Tag Archives: PPD

Nvidia GeForce GTX 1070 Ti Folding@Home Review

In an effort to make as much use of the colder months in New England as I can, I’m running tons of Stanford University’s Folding@Home on my computer to do charitable science for disease research while heating my house. In the last article, I reviewed a slightly older AMD card, the RX 480, to determine its performance and efficiency running Folding@Home. Today, I’ll be taking a look at one of the favorite cards from Nvidia for both folding and gaming: The 1070 Ti.

The GeForce GTX 1070 Ti was released in November 2017, and sits between the 1070 and 1080 in terms of raw performance. As of February 2019, the 1070 Ti can be for a deep discount on the used market, now that the RTX 20xx series cards have been released. I got my Asus version on eBay for $250.

Based on Nvidia’s 14nm Pascal architecture, the 1070 Ti has 2432 CUDA cores and 8 GB of GDDR5 memory, with a memory bandwidth of 256 GB/s. The base clock rate of the GPU is 1607 MHz, although the cards automatically boost well past the advertised boost clock of 1683 Mhz. Thermal Design Power (TDP) is 180 Watts.

The 3rd party Asus card I got is nothing special. It appears to be a dual-slot reference design, and uses a blower cooler to exhaust hot air out the back of the case. It requires one supplemental 8-pin PCI-E Power connection.


ASUS GeForce GTX 1070 Ti

One thing I will note about this card is it’s length. At 10.5 inches (which is similar to many NVidia high-end cards), it can be a bit problematic to fit in some cases. I have a Raidmax Sagitta mid-tower case from way back in 2006, and it fits, but barely. I had the same problem with the EVGA GeForce 1070 I reviewed earlier.


ASUS GTX 1070 Ti – Installed.

Test Environment

Testing was done in Windows 10 on my AMD FX-based system, which is old but holds pretty well, all things considered. You can read more on that here. The system was built for both performance and efficiency, using AMD’s 8320e processor (a bit less power hungry than the other 8-core FX processors), a Seasonic 650 80+ Gold Power Supply, and 8 GB of low voltage DDR3 memory. The real key here, since I take all my power measurements at the wall with a P3 Kill-A-Watt meter, is that the system is the same for all of my tests.

The Folding@Home Client version is 7.5.1, running a single GPU slot with the following settings:

GPU Slot Options

GPU Slot Options for Maximum PPD

These settings tend to result in a slighter higher points per day (PPD), because they request large, advanced work units from Stanford.

Initial Test Results

Initial testing was done on one of the oldest drivers I could find to support the 1070 Ti (driver version 388.13). The thought here was that older drivers would have less gaming optimizations, which tend to hurt performance for compute jobs (unlike AMD, Nvidia doesn’t include a compute mode in their graphics driver settings).

Unfortunately, the best Nvidia driver for the non-Ti GTX 10xx cards (372.90) doesn’t work with the 1070 Ti, because the Ti version came out a few months later than the original cards. So, I was stuck with version 388.13.

Nvidia 1070 TI Baseline Clocks

Nvidia GTX 1070 Ti Monitoring – Baseline Clocks

I ran F@H for three days using the stock clock rate of 1823 MHz core, with the memory at 3802 MHz. Similar to what I found when testing the 1070, Folding@Home does not trigger the card to go into the high power (max performance) P0 state. Instead, it is stuck in the power-saving P2 state, so the core and memory clocks do not boost.

The PPD average for three days when folding at this rate was 632,380 PPD. Checking the Kill-A-Watt meter over the course of those days showed an approximate average system power consumption of 220 watts. Interestingly, this is less power draw than the GTX 1070 (which used 227 watts, although that was with overclocking + the more efficient 372.90 driver). The PPD average was also less than the GTX 1070, which had done about 640,000 PPD. Initial efficiency, in PPD/Watt, was thus 2875 (compared to the GTX 1070’s 2820 PPD/Watt).

The lower power consumption number and lower PPD performance score were a bit surprising, since the GTX 1070 TI has 512 more CUDA cores than the GTX 1070. However, in my previous review of the 1070, I had done a lot of optimization work, both with overclocking and with driver tuning. So, now it was time to do the same to the 1070 Ti.

