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Seeking The Best Performance per Watt for Folding@Home
 Author: Gabriel Torres 64,839 views Type: Reviews Last Updated: October 29, 2008 Page: 4 of 7 Select Page to Load Introduction Our High-Performance Setup Consumption Analysis Performance Analysis Which Video Card is The Best? Fine Tuning our Systems Conclusions
Performance Analysis

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.

 System # Client Project (Points) Time to Complete One WU (seconds) Max. Daily Performance (Points) Max. Monthly Performance (Points) Points/kWh 1 SMP 5101 (2,165) 66,000 2,834 85,020 976 2 SMP 2665 (1,920) 150,000 1,106 33,180 249 2 GPU 5800 (480) 5,800 7,150 214,500 1,307 2 GPU + SMP Above 150,000 (CPU), 8,700 (GPU) 5,873 176,190 1,211 3 SMP 2653 (1760) 84,100 1,808 54,240 384 3 GPU 5014 (480) 10,300 4,026 120,780 981 3 GPU + SMP Above 84,100 (CPU), 15,400 (GPU) 4,501 135,030 880 4 GPU 5651 (388) 14,300 (HD 4850), 14,000 (HD 4870) 2344 + 2394 = 4,738 142,140 459 5 SMP 2665 (1,920) 79,500 2,087 62,610 260 5 GPU 5013 (480) 8,200 (GTX 280), 8,100 (8800 GT), 8,500 (8800 GT) 5,057 + 5,120 + 4,879 = 15,056 451,680 1,422 5 GPU + SMP Above 79,500 (CPU), 8,200 (GTX 280), 8,100 (8800 GT), 8,500 (8800 GT) 17,143 514,290 1,526 6 PS3 5310 (110) 26,100 364 10,920 116

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.

 System # Client Project (Points) Time to Complete One WU (seconds) Max. Daily Performance (Points) Max. Monthly Performance (Points) Points/kWh 4 GPU 4743 (548) 14,300 (HD 4850), 14,000 (HD 4870) 3,311 + 3,382 = 6,693 200,790 648 6 PS3 5305 (330) 26,100 1,092 32,760 347

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.

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