50%). I’ve tried it on Windows, Android and Linux successfully. But I’ve been re-arranging what my devices run, so it’s only running on Linux at the moment.
World Community Grid is a simple way to support cutting-edge research into important global humanitarian causes. Your computer or mobile device could be powering scientific research on health, poverty and sustainability. Join now.
World Community Grid has a number of active sub-projects and a couple of projects that only put out work intermittently. But they have a COVID-19 related project in Beta testing. You can select which sub-projects you want your devices to run, or let it process from any of them. I find WCG is able to run on older machines and Android devices with limited hardware like 32 bit or 1GB of RAM. The older machines seem able to run most of the WCG work units. Though the African Rain Project requires 1.5GB of RAM and only one of those old machines has that much.
I started with a couple old Windows XP machines and they worked, but wanting to update the OS I switched them over to Lubuntu which is a lite version of Ubuntu. Lubuntu 16.04 also supports the older 32 bit CPUs on these machines.
I’ve processed some of the COVID-19 beta work units, and will probably switch my WCG machines to that sub-project when it goes live.
Update: the Open Pandemics COVID-19 project went live May 14. I've switched my WCG machines to process just that project. So I have one machine left processing Rosetta@home which still has COVID work units, two that run Folding@home and the rest are working on WCG Open Pandemics.
Folding@home is a project focused on disease research. The problems we’re solving require so many computer calculations – and we need your help to find the cures!
I find that Folding@home requires the most computer resources. It’s also one of the few projects that can use your graphics card for processing (if the card is new and powerful). Of the four graphics cards I have, I think only one will work, but I haven’t got around to trying to install the Nvidia driver for Linux for that machine. They recently introduced a sub-project specific to COVID-19 and they support selecting which sub-projects you want to devote your device to.
Update: installing the Nvidia GPU driver wasn't as bad as I thought it would be, and so far it's been stable.
Of all the major distributed computing projects involved in protein research, Folding@home is the only one not using the BOINC platform. Both Rosetta@home and Folding@home study protein misfolding diseases such as Alzheimer’s disease, but Folding@home does so much more exclusively. Folding@home almost exclusively uses all-atom molecular dynamics models to understand how and why proteins fold (or potentially misfold, and subsequently aggregate to cause diseases). In other words, Folding@home’s strength is modeling the process of protein folding, while Rosetta@home’s strength is computing protein design and predicting protein structure and docking.https://en.wikipedia.org/wiki/Rosetta@home
The above quote and other things I’ve read suggest that both projects are worthwhile in different ways. Since I have a variety of hardware, and all these projects appear worthwhile, and have (or will have) some COVID-19 work, I’ll continue to support all three.
In general, you can set up the client program to run in the background of devices that you use everyday. I tried that on my phone and my laptop, but found they either made the device too warm if they ran a lot, or tended to take a long time to process a work unit if I restricted how much CPU they use. In the end, I removed them from those devices. I mostly just use the extra devices I have and run them 100% on these projects. But I did put Folding@home on my main desktop and just pause it when I happen to use it for something else. So far that has worked pretty good.
All three projects generate credits or points for the work units you process, but it’s just for bragging rights. Here are my BOINC credits:
Here are my Folding@home stats:[phoenix_folding_stats type=”donor” class=”table table-striped” show_id=”no” show_donor_teams=”no” id=”Lenciviona” show_logo=”no” show_tagline=”no”]