Tuning the Card

By running UNIGINE’s Heaven video game benchmark in windowed mode, I was able to watch what the card did in MSI afterburner. The core clock boosted up to 1860 MHz (a modest increase from the 1823 base clock), and the memory went up to 4000 MHz (the default). I tried these overclocking settings and saw only a modest increase in PPD numbers. So, I decided to push it further, despite the Asus card having only a reference-style blower cooler. From my 1070 review, I found I was able to fold nice and stable with a core clock of 2012 MHz and a memory clock of 3802 MHz. So, I set up the GTX 1070 Ti with those same settings. After running it for five days, I pushed the core a little higher to 2050 Mhz. A few days later, I upgraded the driver to the latest (417.71).

Nvidia 1070 TI OC

Nvidia GTX 1070 Ti Monitoring – Overclocked

With these settings, I did have to increase the fan speed to keep the card below 70 degrees Celsius. Since the Asus card uses a blower cooler, it was a bit loud, but nothing too crazy. Open-air coolers with lots of heat pipes and multiple fans would probably let me push the card higher, but from what I’d read, people start running into stability problems at core clocks over 2100 Mhz. Since the goal of Folding@home is to produce reliable science to help Stanford University fight disease, I didn’t want to risk dropping a work unit due to an unstable overclock.

Here’s the production vs. time history from Stanford’s servers, courtesy of https://folding.extremeoverclocking.com/

Nvidia GTX 1070 Ti Time History

Nvidia GTX1070 Ti Folding@Home Production Time History

As you can see below, the overclock helped improve the performance of the GTX 1070 Ti. Using the last five days worth of data points (which has the graphics driver set to 417.71 and the 2050 MHz core overclock), I got an average PPD of 703,371 PPD with a power consumption at the wall of 225 Watts. This gives an overall system efficiency of 3126 PPD/Watt.

Finally, these results are starting to make more sense. Now, this card is outpacing the GTX 1070 in terms of both PPD and energy efficiency. However, the gain in performance isn’t enough to confidently say the card is doing better, since there is typically a +/- 10% PPD difference depending on what work unit the computer receives. This is clear from the amount of variability, or “hash”, in the time history plot.

Interestingly, the GTX 1070 Ti it is still using about the same amount of power as the base model GTX 1070, which has a Thermal Design Power of 150 Watts, compared to the GTX 1070 Ti’s TDP of 180 Watts. So, why isn’t my system consuming 30 watts more at the wall than it did when equipped with the base 1070?

I suspect the issue here is that the drivers available for the 1070 Ti are not as good for folding as the 372.90 driver for the non-Ti 10-series Nvidia cards. As you can see from the MSI Afterburner screen shots above, GPU Usage on the GTX 1070 Ti during folding hovers in the 80-90% range, which is lower than the 85-93% range seen when using the non-Ti GTX 1070. In short, folding on the 1070 Ti seems to be a bit handicapped by the drivers available in Windows.

Comparison to Similar Cards

Here are the Production and Efficiency Plots for comparison to other cards I’ve tested.

GTX 1070 Ti Performance Comparison

GTX 1070 Ti Performance Comparison

GTX 1070 Ti Efficiency Comparison

GTX 1070 Ti Efficiency Comparison


The Nvidia GTX 1070 Ti is a very good graphics card for running Folding@Home. With an average PPD of 703K and a system efficiency of 3126 PPD/Watt, it is the fastest and most efficient graphics card I’ve tested so far. As far as maximizing the amount of science done per electricity consumed, this card continues the trend…higher-end video cards are more efficient, despite the increased power draw.

One side note about the GTX 1070 Ti is that the drivers don’t seem as optimized as they could be. This is a known problem for running Folding@Home in Windows. But, since the proven Nvidia driver 372.90 is not available for the Ti-flavor of the 1070, the hit here is more than normal. On the used market in 2019, you can get a GTX 1070 for $200 on ebay, whereas the GTX 1070 Ti’s go for $250. My opinion is that if you’re going to fold in Windows, a tuned GTX 1070 running the 372.90 driver is the way to go.

