Folding@Home scientific computing
Next, we have a new addition to our benchmark suite: a slick little Folding@Home benchmark CD created by notfred, one of the members of Team TR, our excellent Folding team. For the unfamiliar, Folding@Home is a distributed computing project created by folks at Stanford University that investigates how proteins work in the human body, in an attempt to better understand diseases like Parkinson's, Alzheimer's, and cystic fibrosis. It's a great way to use your PC's spare CPU cycles to help advance medical research. I'd encourage you to visit our distributed computing forum and consider joining our team if you haven't already joined one.
The Folding@Home project uses a number of highly optimized routines to process different types of work units from Stanford's research projects. The Gromacs core, for instance, uses SSE on Intel processors, 3DNow! on AMD processors, and Altivec on PowerPCs. Overall, Folding@Home should be a great example of real-world scientific computing.
notfred's Folding Benchmark CD tests the most common work unit types and estimates performance in terms of the points per day that a CPU could earn for a Folding team member. The CD itself is a bootable ISO. The CD boots into Linux, detects the system's processors and Ethernet adapters, picks up an IP address, and downloads the latest versions of the Folding execution cores from Stanford. It then processes a sample work unit of each type.
On a system with two CPU cores, for instance, the CD spins off a Tinker WU on core 1 and an Amber WU on core 2. When either of those WUs are finished, the benchmark moves on to additional WU types, always keeping both cores occupied with some sort of calculation. Should the benchmark run out of new WUs to test, it simply processes another WU in order to prevent any of the cores from going idle as the others finish. Once all four of the WU types have been tested, the benchmark averages the points per day among them. That points-per-day average is then multiplied by the number of cores on the CPU in order to estimate the total number of points per day that CPU might achieve.
This may be a somewhat quirky method of estimating overall performance, but my sense is that it generally ought to work. We've discussed some potential reservations about how it works here, for those who are interested. I have included results for each of the individual WU types below, so you can see how the different CPUs perform on each.
Because this is a new addition to our test suite and it takes a while for the benchmark to run, I was only able to run the benchmark once on each CPU, not three times each per our usual practice. Also, all processors tested on the D975XBX motherboard here used the 1334 BIOS and Corsair RAM.
The case of the Pentium Extreme Edition 965 is intriguing. It's the only CPU here that has Hyper-Threading, so it can run four WUs simultaneously on its two cores. The benchmark loads up all four of this processor's front ends at once, and the Extreme Edition 965 responds by turning in the lowest scores of the lot for each WU type, pretty much like one would expect. However, once we multiply the 965's average points per day by its four front ends, this CPU comes out ahead of the Core 2 Extreme X6800. Is this a fair and accurate reflection of how the Extreme Edition 965 would perform in the real world while running four instances of the Folding@Home client? I'm not entirely sure. There are issues of cache sharing and locality created by running different WU types at once on a multi-core CPU, and those issues are multiplied by doing the same with Hyper-Threading.
At any rate, this is a nice first look at comparative Folding@Home performance, and it confirms that the Core 2 Extreme QX6700 is a total beast for Folding.
