Now we want to see what we were getting in terms of score on Folding@Home by running these systems and do some preliminary analysis to see the most efficient configurations we had running. WU stands for work unit. Project is the number of the Folding@Home project each client was running at the time we collected our data, which will tell us how many points they will give us for each delivered work unit (click here to see the full table). We put the number of points given for each completed WU in parenthesis. The maximum daily performance is calculated by dividing 86,400 (number of seconds in a day) by the time to complete one work unit and the result multiplied by the points given to each completed work unit for that project.

Our metric for measuring efficiency will be points/kWh, which is calculated by dividing the maximum monthly performance by the monthly consumption in kWh. This index indicates how many points each system produces with each kWh consumed from the wall. So the higher this number, the better.

You should understand something very important about Folding@Home scoring system. While work units assigned to nVidia-based video cards will almost always give you 480 points, the number of points given by work units processed by ATI-video cards and the Playstation 3 console can change quite often. The above results are based on the project that each client was running at the time we made our tests and do not reflect the best scores ATI and PS3 systems can achieve. Our ATI-based video cards were processing a work unit that gave 388 points, but there are work units that will give 548 points. Our PS3 was processing a work unit that gave 110 points, but there are work units that will give 330 points. The time for completing these units that give more points can be higher, however. Just as an exercise, we compiled the following table for systems four (ATI) and six (PS3) as if they were processing these other kinds of work units that give more points. We are doing this in order to not be accused of being unbiased or someone pointing out this potential flaw in our methodology in the future. For this exercise we will consider that the clients will process each work unit with the same performance, which may not be true in the real work.

As you can see, even simulating the best performance these systems could achieve, both performance and efficiency were at levels below our other systems.

From the above results we learned interesting things about our systems:

• Our Playstation 3 achieved the lowest efficiency index (although in our simulation above it was more efficient that systems running only the SMP client, if it could only process work units that gives 330 points, which isn’t true), meaning that we were spending too much energy to produce too little points compared to our other systems.
• System five was the most expensive to run, but was also the most efficient, meaning that it was the one that could produce the most points per kWh. On this system it was worthwhile to run the SMP client at the same time as our score and points/kWh index increased.
• On systems two and three it wasn’t worthwhile running the SMP client at the same time with the GPU client: the points/kWh index dropped when we did that.
• System four, which had two ATI high-end video cards, achieved a very low points/kWh index. This was the first system we decided to shut down: it was wasting a lot of energy to produce too little results.

Now we were curious to see if we used mid-range or even low-end video cards we would achieve better performance/power ratios. To do that we tested all video cards we had available.