Tag Archives: PPD/Watt

Ultra-Low Power Consumption Computer Tested – 25 Watt AMD Athlon 5350 Quad-Core APU!

When it comes to the web server and file hosting world, where computers run 24/7, power consumption is often the leading concern when selecting hardware. The same is often true for low-load applications, such as HTPCs, where power and heat are at odds with a silent, inexpensive machine. For these machines, which might see an occasional spike in load but typically sit in a near-idle state, a low idle power consumption is key.

The place where lower power components are not as valuable is the high performance computing world. Here, the goal shouldn’t be isn’t the absolute lowest power consumed, but the lowest power required to do a unit of work.

Flipping this around, the goal is to maximize the amount of computational work done per unit of power. This is computational efficiency.

Computational Efficiency on Super Low-Power Computers

Most of the reviews on this blog have been on rather expensive, high-powered hardware. By this I mean big honking graphics cards running on 8-core machines with 16 GB of ram. I’ve even tested dual-CPU servers with 64 GB of ram, like the dual AMD Opteron workstation below:

Dual Opteron RIG

Dual Opteron 4184 12-Core Server – 64 GB Ram

In this article, I’m going in the other direction. I’ll be testing a little teeny-weenie computer, to see just how well an ultra-low power consumption computer does in terms of computational efficiency.

The Machine

Four years ago, AMD did something that some people thought was silly. They released a socketed version of one of their ultra low-power processors. This meant that instead of being constrained with a tiny integrated device like chromebook, people could actually build an upgradable desktop with a drop-in CPU. Well, APU, actually, since AMD included the graphics on the chip.

That processor was the Kabini architecture APU. Built on a 28 nm process, it went along with a new socket (AM1).  There is a really good overview of this here:

https://www.anandtech.com/show/7933/the-desktop-kabini-review-part-1-athlon-5350-am1

I won’t go into too much detail, other than to point out that the flagship chip, the Athlon 5370, was a quad-core, 2.2 Ghz APU with 128 Radeon graphics cores, and an amazing Thermal Design Power of just 25 watts! In a time when the most energy efficient dual and quad-core processors were hovering around 45-65 Watt TDP, this chip was surprising. And, it eliminated the need for a discrete graphics card. And all for $60 bucks!

So, I got my hands on one (not the 5370, but the slightly slower 2.05 Ghz 5350). The prices are a bit inflated now (some nutters want up to 300 dollars for these little guys on eBay, although if you are lucky you can get a deal). For example, this isn’t the one I bought, but it’s a pretty nice combo (board, ram, and CPU) for $72 dollars.

AM1 Build Deal

AMD 25 Watt Quad Core Deal!

Since the goal was to make a machine with the absolute lowest system power consumption, I got a Gigabyte GA-AM1M-S2H microATX board and two sticks of DDR3L (1.35 volt) energy efficient memory. The hard drive is an old, slow, single-platter (I think) Hitachi 80 GB unit, which seems to offer passable performance without the same power consumption as larger multi-platter drives. I used a Seasonic Focus 80+ Platinum 550 watt power supply, which is one of the most energy efficient PSUs available (I went with this vs. a Pico PSU because I wanted the ability to add a big graphics card later). I put 4 80mm case fans on a controller so I can take them right out of the equation.

Here’s pictures of the build. All the stickers make it faster…and external case fans are the bomb (put them on there for my kids to play with).

Defiant_Build

Low Power Consumption Build. Codename: Defiant

After a bit of fussing around, I was able to get the machine up and running with Linux Mint 19.1. Using my P3 Kill A Watt Meter, I measured a system idle power consumption of about 23 watts with the case fans off and 28 watts with the case fans on. That’s less than half of an incandescent light bulb!

Folding@Home Performance

I downloaded the latest V7 Folding@Home client for Linux and enabled 4-core CPU folding (I also set the computer up with a passkey to earn the quick return bonus points). I let it run for a month to make sure everything was stable. Here are the results from the latest week of CPU folding:

AMD APU PPD

AMD Athlon 5350 Folding@Home Production

As you can see, the machine is not fast enough to always return a work unit every day. However, using a 10-day average, the Points Per Day production is 1991.4 PPD. This is in the ballpark of what was reported by the client.

Power consumption when folding was 35 watts (30 with case fans off…with a system this small, the fan power consumption is a significant percentage). I thought it would have been a bit higher, but then again, power supplies are not very efficient at super low loads, and this machine’s mid 20-watt idle consumption is way, way less than what the Seasonic 550-watt PSU is designed for. As the power consumption comes up out of the ultra-low region, the PSU efficiency increases. So, throwing a full 25 Watt TDP of CPU folding at the equation resulted in only a net 10 watt increase in power consumption at the wall.

In short, running full-tilt, this little computer only uses 35 watts of power! That’s incredible! In terms of efficiency, the PPD/Watt is 1991.4/35 = 56.9

The following plots show how this stacks up to other hardware configurations. On the wattage plot, I noted which test machine was used.