Future Work

To fully unlock the capability of the GTX 1070 Ti, I realized I’m going to have to switch operating systems. Stay tuned for a follow-up article in Linux.


Folding on the NVidia GTX 1060


Folding@home is Stanford University’s charitable distributed computing project. It’s charitable because you can donate electricity, as converted into work through your home computer, to fight cancer, Alzheimer, and a host of other diseases.  It’s distributed, because anyone can run it with almost any desktop PC hardware.  But, not all hardware configurations are created equally.  If you’ve been following along, you know the point of this blog is to do the most work for as little power consumption as possible.  After all, electricity isn’t free, and killing the planet to cure cancer isn’t a very good trade-off.

Today we’re testing out Folding@home on EVGA’s single-fan version of the NVIDIA GTX 1060 graphics card.  This is an impressive little card in that it offers a lot of gaming performance in a small package.  This is a very popular graphics card for gamers who don’t want to spend $400+ on GTX 1070s and 1080s.  But, how well does it fold?

Card Specifications

Manufacturer:  EVGA
Model #:  06G-P4-6163
Model Name: EVGA GeForce GTX 1060 SC GAMING (Single Fan)
Max TDP: 120 Watts
Power:  1 x PCI Express 6-pin
GPU: 1280 CUDA Cores @ 1607 MHz (Boost Clock of 1835 MHz)
Memory: 6 GB GDDR5
Bus: PCI-Express X16 3.0
MSRP: $269


EVGA Nvidia GeForce GTX 1060 (photo by EVGA)

Folding@Home Test Setup

For this test I used my normal desktop computer as the benchmark machine.  Testing was done using Stanford’s V7 client on Windows 7 64-bit running FAH Core 21 work units.  The video driver version used was 381.65.  All power consumption measurements were taken at the wall and are thus full system power consumption numbers.

If you’re interested in reading about the hardware configuration of my test rig, it is summarized in this post:


Information on my watt meter readings can be found here:

I Got a New Watt Meter!


The Nvidia GTX 1060 delivers the best Folding@Home performance and efficiency of all the hardware I’ve tested so far.  As seen in the screen shot below, the native F@H client has shown up to 330K PPD.  I ran the card for over a week and averaged the results as reported to Stanford to come up with the nominal 305K Points Per Day number.  I’m going to use 305 K PPD in the charts in order to be conservative.  The power draw at the wall was 185 watts, which is very reasonable, especially considering this graphics card is in an 8-core gaming rig with 16 GB of ram.  This results in a F@H efficiency of about 1650 PPD/Watt, which is very good.

Screen Shot from F@H V7 Client showing Estimated Points per Day:

1060 TI Client

Nvidia GTX 1060 Folding @ Home Results: Windows V7 Client

Here are the averaged results based on actual returned work units

(Graph courtesy of http://folding.extremeoverclocking.com/)

1060 GTX PPD History

NVidia 1060 GTX Folding PPD History

Note that in this plot, the reported results previous to the circled region are also from the 1060, but I didn’t have it running all the time.  The 305K PPD average is generated only from the work units returned within the time frame of the red circle (7/12 thru 7/21)

Production and Efficiency Plots

Nvidia 1060 PPD

NVidia GTX 1060 Folding@Home PPD Production Graph

Nvidia 1060 PPD per Watt

Nvidia GTX 1060 Folding@Home Efficiency Graph


For about $250 bucks (or $180 used if you get lucky on eBay), you can do some serious disease research by running Stanford University’s Folding@Home distributed computing project on the Nvidia GTX 1060 graphics card.  This card is a good middle ground in terms of price (it is the entry-level in NVidia’s current generation of GTX series of gaming cards).  Stepping up to a 1070 or 1080 will likely continue the trend of increased energy efficiency and performance, but these cards cost between $400 and $800.  The GTX 1060 reviewed here was still very impressive, and I’ll also point out that it runs my old video games at absolute max settings (Skyrim, Need for Speed Rivals).  Being a relatively small video card, it easily fits in a mid-tower ATX computer case, and only requires one supplemental PCI-Express power connector.  Doing over 300K PPD on only 185 watts, this Folding@home setup is both efficient and fast. For 2017, the NVidia 1060 is an excellent bang-for-the-buck Folding@home Graphics Card.

