Tag Archives: Folding@home

Power Supply Efficiency: Let’s Save Some Money

Posted on November 14, 2019 | 4 comments

A while ago, I wrote a pair of articles on why it’s important to consider the energy efficiency of your computer’s power supply. Those articles showed how maximizing the efficiency of your Power Supply Unit (PSU) can actually save you money, since less electricity is wasted as heat with efficient power supplies.

Efficient Power Supplies: Part 1

Energy Efficient Power Supplies: Part 2

In this article, I’m putting this into practice, because the PSU in my Ubuntu folding box (Codenamed “Voyager”) is on the fritz.

This PSU is a basic Seasonic S12 III, which is a surprisingly bad power supply for such a good company as Seasonic. For one, it uses a group regulated design, which is inherently less efficient than the more modern DC-DC units. Also, the S12 is prone to coil whine (mine makes tons of noise even when the power supply is off). Finally, in my case, the computer puts a bunch of feedback onto the electrical circuits in my house, causing my LED lights to flicker when I’m running Folding@Home. That’s no good at all! Shame on you, Seasonic, shame!

Don’t believe me on how bad this PSU is? Read reviews here:

https://www.newegg.com/seasonic-s12iii-bronze-series-ssr-500gb3-500w/p/N82E16817151226

Seasonic S12III potentially on the way from hardware

Now, I love Seasonic in general. They are one of the leading PSU manufactures, and I use their high-end units in all of my machines. So, to replace the S12iii, I picked up one of their midrange PSU’s in the Focus line…specifically, the Focus Gold 450. I got a sweet deal on eBay (got a used one for about $40, MSRP new on the SSR-450FM is $80).

SSR-450M Ebay Purchase Price

Here they are side by side. One immediate advantage of the new Focus PSU is that it is semi-modular, which will help me with some cable clutter.

Seasonic PSU Comparison: Focus Gold 450W (left) vs S12iii 500W (right)

Seasonic PSU Comparison: Focus Gold 450W (left) vs S12iii 500W (right)

Inspecting the specification labels also shows a few differences…namely the Focus is a bit less powerful (three less amps on the +12v rail), which isn’t a big deal for Voyager, since it is only running a single GeForce 1070 Ti card (180 Watt TDP) and an AMD A10-7700K (95 Watt TDP). Another point worth noting is the efficiency…whereas the S12iii is certified to the 80+ Bronze standard, the new Focus unit is certified as 80+ Gold.

 

 

 

 

Now this is where things get interesting. Voyager has a theoretical power draw of about 300 Watts max (180 Watts for the video card, 95 for the CPU, and about 25 Watts for the motherboard, ram, and drives combined). This is right around the 60% capacity rating of these power supplies. Here is the efficiency scorecard for the various 80+ certifications:

80+ Table

80+ Efficiency Table

As you can see, there is about a 5% improvement in efficiency going from 80+ bronze to 80+ gold. For a 300 watt machine, that would equate to 15 watts of difference between the Focus and the S12iii PSU’s. By upgrading to the Focus, I should more effectively turn the 120V AC power from my wall into 12V DC to run my computer, resulting in less total power draw from the wall (and less waste heat into my room).

I tested it out, using Stanford’s Folding@Home distributed computing project of course! Might as well cure some cancer, you know!

The Test

To do this test, I first let Voyager pull down a set of work units from Stanford’s server (GPU + CPU folding slots enabled). When the computer was in the middle of number crunching, I took a look at the instantaneous power consumption as measured by my watt meter:

Voyager_Old_PSU_Peak

80+ Bronze PSU: 259.1 Watts @ Full Load

260 Watts is about the max I ever see Voyager draw in practice, since Folding@Home never fully loads the hardware (typically it can hit the GFX card for about 90% capacity). So, this result made perfect sense. Next, I shut the machine down with the work units half-finished and swapped out the 80+ Bronze S12iii for the 80+ Gold Focus unit. I turned the machine back on and let it get right back to doing science.

Here is the updated power consumption number with the more efficient power supply.

Voyager_New_PSU_Peak

80+ Gold PSU Power Consumption @ 100% Load

As you can see, the 80+ Gold Rated power supply shaved 11.8 watts off the top. This is about 4.5% of the old PSU unit’s previous draw, and it is about 4.8% of the new PSU unit’s power draw. So, it is very close to the advertised 5% efficiency improvement one would expect per the 80+ specifications. Conclusion: I’m saving electricity and the planet! Yay! 

As a side note, all the weird coil whine and light flickering issues I was having with the S12iii went away when I switched to Seasonic’s better Focus PSU.

But, Was It Worth It?

Now, as an environmentalist, I would say that this type of power savings is of course worth it, because it’s that much less energy wasted and that much less pollution. But, we are really talking about just a few watts (albeit on a machine that is trying to cure cancer 24/7 for years on end).

To get a better understanding of the financial implications of my $40 upgrade, I did a quick calc in Excel, using Connecticut’s average price of electricity as provided by Eversource ($0.18 per KWH).

