We measured consumption under three scenarios. First with the system running only the SMP client, then only with the GPU client, and then with both clients at the same time. We wanted to see if it would make sense to run both SMP and GPU clients at the same time.

* USD 0.1224800 per kWh running 24/7.

Here we found out something very curious. Each GPU client puts at least one of the CPU cores working at 100%. On dual-core CPUs, one of the cores will always be working at 100% load (independently of the number of GPUs you have running Folding@Home). On quad-core CPUs, you will have one CPU core per GPU running at 100%. For example, with our Core 2 Duo when we started the GPU client the CPU load went straight to 50%, with one core being idle and the second core being 100% used. Because of this funny behavior, when we put the SMP and GPU clients to run at the same time consumption (and performance) lowered, as both clients were disputing the CPU. On our quad-core system with three video cards, CPU utilization was at 75%, with one CPU core being used per GPU running Folding@Home.

Notice that except on systems one and six, all other systems had a video card installed and the consumption presented when running the SMP client alone includes the consumption of the installed video cards in idle mode.

See how our Phenom 9600 running the SMP client was consuming more than a GeForce 9800 GT running the GPU client.

Since we were running all systems with the GPU and SMP clients at the same time (except on machine number 4), the estimated monthly cost for running these six machines was of USD 138. Ouch. The estimated daily score was of 30,715 points, with an estimated monthly score of 1,063,590.

On system 1 we were running Debian Linux 4.0 (64-bit), while on the other systems we were running Windows XP SP3 with Catalyst 8.10 drivers for ATI and 177.84 drivers for NVIDIA.