We got so excited in participating in the Folding@Home project that we built as many high performance systems we could running both the SMP and GPU clients. We were very happy with the results until we received our first electricity bill: our energy consumption more than doubled – and we haven’t even had our systems running 24/7 for 30 days! Since we still wanted to contribute as much as we can to Folding@Home, we decided to go in a quest to find out if there is a way to score lots of points at Folding@Home and, at the same time, not going bankrupt. We got all video cards we had available here in our lab to see which one provided the best performance/consumption ratio. Check it out.

If you are not familiar with the Folding@Home project, it is a project sponsored by Stanford University for using computers all around the world to make protein folding simulations in order to find cure for certain diseases. You can collaborate with the project by installing a client on your PC and, when your PC is idle, it automatically downloads, calculates and sends the results back to Stanford. Thus they can have the largest supercomputer in the world (made by the collaborative effort from all people participating in the project) without spending a dime.

If you want to collaborate more, you can install and run high-performance clients, like the SMP client (which recognizes more than one CPU or more than one CPU core; the standard client only recognizes one CPU core) and the GPU client (which uses the graphics chip from your video card to do the calculations, a technique called GPGPU, or General Purpose Graphics Processing Unit). These clients will complete the calculations at a faster pace but, on the other hand, will consume more power, increasing your electricity bill. Finding the “perfect” balance between performance and power consumption is the goal of this article.

For every completed work you send back to the university you get a certain number of points. The number of points will depend on the kind of client you have installed (standard, SMP, GPU, etc) and the kind of job you are running. The number of points you receive will be our metric for performance, as most people participating in Folding@Home in teams (like ourselves) are interested in achieving the highest possible score.

Now let’s show you the systems we built to collaborate with the project, their performance (i.e., the number of points they were giving us) and their electrical consumption. With this data you can have an idea of how much we were spending to have them turned on 24/7. We will do a lot of investigation on how to decrease consumption and, at the same time, keeping a high score.

But before we present you numbers, you need to understand more about power consumption. We measured consumption with a digital watt meter, which presents results in watts. With this instrument we were measuring the AC consumption of our system. This is not what the system was pulling from the power supply, because the power supply itself consumes and wastes power. The ratio between the power that the system is pulling from the power supply and the power that the power supply is actually pulling from the wall is called efficiency.

The higher efficiency is the better, as you will be wasting less energy. For example, if a certain system is pulling 200 W from the wall that means that your whole computer is pulling 200 W (and you will pay to the electricity company based on this amount) but the components that are connected to the power supply will be consuming less that than. If we take a typical power supply with 80% efficiency, the components would be pulling 160 W.

Suppose you replace your 80% efficiency power supply with another with 88% efficiency. Your system will still be pulling 160 W from the power supply, but your new unit will be pulling less from the wall: 182 W.

So off the bat one way to save on the electricity bill is replacing your power supply with another with higher efficiency. One way to discover your unit’s efficiency is reading the efficiency chart provided by the manufacturer. Another way is reading our reviews, where we measure this.

Watts (W) is the amount of power the equipment is consuming, but the electricity company charges you based on how much energy you are consuming, which is measured in kWh (kilowatt-hour). Energy is the amount of power you consume over time. So one kWh represents 1 kW (1,000 watts) consumed over one hour. Since we are going to assume that we will be running each machine 24/7, we will multiply the amount of watts by 0.72 (24 hours x 30 days / 1,000; the division by 1,000 is necessary to convert Wh into kWh) to have an estimate of the monthly consumption in kWh. Then we can simply multiply this number by the cost of each kWh to have an idea of the monthly cost to run each system 24/7. Of course the cost of electricity varies depending where you live; we are using the value of USD 0.1224800 per kWh, which is the electricity cost in our town on the day we published this article. On top of that we had other charges like franchise fee, green power financing, etc that we are not considering for simplicity.

As for performance, we measured how much each video card or CPU delayed to process 1% of the work load. By multiplying this time to 100, we had how much time each device would take to process the entire work unit. By dividing 86,400 (number of seconds in a day) from this number, we had the maximum number of work units the device can process per day. As we know how many points each work unit is worth, we can find out the maximum score we can expect from the device by multiplying the maximum number of work units the device can process per day by the number of points each work unit will give us. The result is the maximum score this device can give you per day, and this is the number we will be using.


Gabriel Torres is a Brazilian best-selling ICT expert, with 24 books published. He started his online career in 1996, when he launched Clube do Hardware, which is one of the oldest and largest websites about technology in Brazil. He created Hardware Secrets in 1999 to expand his knowledge outside his home country.