Voyager PSU Efficiency Upgrade Calc

Voyager PSU Efficiency Upgrade Calc

Performing this calculation is fairly straightforward. Basically, it’s just taking the difference in wattage between the two power supply units and turning that into energy by multiplying it by one year’s worth of run time (Energy = Power * Time). Then, I multiply that out by the cost of energy to get a yearly cost savings of about $20 bucks. That’s not bad! Basically, I could pay for my PSU upgrade in two years if I run the machine constantly.

Things get better if I sell the old PSU. Getting $20 for a Seasonic anything should be easly (ignoring the moral dilemma of sticking someone with a shitty power supply that whines and makes their lights flicker). Then, I’d recoup my investment in a year, all while saving the planet!

So, from my perspective as someone who runs the computer 24/7, this power supply efficiency upgrade makes a lot of sense. It might not make as much sense for people whose computers are off for most of the day, or for computers that just sit around idle, because then it would take a lot longer to recover the costs.

P.S. Now when I pop the side panel off Voyager, I am reminded to focus…

Voyager New PSU

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AMD Radeon RX 580 8GB Folding@Home Review

Posted on November 9, 2019 | 18 comments

Hello again.

Today, I’ll be reviewing the AMD Radeon RX 580 graphics card in terms of its computational performance and power efficiency for Stanford University’s Folding@Home project. For those that don’t know, Folding@Home lets users donate their computer’s computations to support disease research. This consumes electrical power, and the point of my blog is to look at how much scientific work (Points Per Day or PPD) can be computed for the least amount of electrical power consumption. Why? Because in trying to save ourselves from things like cancer, we shouldn’t needlessly pollute the Earth. Also, electricity is expensive!

The Card

AMD released the RX 580 in April 2017 with an MSRP of $229. This is an updated card based on the Polaris architecture. I previously reviewed the RX 480 (also Polaris) here, for those interested. I picked up my MSI-flavored RX 580 in 2019 on eBay for about $120, which is a pretty nice depreciated value. Those who have been following along know that I prefer to buy used video cards that are 2-3 years old, because of the significant initial cost savings, and the fact that I can often sell them for the same as I paid after running Folding for a while.

RX_580

MSI Radeon RX 580

I ran into an interesting problem installing this card, in that at 11 inches long, it was about a half inch too long for my old Raidmax Sagitta gaming case. The solution was to take the fan shroud off, since it was the part that was sticking out ever so slightly. This involved an annoying amount of disassembly, since the fans actually needed to be removed from the heat sink for the plastic shroud to come off. Reattaching the fans was a pain (you need a teeny screw driver that can fit between the fan blade gaps to get the screws by the hub).

RX_580_noShroud

RX 580 with Fan Shroud Removed. Look at those heat pipes! This card has a 185 Watt TDP (Board Power Rating). 

RX_580_Installed

RX 580 Installed (note the masking tape used to keep the little side LED light plate off of the fan)

RX_580_tightFit

Now That’s a Tight Fit (the PCI Express Power Plug on the video card is right up against the case’s hard drive bays)

The Test Setup

Testing was done on my rather aged, yet still able, AMD FX-based system using Stanford’s Folding@Home V7 client. Since this is an AMD graphics card, I made sure to switch the video card mode to “compute” within the driver panel. This optimizes things for Folding@home’s workload (as opposed to games).

Test Setup Specs

  • Case: Raidmax Sagitta
  • CPU: AMD FX-8320e
  • Mainboard : Gigabyte GA-880GMA-USB3
  • GPU: MSI Radeon RX 580 8GB
  • Ram: 16 GB DDR3L (low voltage)
  • Power Supply: Seasonic X-650 80+ Gold
  • Drives: 1x SSD, 2 x 7200 RPM HDDs, Blu-Ray Burner
  • Fans: 1x CPU, 2 x 120 mm intake, 1 x 120 mm exhaust, 1 x 80 mm exhaust
  • OS: Win10 64 bit
  • Video Card Driver Version: 19.10.1

 

Performance and Power

I ran the RX 580 through its paces for about a week in order to get a good feel for a variety of work units. In general, the card produced as high as 425,000 points per day (PPD), as reported by Stanford’s servers. The average was closer to 375K PPD, so I used that number as my final value for uninterrupted folding. Note that during my testing, I occasionally used the machine for other tasks, so you can see the drops in production on those days.

RX 580 Client

Example of Client View – RX 580

RX580 History

RX 580 Performance – About 375K PPD

I measured total system power consumption at the wall using my P3 Watt Meter. The system averaged about 250 watts. That’s on the higher end of power consumption, but then again this is a big card.

Comparison Plots

RX 580 Performance

AMD Radeon RX 580 Folding@Home Performance Comparison

RX 580 Efficiency

AMD Radeon RX 580 Folding@Home Efficiency Comparison

Conclusion

For $120 used on eBay, I was pretty happy with the RX 580’s performance. When it was released, it was directly competing with Nvidia’s GTX 1060. All the gaming reviews I read showed that Team Red was indeed able to beat Team Green, with the RX 580 scoring 5-10% faster than the 1060 in most games. The same is true for Folding@Home performance.

