We’re living in very strange times—but it’s not all bad news. Thanks to advancements in technology, the average person is better prepared to help solve the world’s biggest problems than ever before.
Enter Folding@home, a distributed computing project for disease research. Even though they’ve been around for twenty years, lately their popularity has soared, and for good reason. Their research touches many different areas; however, their main focus right now, as you may have guessed, is researching COVID-19.
To give you a basic idea of what they do, I’ll simply quote the project’s blog: “[they’re] simulating the dynamics of COVID-19 proteins to hunt for new therapeutic opportunities.”1
Viruses are like tiny machines, but it’s not yet clear exactly how these particular machines work. By running lots (and lots) of simulations, which require lots (and lots) of computing power, we can gain insights into which drug treatments are most effective in order to save lives and curb the spread of the virus, and eventually create a vaccine.
By signing up for the project (which is free), you allow them to utilize some of your computer’s resources to perform biomedical research. To give you an idea of what that work looks like, this video shows a protein simulation in action. And yes, they’re always that wiggly.
In the midst of all the confusion, misinformation, and everyone buying all the toilet paper, it’s been refreshing to see some companies doing what they can to help. For instance, recently our friends over at NVIDIA called on all PC enthusiasts via reddit and Twitter to sign up for Folding@home. They received an overwhelming response, and for a bit, so did their servers. This is exciting news, as NVIDIA GPUs are very good at the sort of computing tasks that several of Folding@home’s coronavirus-related projects rely on.
Speaking of, if you already happen to own a BOXX system, particularly one of our 4-GPU or 8-GPU RAXX models, there’s a chance you could have a substantial impact on that research in-between, say, your daily rendering jobs or deep neural network training. However, if you wanted the ultimate simulation workstation, you’d need our massive 16-GPU system, the RAXX P6G Jupiter, so named because it’s packed with so many video cards that it practically creates its own gravitational field.2
The RAXX P6G Jupiter, seen here with enough GPUs to power a tiny island nation.
The great thing about Folding@home is that you don’t actually need a massive 16-GPU workstation to make a difference. Any computer with an internet connection can help. It’s all about the cumulative effect of individuals, and not the work of any one system. However, the more powerful your computer, the more you can contribute.
In addition to being a worthy cause, Folding@home is very flexible in terms of its usability. For example, you can easily adjust the type and amount of resources it uses while it runs in the background. You can also tell it to only start when the PC is idle, or you can simply pause it as needed. You can also join teams, track your contributions, and even compare your participation to others.
It’s also worth noting that Folding@home isn’t the only game in town. There are many distributed computing projects all around the world that support all kinds of research, such as astrophysics, cryptography, mathematics, robotics, and seismology.3 Lastly, it’s important to remember that, due to the nature of how these projects work, contributing may increase your electricity bill to some degree.
So, do you have an old computer collecting dust that you can put to work? Or do you want to let your current workstation do some sciencing on the side? (Alternatively, you can use an Android phone as well.) If you’re interested, you can sign up here.
BOXX is a leading manufacturer of purpose-built workstations that accelerate productivity for many professional workloads in multiple industries, including manufacturing & product design, media & entertainment, and data science. To learn more, visit our website or consult with a BOXX performance specialist today.