 

AMD Athlon 5350 (25 Watt TDP Quad Core APU) Folding@Home Results

AMD Athlon 5350 PPD Comparison

The Athlon 5350 is not very fast…all the other processors do more science per day, and the graphics cards do a lot more!

AMD APU Efficiency Comparison

The Athlon 5350 is also not very efficient. Even though its power consumption is low, it does not produce much science for the power that it draws. It is, interestingly, more efficient than an old Intel Q6600 quad core.

AMD APU Watt Comparison

The Athlon 5350 is an extremely low-power CPU. The desktop build here draws less power than anything I’ve tested, including my laptop!

Conclusion

Super low-power consumption computers, such as one based on the 25-watt quad-core Athlon 5350, are good at (you guessed it) drawing almost no power from the wall. I was able to build a desktop machine that, when running full tilt, uses the same amount of power as three LED light bulbs (or half of one standard incandescent light bulb). It even uses less power than my laptop (and my laptop is tiny!). That’s pretty cool.

Sadly, that’s where the coolness end. If your goal is to do tons of computation, low-power PC parts won’t help (dur!). In the case of supporting disease research for Stanford University’s Folding@Home distributed computing project, the Athlon 5350 test system got spanked by everything else I’ve tested, including my 10-year-old Inspiron 1545 laptop. Worse, despite its ultra low power consumption, the sheer lack of performance kills the efficiency of this machine.

As a side note, I have been overwhelmingly pleased with the computer as a HTPC. It is quiet, uses almost no electricity, and is actually pretty quick at multi-tasking in Linux Mint’s desktop environment, thanks to the 4 CPU cores. This build also offers me the chance to test something else…namely pushing the efficiency of graphics card folding. By reducing the background system power consumption to an incredibly low level, the whole-system efficiency of a folding computer can be increased. All I have to do next is give this little computer some teeth…in the form of a big graphics card! So, it sounds like I’ll have to do another article….stay tuned!

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Folding@Home Efficiency vs. GPU Power Limit

Folding@Home: The Need for Efficiency

Distributed computing projects like Stanford University’s Folding@Home sometimes get a bad rap on account of all the power that is consumed in the name of science.  Critics argue that any potential gains that are made in the area of disease research are offset by the environmental damage caused by thousands of computers sucking down electricity.

This blog hopes to find a balance by optimizing the way the computational research is done. In this article, I’m going to show how a simple setting in the graphics card driver can improve Folding@Home’s Energy Efficiency.

This blog uses an Nvidia graphics card, but the general idea should also work with AMD cards. The specific card here is an EVGA GeForce GTX 1060 (6 GB).  Green F@H Review here: Folding on the NVidia GTX 1060

If you are folding on a CPU, similar efficiency improvements can be achieved by optimizing the clock frequencies and voltages in the BIOS.  For an example on how to do this, see these posts:

F@H Efficiency: AMD Phenom X6 1100T

F@H Efficiency: Overclock or Undervolt?

(at this point in time I really just recommend folding on a GPU for optimum production and efficiency)

GPU Power Limit Overview

The GPU Power limit slider is a quick way to control how much power the graphics card is allowed to draw. Typically, graphics cards are optimized for speed, with efficiency a second goal (if at all). When a graphics card is pushed harder, it will draw more power (until it runs into the power limit). Today’s graphics cards will also boost their clock rate when loaded, and reduce it when the load goes away. Sometimes, a few extra MHz can be achieved for minimal extra power, but go too far and the amount of power needed to drive the card will grow exponentially. Sure the card is doing a bit more work (or playing a game a bit faster), but the heaps of extra power needed to do this are making it very inefficient.

What I’m going to quickly show is that going the other way (reducing power) can actually improve efficiency, albeit at a reduction of raw output. For  this quick test, I’m just going to look a the default power limit, 100%, vs 50%. Specific tuning is going to be dependent on your actual graphics card. But, with a few days at different settings, you should be able to find a happy balance between performance and efficiency.

For these plots, I used my watt meter to obtain actual power consumption at the wall. You can read about my watt meters here.

Changing the Power Limit

A tool such as MSI Afterburner can be used to view the graphics card’s settings, including the power limit. In the below screenshot, I reduced the card’s power limit by 50% midway through taking data. You can clearly see the power consumption and GPU temperature drop. This suggests the entire computer should be drawing less power from the wall. I confirmed this with my watt meter.

Adjust Power Limit MSI Afterburner

MSI Afterburner is used to reduce the graphics card’s power limit.

Effect on Results

I ran the card for multiple days at each power setting and used Stanford’s actual stats to generate an averaged number for PPD. Reporting an average number like this lends more confidence that the results are real, since PPD as reported in the client varies a lot with time, and PPD can bounce around by +/- 10 percent with different projects.

Below is the production time history plot, courtesy of https://folding.extremeoverclocking.com/. I marked on the plot the actual power consumption numbers I was seeing from my computer at the wall. As you can see, reducing the power limit on the 1060 from 100% to 50% saved about 40 watts of power at the wall.