However, that is not the end of the story. Where the Nvidia GTX 1060 has a 120 Watt TDP (Thermal Dissipated Power), AMD’s RX 580 needs 185 Watts. It is a hungry card, and that shows up in the efficiency plots, which take the raw PPD (performance) and divide out the power consumption in watts I am measuring at the wall. Here, the RX 580 falls a bit short, although it is still a healthy improvement over the previous generation RX 480.

Thus, if you care about CO2 emissions and the cost of your folding habits on your wallet, I am forced to recommend the GTX 1060 over the RX 580, especially because you can get one used on eBay for about the same price. However, if you can get a good deal on an RX 580 (say, for $80 or less), it would be a good investment until more efficient cards show up on the used market.

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Folding@Home: Nvidia GTX 1080 Review Part 3: Memory Speed

Posted on May 19, 2019 | 6 comments

In the last article, I investigated how the power limit setting on an Nvidia Geforce GTX 1080 graphics card could affect the card’s performance and efficiency for doing charitable disease research in the Folding@Home distributed computing project. The conclusion was that a power limit of 60% offers only a slight reduction in raw performance (Points Per Day), but a large boost in energy efficiency (PPD/Watt). Two articles ago, I looked at the effect of GPU core clock. In this article, I’m experimenting with a different variable. Namely, the memory clock rate.

The effect of memory clock rate on video games is well defined. Gamers looking for the highest frame rates typically overclock both their graphics GPU and Memory speeds, and see benefits from both. For computation projects like Stanford University’s Folding@Home, the results aren’t as clear. I’ve seen arguments made both ways in the hardware forums. The intent of this article is to simply add another data point, albeit with a bit more scientific rigor.

The Test

To conduct this experiment, I ran the Folding@Home V7 GPU client for a minimum of 3 days continuously on my Windows 10 test computer. Folding@Home points per day (PPD) numbers were taken from Stanford’s Servers via the helpful team at https://folding.extremeoverclocking.com.  I measured total system power consumption at the wall with my P3 Kill A Watt meter. I used the meter’s KWH function to capture the total energy consumed, and divided out by the time the computer was on in order to get an average wattage value (thus eliminating a lot of variability). The test computer specs are as follows:

Test Setup Specs

  • Case: Raidmax Sagitta
  • CPU: AMD FX-8320e
  • Mainboard : Gigabyte GA-880GMA-USB3
  • GPU: Asus GeForce 1080 Turbo
  • Ram: 16 GB DDR3L (low voltage)
  • Power Supply: Seasonic X-650 80+ Gold
  • Drives: 1x SSD, 2 x 7200 RPM HDDs, Blu-Ray Burner
  • Fans: 1x CPU, 2 x 120 mm intake, 1 x 120 mm exhaust, 1 x 80 mm exhaust
  • OS: Win10 64 bit
  • Video Card Driver Version: 372.90

I ran this test with the memory clock rate at the stock clock for the P2 power state (4500 MHz), along with the gaming clock rate of 5000 MHz and a reduced clock rate of 4000 MHz. This gives me three data points of comparison. I left the GPU core clock at +175 MHz (the optimum setting from my first article on the 1080 GTX) and the power limit at 100%, to ensure I had headroom to move the memory clock without affecting the core clock. I verified I wasn’t hitting the power limit in MSI Afterburner.

*Update. Some people may ask why I didn’t go beyond the standard P0 gaming memory clock rate of 5000 MHz (same thing as 10,000 MHz double data rate, which is the card’s advertised memory clock). Basically, I didn’t want to get into the territory where the GDDR5’s error checking comes into play. If you push the memory too hard, there can be errors in the computation but work units can still complete (unlike a GPU core overclock, where work units will fail due to errors). The reason is the built-in error checking on the card memory, which corrects errors as they come up but results in reduced performance. By staying away from 5000+ MHz territory on the memory, I can ensure the relationship between performance and memory clock rate is not affected by memory error correction.

1080 Memory Boost Example

Memory Overclocking Performed in MSI Afterburner

Tabular Results

I put together a table of results in order to show how the averaging was done, and the # of work units backing up my +500 MHz and -500 MHz data points. Having a bunch of work units is key, because there is significant variability in PPD and power consumption numbers between work units. Note that the performance and efficiency numbers for the baseline memory speed (+0 MHz, aka 4500 MHz) come from my extended testing baseline for the 1080 and have even more sample points.

Geforce 1080 PPD Production - Ram Study

Nvidia GTX 1080 Folding@Home Production History: Data shows increased performance with a higher memory speed

Graphic Results

The following graphs show the PPD, Power Consumption, and Efficiency curves as a function of graphics card memory speed. Since I had three points of data, I was able to do a simple three-point-curve linear trendline fit. The R-squared value of the trendline shows how well the data points represent a linear relationship (higher is better, with 1 being ideal). Note that for the power consumption, the card seems to have used more power with a lower memory clock rate than the baseline memory clock. I am not sure why this is…however, the difference is so small that it is likely due to work unit variability or background tasks running on the computer. One could even argue that all of the power consumption results are suspect, since the changes are so small (on the order of 5-10 watts between data points).