Request: Anyone want to loan me a 1070 or 1080 to test?  I’ll return it fully functional (I promise!)

Folding@Home on the Nvidia GeForce GTX 1050 TI: Extended Testing

Hi again.  Last week, I looked at the performance and energy efficiency of using an Nvidia GeForce GTX 1050 TI to run Stanford’s charitable distributed computing project Folding@home.  The conclusion of that study was that the GTX 1050 TI offers very good Points Per Day (PPD) and PPD/Watt energy efficiency.  Now, after some more dedicated testing, I have a few more thoughts on this card.

Average Points Per Day

In the last article, I based the production and efficiency numbers on the estimated completion time of one work unit (Core 21), which resulted in a PPD of 192,000 and an efficiency of 1377 PPD/Watt.  To get a better number, I let the card complete four work units and report the results to Stanford’s collection server.  The end result was a real-world performance of 185K PPD and 1322 PPD/Watt (power consumption is unchanged at 140 watts @ the wall).  These are still very good numbers, and I’ve updated the charts accordingly.  It should be noted that this still only represents one day of folding, and I am suspicious that this PPD is still on the high end of what this card should produce as an average.  Thus, after this article is complete, I’ll be running some more work units to try and get a better average.

Folding While Doing Other Things

Unlike the AMD Radeon HD 7970 reviewed here, the Nvidia GTX 1050 TI doesn’t like folding while you do anything else on the machine.  To use the computer, we ended up pausing folding on multiple occasions to watch videos and browse the internet.  This results in a pretty big hit in the amount of disease-fighting science you can do, and it is evident in the PPD results.

Folding on a Reduced Power Setting

Finally, we went back to uninterrupted folding on the card, but at a reduced power setting (90%, set using MSI Afterburner).  This resulted in a 7 watt reduction of power consumption as measured at the wall (133 watts vs. 140 watts).  However, in order to produce this reduction in power, the graphics card’s clock speed is reduced, resulting in more than a performance hit.  The power settings can be seen here:

GTX 1050 Throttled

MSI Afterburner is used to reduce GPU Power Limit

Observing the estimated Folding@home PPD in the Windows V7 client shows what appears to be a massive reduction in PPD compared to previous testing.  However, since production is highly dependent on the individual projects and work units, this reduction in PPD should be taken with a grain of salt.

GTX 1050 V7 Throttled Performance

In order to get some more accurate results at the reduced power limit, we let the machine chug along uninterrupted for a week.  Here is the PPD production graph courtesy of http://folding.extremeoverclocking.com/

GTX 1050 Extended Performance Testing

Nvidia GTX 1050 TI Folding@Home Extended Performance Testing

It appears here that the 90% power setting has caused a significant reduction in PPD. However, this is based on having only one day’s worth of results (4 work units) for the 100% power case, as opposed to 19 work units worth of data for the 90% power case. More testing at 100% power should provide a better comparison.

Updated Charts (pending further baseline testing)

GTX 1050 PPD Underpowered

Nvidia GTX 1050 PPD Chart

GTX 1050 Efficiency Underpowered

Nvidia GTX 1050 TI Efficiency

As expected, you can contribute the most to Stanford’s Folding@home scientific disease research with a dedicated computer.  Pausing F@H to do other tasks, even for short periods, significantly reduces performance and efficiency.  Initial results seem to indicate that reducing the power limit of the graphics card significantly hurts performance and efficiency.  However, there still isn’t enough data to provide a detailed comparison, since the initial PPD numbers I tested on the GTX 1050 were based on the results of only 4 completed work units.  Further testing should help characterize the difference.

Squeezing a few more PPD out of the FX-8320E

In the last post, the 8-core AMD FX-8320E was compared against the AMD Radeon 7970 in terms of both raw Folding@home computational performance and efficiency.  It lost, although it is the best processor I’ve tested so far.  It also turns out it is a very stable processor for overclocking.