GTX 1060 F@H Reduced Power Limit Production

GTX 1060 Folding@Home Performance at 100% and 50% Power

On the efficiency plot, you can see that reducing the power limit on the 1060 actually improved its efficiency slightly. This is a great way to fold more effectively.

Nvidia 1060 PPD per Watt Updated

NVidia GTX 1060 Folding@Home Efficiency Results

There is a downside of course, and that is in raw production. The Points Per Day plot below shows a pretty big reduction in PPD for the reduced power 1060, although it is still beating its little brother, the 1050 TI. One of the reasons PPD falls off so hard is that Stanford provides bonus points that are tied to how fast your computer can return a work unit. These points increase exponentially the faster your computer can do work. So, by slowing the card down, we not only lose on base points, but we lose on  the quick return bonus as well.

Nvidia 1060 PPD Updated

NVidia GTX 1060 Folding@Home Performance Results

Conclusion

Reducing the power limit on a graphics card can increase its computational energy efficiency in Folding@Home, although at the cost of raw PPD. There is probably a sweet spot for efficiency vs. performance at some power setting between 50% and 100%. This will likely be different for each graphics card. The process outlined above can be used for various power limit settings to find the best efficiency point.

 

Folding on the NVidia GTX 1060

Overview

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

06G-P4-6163-KR_XL_4

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:

https://greenfoldingathome.com/2017/04/21/cpu-folding-revisited-amd-fx-8320e-8-core-cpu/

Information on my watt meter readings can be found here:

I Got a New Watt Meter!

FOLDING@HOME TEST RESULTS – 305K PPD AND 1650 PPD/WATT

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

Conclusion

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

Conclusion

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

Laptops!  

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

Specs:

  • 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.

Results:

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

Conclusion

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.  

F@H Efficiency: Overclock or Undervolt?

Efficiency Tweaking

After reading my last post about the AMD Phenom II X6 1100T’s performance and efficiency, you might be wondering if anything can be done to further improve this system’s energy efficiency.  The answer is yes, of course!  The 1100T is the top-end Phenom II processor, and is unlocked to allow tweaking to your heart’s content.  Normal people push these processors higher in frequency, which causes them to need more voltage and use more power.  While that is a valid tactic for gaining more raw points per day, I wondered if the extra points would be offset by a non-proportional increase in power consumption.  How is efficiency related to clock speed and voltage?  My aim here is to show you how you can improve your PPD/Watt by adjusting these settings.  By increasing the efficiency of your processor, you can reduce the guilt you feel about killing the planet with your cancer-fighting computer.  Note that the following method can be applied to any CPU/motherboard combo that allows you to adjust clock frequencies and voltages in the BIOS.  If you built your folding rig from scratch, you are in luck, because most custom PCs allow this sort of BIOS fun.  If you are using your dad’s stock Dell, you’re probably out of luck.

AMD Phenom II X6: Efficiency Improved through Undervolting

The baseline stats for the X6 Phenom 1100T are 3.3 GHz core speed with 2000 MHz HyperTransport and Northbridge clocks. This is achieved with the CPU operating at 1.375v, with a rated TDP (max power consumption) of 125 watts. Running the V7 Client in SMP-6 with my pass key, I saw roughly 12K ppd on A3 work units.  This is what was documented in my blog post from last time.

Now for the fun part.  Since this is a Black Edition processor from AMD, the voltages, base frequencies, and multipliers are all adjustable in the system BIOS (assuming your motherboard isn’t a piece of junk).  So, off I went to tweak the numbers.  I let the system “soak” at each setting in order to establish a consistent PPD baseline.  I got my PPD numbers by verifying what the client was reporting with the online statistics reporting.  Wattage numbers come from my trusty P3 Kill-A-Watt meter.

First, I tried overclocking the processor.  I upped the voltage as necessary to keep it stable (stable = folding overnight with no errors in F@H or my standard benchmark tests).  It was soon clear that from an efficiency standpoint, overclocking wasn’t really the way to go.  So, then I went the other way, and took a bit of clock speed and voltage out.

F@H Efficiency Curve: AMD Phenom II X6 1100T

F@H Efficiency Curve: AMD Phenom II X6 1100T

These results are very interesting.  Overclocking does indeed produce more points per day, but to go to higher frequencies required so much voltage that the power consumption went up even more, resulting in reduced efficiency.  However, a slight sacrifice of raw PPD performance allowed the 1100T to be stable at 1.225 volts, which caused a marked improvement in efficiency.  With a little more experimenting on the underclocking / undervolting side of things, I bet I could have got this CPU to almost 100 PPD / Watt!

Conclusion

PPD/Watt efficiency went up by about 30% for the Phenom II X6 1100T, just by tweaking some settings in the BIOS.  Optimizing core speed and voltage for efficiency should work for any CPU (or even graphics card, if your card has adjustable voltage).  If you care about the planet, try undervolting / underclocking your hardware slightly.  It will run cooler, quieter, and will likely last longer, in addition to doing more science for a given amount of electricity.

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!

Q6600_Efficiency

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…

-C