Geforce 1080 Performance vs Ram Speed

Geforce 1080 Power vs Ram Speed

Geforce 1080 Efficiency vs Ram Speed

Conclusion

Increasing the memory speed of the Nvidia Geforce GTX 1080 results in a modest increase in PPD and efficiency, and arguably a slight increase in power consumption. The difference between the fastest (+500 MHz) and slowest (-500 MHz) data points I tested are:

PPD: +81K PPD (11.5%)

Power: +9.36 Watts (3.8%)

Efficiency: +212.8 PPD/Watt (7.4%)

Keep in mind that these are for a massive difference in ram speed (5000 MHz vs 4000 MHz).

Another way to look at these results is that underclocking the graphics card ram in hopes of improving efficiency doesn’t work (you’ll actually lose efficiency). I expect this trend will hold true for the rest of the Nvidia Pascal series of cards (GTX 10xx), although so far my testing of this has been limited to this one card, so your mileage may vary. Please post any insights if you have them.

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NVIDIA GEFORCE GTX 1080 Folding@Home Review (Part 2)

Posted on May 7, 2019 | 3 comments

Welcome back. In the last article, I found that the GeForce GTX 1080 is an excellent graphics card for contributing to Stanford University’s charitable distributed computing project Folding@Home. For Part 2 of the review, I did some extended testing to determine the relationship between the card’s power target and Folding@Home performance & efficiency.

Setting the graphics card’s power target to something less than 100% essentially throttles the card back (lowers the core clock) to reduce power consumption and heat. Performance generally drops off, but computational efficiency (performance/watt of power used) can be a different story, especially for Folding@Home. If the amount of power consumed by the card drops off faster than the card’s performance (measured in Points Per Day for Folding@Home), then the performance can actually go up!

Test Methodology

The test computer and environment was the same as in Part 1. Power measurements were made at the wall with a P3 Kill A Watt meter, using the KWH function to track the total energy used by the computer and then dividing by the recorded uptime to get an average power over the test period. Folding@Home PPD Returns were taken from Stanford’s collection servers.

To gain useful statistics, I set the power limit on the graphics card driver via MSI Afterburner and let the card run for a week at each setting. Averaging the results over many days is needed to reduce the variability seen across work units. For example, I used an average of 47 work units to come up with the performance of 715K PPD for the 80% Power Limit case:

Work Unit Averaging

80% Power Limit: Average PPD Calculation over Six Days

The only outliers I tossed was one day when my production was messed up by thunderstorms (unplug your computers if there is lighting!), plus one of the days at the 60% power setting, where for some reason the card did almost 900K PPD (probably got a string of high value work units). Other than that the data was not massaged.

I tested the card at 100% power target, then at 80%, 70%, 60%, and 50% (90% did not result in any differences vs 100% because folding doesn’t max out the graphics card, so essentially it was folding at around 85% of the card’s power limit even when set to 90% or 100%).

FAH 1080 Power Target Example

Setting the Power Limit in MSI Afterburner

I left the core clock boost setting the same as my final test value from the first part of this review (+175 MHz). Note that this won’t force the card to run at a set faster speed…the power limit constantly being hit causes the core clock to drop. I had to reduce the power limit to 80% to start seeing an effect on the core clock. Further reductions in power limit show further reductions in clock rate, as expected. The approximate relationship between power limit and core clock was this:

Core Clock vs Power Limit

GTX 1080 Core Clock vs. Power Limit

Results

As expected, the card’s raw performance (measured in Points Per Day) drops off as the power target is lowered.

GTX 1080 Performance Part 2

Folding@Home Performance

 

The system power consumption plot is also very interesting. As you can see, I’ve shaved a good amount of power draw off of this build by downclocking the card via the power limit. GTX 1080 Power Consumption

 

By far, the most interesting result is what happens to the efficiency. Basically, I found that efficiency increases (to a point) with decreasing power limit. I got the best system efficiency I’ve ever seen with this card set to 60% power limit (50% power limit essentially produced the same result).

GTX 1080 Efficiency Part 2

Folding@Home Efficiency

Conclusion

For NVIDIA’s Geforce GTX 1080, decreasing a graphic’s card’s power limit can actually improve the efficiency of the card for doing computational computing in Folding@Home. This is similar to what I found when reviewing the 1060. My recommended setting for the 1080 is a power limit of 60%, because that provides a system efficiency of nearly 3500 PPD/Watt and maintains a raw performance of almost 700K PPD.

 

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NVIDIA GEFORCE GTX 1080 Folding@Home Review (Part 1)

Posted on April 8, 2019 | 1 comment

Intro

It’s hard to believe that the Nvidia GTX 1080 is almost three years old now, and I’m just getting around to writing a Folding@Home review of it. In the realm of graphics cards, this thing is legendary, and only recently displaced from the enthusiast podium by Nvidia’s new RTX series of cards. The 1080 was Nvidia’s top of the line gaming graphics card (next to the Ti edition of course), and has been very popular for both GPU coin mining and cancer-curing (or at least disease research for Stanford University’s charitable distributed computing project: Folding@Home). If you’ve been following along, you know it’s that second thing that I’m interested in. The point of this review is to see just how well the GTX 1080 folds…and by well, I mean not just raw performance, but also energy efficiency.