Typical CPU overclocking focuses on raw performance only, and involves upping the clock frequency of the chip as well as the supplied voltage.  When tuning for efficiency, doing more work for the same (or less) power is what is desired.  In that frame of mind, I increased the clock rate of my FX-8320e without adjusting the voltage to try and find an improved efficiency point.

Overclocking Results

My FX-8320E proved to be very stable at stock voltage at frequencies up to 3.6 GHz.  By very stable, I mean running Folding@home at max load on all CPUs for over 24 hours with no crashes, while also using the computer for daily tasks.   This is a 400 MHz increase over the stock clock rate of 3.2 GHz.  As expected, F@H production went up a noticeable amount (over 3000 PPD).  Power consumption also increased slightly.  It turns out the efficiency was also slightly higher (190 PPD/watt vs. 185 PPD/watt).  So, overclocking was a success on all fronts.

FX 8320e overclock PPD

FX 8320e overclock efficiency

Folding Stats Table FX-8320e OC


As demonstrated with the AMD FX-8320e, mild overclocking can be a good way to earn more Points Per Day at a similar or greater efficiency than the stock clock rate.  Small tweaks like this to Folding@home systems, if applied everywhere, could result in more disease research being done more efficiently.

F@H Efficiency on Dell Inspiron 1545 Laptop


When browsing internet forums looking for questions that people ask about F@H, I often see people asking if it is worth folding on laptops (note that I am talking about normal, battery-life optimized laptops, not Alienware gaming laptops / desktop replacements).  In general, the consensus from the community is that folding on laptops is a waste of time.  Well, that is true from a raw performance perspective.  Laptops, tablets, and other mobile devices are not the way to rise to the top of the Folding at Home leader boards.  They’re just too slow, due to the reduced clock speeds and voltages employed to maximize battery life.

But wait, didn’t you say that low voltage is good for efficiency?

I did, in the last article.  By undervolting and slightly underclocking the Phenom II X6 in a desktop computer, I was able to get close to 90 PPD/Watt while still doing an impressive twelve thousand PPD.

However, this raised the interesting question of what would happen if someone tried to fold on a computer that was optimized for low voltage, such as a laptop.  Lets find out!

Dell Inspiron 1545


  • Intel T9600 Core 2 Duo
  • 8 GB DDR2 Ram
  • 250 GB spinning disk style HDD (5400 RPM, slow as molasses)
  • Intel Integrated HD Graphics (horrible for gaming, great for not using much extra electricity)
  • LCD Off during test  to reduce power

I did this test on my Dell Inspiron 1545, because it is what I had lying around.  It’s an older laptop that originally shipped with a slow socket P Intel Pentium dual core.  This 2.1 GHz chip was going to be so slow at folding that I decided to splurge and pick up a 2.8 GHz T9600 Core 2 Duo from Ebay for 25 bucks (can you believe this processor used to cost $400)?  This high end laptop processor has the same 35 watt TDP as the Pentium it is replacing, but has 6 times the total cache.  This is a dual core part that is roughly similar in architecture to the Q6600 I tested earlier, so one would expect the PPD and the efficiency to be close to the Q6600 when running on only 2 cores (albeit a bit higher due to the T9600’s higher clock speed).  I didn’t bother doing a test with the old laptop processor, because it would have been pretty bad (same power consumption but much slower).

After upgrading the processor (rather easy on this model of laptop, since there is a rear access panel that lets you get at everything), I ran this test in Windows 7 using the V7 client.  My computer picked up a nice A4 work unit and started munching away.  I made sure to use my passkey to ensure I get the quick return bonus.


The Intel T9600 laptop processor produced slightly more PPD than the similar Q6600 desktop processor when running on 2 cores (2235 PPD vs 1960 PPD). This is a decent production rate for a dual core, but it pales in comparison to the 6000K PPD of the Q6600 running with all 4 cores, or newer processors such as the AMD 1100T (over 12K PPD).

However, from an efficiency standpoint, the T9600 Core2 Duo blows away the desktop Core2 Quad by a lot, as seen in the chart and graph below.