Quick Stats Comparison

I threw together a quick table to give you an idea of where the GTX 1080 stacks up (I left the newer RTX cards and the older GTX 9-series cards off of here because I’m lazy…

Nvidia Pascal Cards

Nvidia Pascal Family GPU Comparison

As you can see, the GTX 1080 is pretty fast, eclipsed only by the GTX 1080 Ti (which also has a higher Thermal Design Power, suggesting more electricity usage). From my previous articles, we’ve seen that the more powerful cards tend to do work more efficiency, especially if they are in the same TDP bracket. So, the 1080 should be a better folder (both in PPD and PPD/Watt efficiency) than the 1070 Ti I tested last time.

Test Card: ASUS GeForce GTX 1080 Turbo

As with the 1070 Ti, I picked up a pretty boring flavor of a 1080 in the form of an Asus turbo card. These cards lack back plates (which help with circuit board rigidity and heat dissipation) and use cheap blower coolers, which suck in air from a single centrifugal fan on the underside and blow it out the back of the case (keeping the hot air from building up in the case). These are loud, and tend to run hotter than open-fan coolers, so overclocking and boost clocks are limited compared to aftermarket designs. However, like Nvidia’s own Founder’s Edition reference cards, this reference design provides a good baseline for a 1080’s minimum performance.

ASUS GeForce GTX 1080 Turbo

ASUS GeForce GTX 1080 Turbo

The new 1080 looks strikingly similar to the 1070 Ti…Asus is obviously reusing the exact same cooler since both cards have a 180 Watt TDP.

Asus GTX 1080 and 1070 Ti

Asus GTX 1080 and 1070 Ti (which one is which?)

Test Environment

Like most of my previous graphics card testing, I put this into my AMD FX-Based Test System. If you are interested in how this test machine does with CPU folding, you can read about it here. Testing was done using Stanford’s Folding@Home V7 Client (version 7.5.1) in Windows 10. Points Per Day (PPD) production was collected from Stanford’s servers. Power measurements were done with a P3 Kill A Watt Meter (taken at the wall, for a total-system power profile).

Test Setup Specs

  • Case: Raidmax Sagitta
  • CPU: AMD FX-8320e
  • Mainboard : Gigabyte GA-880GMA-USB3
  • GPU: Asus GeForce 1080 Turbo
  • Ram: 16 GB DDR3L (low voltage)
  • Power Supply: Seasonic X-650 80+ Gold
  • Drives: 1x SSD, 2 x 7200 RPM HDDs, Blu-Ray Burner
  • Fans: 1x CPU, 2 x 120 mm intake, 1 x 120 mm exhaust, 1 x 80 mm exhaust
  • OS: Win10 64 bit
  • Video Card Driver Version: 372.90

Video Card Configuration – Optimize for Performance

In my previous articles, I’ve shown how Nvidia GPUs don’t always automatically boost their clock rates when running Folding@home (as opposed to video games or benchmarks). The same is true of the GTX 1080. It sometimes needs a little encouragement in order to fold at the maximum performance. I overclocked the core by 175 MHz and increased the power limit* by 20% in MSI afterburner using similar settings to the GTX 1070. These values were shown to be stable after 2+ weeks of testing with no dropped work units.

*I also experimented with the power limit at 100% and I saw no change in card power consumption. This makes sense…folding is not using 100% of the GPU. Inspection of the MSI afterburner plots shows that while folding, the card does not hit the power limit at either 100% or 120%. I will have to reduce the power limit to get the card to throttle back (this will happen in part 2 of this article).

As with previous cards, I did not push the memory into its performance zone, but left it at the default P2 (low-power) state clock rate. The general consensus is that memory clock does not significantly affect folding@home, and it is better to leave the power headroom for the core clock, which does improve performance. As an interesting side-note, the memory clock on this thing jumps up to 5000 Mhz (effective) in benchmarks. For example, see the card’s auto-boost settings when running Heaven:

1080 Benchmark Stats

Nvidia GeForce GTX 1080 – Boost Clocks (auto) in Heaven Benchmark

Testing Overview

For most of my tests, I just let the computer run folding@home 24/7 for a couple of days and then average the points per day (PPD) results from Stanford’s stats server. Since the GTX 1080 is such a popular card, I decided to let it run a little longer (a few weeks) to get a really good sampling of results, since PPD can vary a lot from work unit to work unit. Before we get into the duration results, let’s do a quick overview of what the Folding@home environment looks like for a typical work unit.

The following is an example screen shot of the display from the client, showing an instantaneous PPD of about 770K, which is very impressive. Here, it is folding on a core 21 work unit (Project 14124).

F@H Client 1080

Folding@Home V7 Client – GeForce GTX 1080

MSI Afterburner is a handy way to monitor GPU stats. As you can see, the GPU usage is hovering in the low 80% region (this is typical for GPU folding in Windows. Linux can use a bit more of the GPU for a few percentage points more PPD). This Asus card, with its reference blower cooler, is running a bit warm (just shy of 70 degrees C), but that’s well within spec. I had the power limit at 120%, but the card is nowhere near hitting that…the power limit seems to just peak above 80% here and there.

GTX 1080 MSI Afterburner

GTX 1080 stats while folding.

Measuring card power consumption with the driver shows that it’s using about 150 watts, which seems about right when compared to the GPU usage and power % graphs. 100% GPU usage would be ideal (and would result in a power consumption of about 180 watts, which is the 1080’s TDP).

In terms of card-level efficiency, this is 770,000 PPD / 150 Watts = 5133 PPD/Watt.

Power Draw (at the card)

Nvidia Geforce GTX 1080 – Instantaneous Power Draw @ the Card

Duration Testing

I ran Folding@Home for quite a while on the 1080. As you can see from this plot (courtesy of https://folding.extremeoverclocking.com/), the 1080 is mildly beating the 1070 Ti. It should be noted that the stats for the 1070 Ti are a bit low in the left-hand side of the plot, because folding was interrupted a few times for various reasons (gaming). The 1080 results were uninterrupted.

1080 Production History

Geforce GTX 1080 Production History

Another thing I noticed was the amount of variation in the results. Normal work unit variation (at least for less powerful cards) is around 10-20 percent. For the GTX 1080, I saw swings of 200K PPD, which is closer to 30%. Check out that one point at 875K PPD!

Average PPD: 730K PPD

I averaged the PPD over two weeks on the GTX 1080 and got 730K PPD. Previous testing on the GTX 1070 Ti (based on continual testing without interruptions) showed an average PPD of 700K. Here is the plot from that article, reproduced for convenience.

Nvidia GTX 1070 Ti Time History

Nvidia GTX 1070 Ti Folding@Home Production Time History

I had expected my GTX 1080 to do a bit better than that. However, it only has about 5% more CUDA cores than the GTX 1070 Ti (2560 vs 2438). The GTX 1080’s faster memory also isn’t an advantage in Folding@Home. So, a 30K PPD improvement for the 1080, which corresponds to about a 4.3% faster, makes sense.

System Average Power Consumption: 240 Watts @ the Wall

I spot checked the power meter (P3 Kill A Watt) many times over the course of folding. Although it varies with work unit, it seemed to most commonly use around 230 watts. Peek observed wattage was 257, and minimum was around 220. This was more variation than I typically see, but I think it corresponds with the variation in PPD I saw in the performance graph. It was very tempting to just say that 230 watts was the number, but I wasn’t confident that this was accurate. There was just too much variation.

In order to get a better number, I reset the Kill-A-Watt meter (I hadn’t reset it in ages) and let it log the computer’s usage over the weekend. The meter keeps track of the total kilowatt-hours (KWH) of energy consumed, as well as the time period (in hours) of the reading. By dividing the energy by time, we get power. Instead of an instantaneous power (the eyeball method), this is an average power over the weekend, and is thus a compatible number with the average PPD.

The end result of this was 17.39 KWH consumed over 72.5 hours. Thus, the average power consumption of the computer is:

17.39/72.5 (KWH/H) * 1000 (Watts/KW) = about 240 Watts (I round a bit for convenience in reporting, but the Excel sheet that backs up all my plots is exact)

This is a bit more power consumed than the GTX 1070 Ti results, which used an average of 225 watts (admittedly computed by the eyeball method over many days, but there was much less variation so I think it is valid). This increased power consumption of the GTX 1080 vs. the 1070 Ti is also consistent with what people have seen in games. This Legit Reviews article shows an EVGA 1080 using about 30 watts more power than an EVGA 1070 Ti during gaming benchmarks. The power consumption figure is reproduced below:

LegitReviews_power-consumption

Modern Graphics Card Power Consumption. Source: Legit Reviews

This is a very interesting result. Even though the 1080 and the 1070 Ti have the same 180 Watt TDP, the 1080 draws more power, both in folding@home and in gaming.

System Computational Efficiency: 3044 PPD/Watt

For my Asus GeForce GTX 1080, the folding@home efficiency is:

730,000 PPD / 240 Watts = 3044 PPD/Watt.

This is an excellent score. Surprisingly, it is slightly less than my Asus 1070 Ti, which I found to have an efficiency of 3126 PPD/Watt. In practice these are so close that it just could be attributed to work unit variation. The GeForce 1080 and 1070 Ti are both extremely efficient cards, and are good choices for folding@home.

Comparison plots here:

GeForce 1080 PPD Comparison

GeForce GTX 1080 Folding@Home PPD Comparison

GeForce 1080 Efficiency Comparison

GeForce GTX 1080 Folding@Home Efficiency Comparison

Final Thoughts

The GTX 1080 is a great card. With that said, I’m a bit annoyed that my GTX 1080 didn’t hit 800K PPD like some folks in the forums say theirs do (I bet a lot of those people getting 800K PPD use Linux, as it is a bit better than Windows for folding). Still, this is a good result.

Similarly, I’m annoyed that the GTX 1080 didn’t thoroughly beat my 1070 Ti in terms of efficiency. The results are so close though that it’s effectively the same. This is part one of a multi-part review, where I tuned the card for performance. In the next article, I plan to go after finding a better efficiency point for running this card by experimenting with reducing the power limit. Right now I’m thinking of running the card at 80% power limit for a week, and then at 60% for another week, and reporting the results. So, stay tuned!