Intel T9600 Folding@Home Efficiency

Intel T9600 Folding@Home Efficiency

Intel T9600 Folding@Home Efficiency vs. Intel Desktop Processors

Intel T9600 Folding@Home Efficiency vs. Desktop Processors


So, the people who say that laptops are slow are correct.  Compared to all the crazy desktop processors out there, a little dual core in a laptop isn’t going to do very many points per day.  Even modern quad cores laptops are fairly tame compared to their desktop brethren.  However, the efficiency numbers tell a different story.

Because everything from the motherboard, video card, audio circuit, hard drive, and processor are optimized for low voltage, the total system power consumption was only 39 watts (with the lid closed).  This meant that the 2235 PPD was enough to earn an efficiency score of 57.29 PPD/Watt.  This number beats all of the efficiency numbers from the most similar desktop processor tested so far (Q6600), even when the Q6600 is using all four cores.

So, laptops can be efficient F@H computers, even though they are not good at raw PPD production.  It should also be noted that during this experiment the little T9600 processor heated up to a whopping 67 degrees C. That’s really warm compared to the 40 degrees Celsius the Q6600 runs at in the desktop.  Over time, that heat load would probably break my poor laptop and give me an excuse to get that Alienware I’ve been wanting.  

Folding at Home CPU Efficiency: Multi-Core Intel Q6600

In the last post, I showed how environmentally unfriendly it is to run just the uniprocessor client.  In this post, I’ll finish off the study about # of CPU cores vs. folding efficiency.  As it turns out, you can virtually double your folding at home efficiency when you double the amount of CPU cores you are running with. Using the same Intel Q6600 as before, I told the Folding at Home client to ramp up and use three cores.  Then, once I had some data, I switched it to four-core folding.  With the CPU fully engaged, my computer became a bit slow to use, but that’s not a problem since what we are all about here is dedicated F@H Rigs (the only way to fold efficiently is to fold 100%).   If I want to use my computer, I’ll stop the folding to do so, then start it up later.

Here are the results of the 1 through 4 core F@H PPD experiment!


As you can see, both performance (PPD) and energy efficiency (technically efficacy in PPD/Watt) scale with the # of CPU cores being used.  Yes, the system does use more total electricity when more cores are engaged (169 watts vs. 142), but the amount of work being done per day has far surpassed the slight increase in power consumption.  In graph form:

Intel Q6600 Folding@Home Points Per Day / Watt Graph

Intel Q6600 Folding at Home Efficiency Graph

Intel Q6600 Folding at Home Efficiency Graph

In conclusion, it makes the most sense from a performance and efficiency standpoint to use as much of your CPU as you can.  In the next post, I’ll look at a few more powerful CPU-based folding@home systems.

PPD/Watt Shootout: Uniprocessor Client is a Bad Idea

My Gaming / Folding computer with Q6600 / GTX 460 Installed

My Gaming / Folding computer with Q6600 / GTX 460 Installed

Since the dawn of Folding@Home, Stanford’s single-threaded CPU client known as “uniprocessor” has been the standard choice for stable folding@home installations.  For people who don’t want to tinker with many settings, and for people who don’t plan on running 24/7, this has been a good choice of clients because it allows a small science contribution to be done without very much hassle.  It’s a fairly invisible program that runs in the background and doesn’t spin up all your computer’s fans and heat up your room.  But, is it really efficient?  

The question, more specifically targeted for folding freaks reading this blog, is this:  Does the uniprocessor client make sense for an efficient 24/7 folding@home rig?  My answer:  a resounding NO!  Kill that process immediately!

A basic Google search on this will show that you can get vastly more points per day running the multicore client (SMP), a dedicated graphics card client (GPU), or both.  Just type “PPD Uniprocessor SMP Folding” into Google and read for about 20 minutes and you’ll get the idea.  I’m too lazy to point to any specific threads (no pun intended), but the various forum discussions reveal that the uniprocessor client is slower than slow.  This should not be surprising.  One CPU core is slower than two, which is slower than three!  Yay, math!