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

Posted on April 1, 2019 | 7 comments

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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Nvidia GeForce GTX 1070 Ti Folding@Home Review

Posted on February 23, 2019 | 9 comments

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.

IMG_20190206_185514342

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.

IMG_20190206_190210910_TOP

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

Conclusion

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.

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Folding@Home Efficiency vs. GPU Power Limit

Posted on February 7, 2019 | 2 comments

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.

 

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Folding on the Nvidia GTX 1070

Posted on January 28, 2019 | 1 comment

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’s, 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 an EVGA NVIDIA GTX 1070 graphics card.  This card offers a big step up in gaming and compute horsepower compared to the 1060 I reviewed previously, and is capable of pushing solid frame rates at 4K resolution. So, how well does it fold?

Card Specifications (Nvidia Reference Specs)

1070 specs

Nvidia GTX 1070 Specifications

evga 1070 acx stock photo

EVGA Nvidia GTX 1070 ACX 3.0 (photo credit: 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 10 64-bit running FAH Core 21 work units.  The video driver version used was initially 388.59, and subsequently 372.90. Power consumption measurements reported in the charts 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!

Initial Testing and Troubleshooting

Like the GTX 1060, the 1070 uses Nvidia’s Pascal architecture, which is very efficient and has a reputation for solid compute performance. The 1070 has 50% more CUDA cores than the 1060, and with Folding@Home’s exponential points system (the quick return bonus gives you more points for doing work quickly), we should see roughly double the PPD of the 1060, which does 300 – 350 thousand PPD depending on the work unit. Based on various people’s experiences, and especially this forum post, I was expecting the 1070 to produce somewhere in the range of 600-700K PPD.

That wasn’t what happened. The card wasn’t exactly slow, but initial testing showed an estimated 450 to 550K PPD, as reported by the client. I ran it for a few days, since PPD can vary a good deal depending on the work unit, but the result was unfortunately the same. 550K PPD was about as much as my card would do.

initial_1070_results

Initial GTX 1070 Results – 544K PPD

At first I thought it might be due to the card running hot. Unlike my test of a brand new 1060, I obtained my 1070 used off of eBay for a great price of $200 dollars + shipping. It was a little dusty, so I blew it all out and fired up MSI Afterburner to check out the temps. Unfortunately, the fans on the card weren’t even breaking a sweat, and it was nice and cool. Points didn’t increase.

evga 1070 acx 3.0

My Used EVGA GTX 1070 ACX 3.0 – eBay Price: $200

initial 1070 afterburner report

MSI Afterburner Report: NVidia GTX 1070, Stock Clocks, Driver 388.59

After doing some more digging, I ran across a few threads online that indicated the 1070 (along with a few other GTX models) don’t always boost up to their maximum clock rates for compute loads. Opening up a video, or Folding@home’s protein viewer, can sometimes force the card to clock up. I tried this and didn’t have any luck. My card was running at the stock clocks, and in fact the memory even appeared to be running 200 Megahertz below the 4000 Mhz reference clock rate. This suggested the card was in a low-power mode.

Thankfully, Nvidia’s System Management Interface tool can be used to see what is going on. This tool, which in Windows 10 lives in C:\Program Files\Nvidia Corporation, can be accessed by the command line. I followed the tutorial here to learn a few things about what my 1070 was doing. Although that write-up is geared at people mining for cryptocurrency, the steps are still releveant.

As can be seen here, my card was in the “P2” state, which is not the high-performance “P0” state. This is why the card wasn’t boosting, and why the memory clock seems diminished.

1070 performance state

Nvidia 1070 Performance State

Another feature of the Nvidia System Management Interface is the ability to get the power consumption at the card. This is measured by the driver, using the card’s hardware, and is the total instantaneous power the card is consuming (PCI slot power + supplemental power connections). As you can see, in the P2 state, the card is very rarely nearing the 150 watt TDP.

Now, this doesn’t necessarily mean the card would get closer to 150 watts in the P0 state. F@H does not utilize every portion of the graphics card, and it is expected that the power consumption would not be right at the limit. Still, these numbers seemed a bit low to me.

1070 card-level power consumption (before tuning)

1070 card-level power consumption (before tuning)

Overclocking Manually to Approximate P0 State

Unlike what was suggested in that crypto mining article, I wasn’t able to use the NVSMI tool to force a P0 state. For some reason, my NVSMI tool wouldn’t show me the available clock rate settings for my 1070. However, manual overclocking with a program such as MSI Afterburner is really easy. By maxing out the power limit and setting the core clock to a higher value, I can basically make the card run at its boost frequency, or higher.

First, I set the power limit to the maximum allowed (112%). Don’t worry, this won’t hurt anything. It is limited in the driver to not cause any damage. Basically, this will allow the card to sip a bit more electricity (albeit at a reduction of efficiency). For a card that was in the P0 state (say, running a video game), this would allow higher boost clocks.