Also, Stanford’s point reward system isn’t linear but exponential.  If you return a work unit twice as fast, you get more than twice as many points as a reward, because prompt results are very valuable in the scientific world.  This bonus is known as the Quick Return Bonus, and it is available to users running with a passkey (a long auto-generated password that proves you are who you say you are to Stanford’s servers).  I won’t regurgitate all that info on passkeys and points here, because if you are reading this site then you most likely know it already.  If not, start by downloading Stanford’s latest all-in-one client known as Client V7.  Make sure you set yourself up with a username as well as a passkey, in case you didn’t have one.  Once you return 10 successful work units using your passkey, you can get the extra QRB points.  For the record, this is the setup I am using for this blog at the moment: V7 Client Version 7.3.6, running with passkey.

Unlike the older 6.x client interfaces, the new V7 client lets you pick the specific work package type you want to do within one program.  “Uniprocessor” is no longer a separate installation, but is selectable by adding a CPU slot within the V7 client and telling it how many threads to run.  V7 then downloads the correct work unit to munch on.

I thought I was talking efficiency!  Well, to that end, what we want to do is maximize the F@H output relative to the input.  We want to make as many Points per Day while drawing the fewest watts from the wall as possible.  It should be clear by now where this is going (I hope).  Because Stanford’s points system heavily favors the fast return of work units, it is often the case that the PPD/Watt increases as more and more CPU cores or GPU shaders are engaged, even though the resulting power draw of the computer increases.

Limiting ourselves to CPU-only folding for the moment, let’s have a look at what one of my Folding@Home rigs can do.  It’s Specs Time (Yay SPECS!). Here are the specs of my beloved gaming computer, known as Sagitta (outdated picture was up at the top).

  • Intel Q6600 Quad Core CPU @ 2.4 GHz
  • Gigabyte AMD Radeon HD 7870 Gigahertz Edition
  • 8 GB Kingston DDR2-800 Ram
  • Gigabyte 965-P S3 motherboard
  • Seasonic X-650 80+ Gold PSU
  • 2 x 500 GB Western Digital HDDs RAID-1
  • 2 x 120 MM Intake Fans
  • 1 x 120 MM Exhaust Fan
  • 1 x 80 MM Exhaust Fan
  • Arctic Cooling Freezer 7 CPU Cooler
  • Generic PCI Slot centrifugal exhaust fan
Ancient Pic of Sagitta (2006 Vintage).  I really need to take a new pic of the current configuration.

Ancient Pic of Sagitta (2006 Vintage). I really need to take a new pic of the current configuration.

You’ll probably say right away that this system, except for the graphics card, is pretty out of date for 2014, but for relative A to B comparisons within the V7 client this doesn’t matter.  For new I7 CPUs, the relative performance and efficiency differences seen by increasing the number of CPU cores for Folding reveals the same trend as will be shown here.  I’ll start by just looking at the 1-core option (uniprocessor) vs a dual-core F@H solve.

Uniprocessor Is Slow

As you can see, switching to a 2-CPU solve within the V7 client yields almost twice as many PPD (12.11 vs 6.82).  And, this isn’t even a fair comparison, because the dual-core work unit I received was one of the older A3 cores, which tend to produce less PPD than the A4 work units.

In conclusion, if everyone who is out there running the uniprocessor client switched to a dual-core client, FOLDING AT HOME WOULD BECOME TWICE AS EFFICIENT!  I can’t scream this loud enough.  Part of the reason for this is because it doesn’t take many more watts to feed another core in a computer that is already fired up and folding.  In the above example, we really started getting twice the amount of work done for only 13 more watts of power consumed.  THIS IS AWESOME, and it is just the beginning.  In the next article, I’ll look at the efficiency of 3 and 4 CPU Folding on the Q6600, as well as 6-CPU folding on my other computer, which is powered by a newer processor (AMD Phenom II X6 1100T). I’ll then move on to dual-CPU systems (non BIGADV at this point for those of you who know what this means, but we will get there too), and to graphics cards.  If you think 12 PPD/Watt is good, just wait until you read the next article!

Until next time…