Next, I started upping the core clock in increments of 100 Mhz. I didn’t run into any stability problems, and settled in on a core clock of 2000 Mhz (factory clock is 1506 Mhz / 1683 boost). Note that that factory boost number is deceiving, since the latest drivers will crank the GPU core up past 1900 MHz if there is power and voltage headroom. From what I read, many people can run the 1070 stable at 2050 Mhz without adding voltage.

I decided not to boost the voltage, and to stay 50 Mhz below that supposedly stable number, because it’s not worth risking the stability of Folding@home. We want accurate, repeatable science! Plus, dropping work units is much worse for PPD than running slightly below a card’s maximum capability.

I experimented with clocking the memory up from 3800 MHz to 4000 MHz (note it’s double data rate so this equates to 8000 MHz as reported by some programs). This didn’t seem to affect results. F@H has historically been fairly insensitive to memory clocks, and boosting memory too much can cause slowdowns due to the error-checking routines having to work harder to ensure clean results. Basically, everyone says it’s not worth it. I ran it at 4000 MHz long enough to confirm this (a day), then throttled it back down to 3800 MHz. The benefit here will be more power available for the GPU cores, which is what really counts for folding.

Here are my final overclock numbers. The card has been running with these clocks for a week and a half non-stop, with no stability issues:

final 1070 afterburner report

Overclocked Settings: +160 MHz Core, 112% Power Limit

Note the driver version as shown in the updated Afterburner screen shot is different…as it turns out, this can have a huge effect on F@H PPD. More on that in a moment.

Overclocking Result: An Extra 50,000 PPD

Running the core at 2012 MHz (+160 MHz boost from the P2 power state) and upping the card’s power limit by 12% made the average PPD, as observed over two days, climb from 500-550K PPD to 550K-600K PPD. So, that’s a 50,000 PPD increase for minimal effort. But, something still seemed off. At the time I was still running driver version 388.59, and one of the things I had discovered when searching around for 1070 tuning tips is that not all drivers are created equal.

Nvidia Driver 372.90: The Best Folding Driver for the GTX 1070

Nvidia has been updating drivers with more and more emphasis on gaming optimizations and less on compute. So, it makes sense that older drivers might actually offer better compute performance. There are many threads in the Folding@Home Hardware Forum discussing this, and one driver version that keeps being mentioned is 372.90. It’s a bit tricky to keep it installed on Windows 10, since Windows is always trying to push a newer version, but for my 24/7 folding rig, I installed it and simply never rebooted it in order to get a week’s worth of data.

This driver change alone seemed to also offer a 50,000 point boost. After running various core 21 work units, the GTX 1070’s PPD has stayed between 630,000 and 660,000. This is normal variation between work units, and I feel confident reporting a final PPD of 640K. As I write this, the client is estimating 660K PPD.

final_1070_results

Nvidia GTX 1070: 660K PPD on Project 13815 (Core 21)

This is an excellent result. It’s twice the PPD of the GTX 1060, although eking out that last 100K PPD took a manual overclock plus a driver “update” to an older version.

Now, for the fun part. Efficiency! This 1070 is rated at 150 watts, which is only 30 watts more than the 1060. So we are supposedly doing 100% more science for Stanford University, and for a meager 25% increase in power consumption. Time to bust out the watt meter and find out!

Power Consumption at the Wall

Using my P3 Kill-A-Watt Power Meter, I measured the total system power consumption. This is the same way I measure all of my graphics cards (as opposed to estimating the card’s power by the TDP or using the video card driver to spit out instantaneous card power). The reason is that I like to have a full-system view, factoring in the power usage of my CPU, main board, and RAM, all essential components to keep the card happy.

While folding with the GTX 1070, my system’s total power draw varied between 225 and 230 watts. I’m going to go with 227 watts as the average power number. 

Efficiency

Computing computational efficiency as Points Per Day (PPD) / Power (Watts) gives:

640,000 PPD / 227 Watts = 2820 PPD/Watt.

Conclusion

The Nvidia GTX 1070 is a very efficient card for running Stanford’s Folding@Home Distributed Computing Project. The trend established in my previous articles seems to be continuing, namely that the more expensive high-end video cards are more efficient, despite their higher power draw. In this case of the 1070, some manual overclocking was needed to unlock the full PPD potential. As proven by many others, the default drivers weren’t very good, but the 372.90 drivers really opened it up.

Base PPD: 550,000

Tuned PPD (drivers + overclock) = 640,000

PPD/Watt(@wall) = 2820

1070 ppd plot

Nvidia GTX 1070 Performance Comparison

1070 efficiency plot

Nvidia 1070 Efficiency Comparison

As a final note, this post focused more on PPD than efficiency, since for much of the testing my watt meter was not installed (my kids keep playing with it). At some point in the future, I’ll do an article where I tune one of these cards to find the best efficiency point. This will likely be at a lower power limit than 100%, with perhaps a slight reduction in clock rate.

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Is Folding@Home a Waste of Electricity?

Posted on May 28, 2018 | 3 comments

Folding@home has brought together thousands of people (81 thousand active folders as of the time of this writing, as evidenced from Stanford’s One in a Million contributor drive.) This is awesome…tens of thousands of people teaming up to help researchers unravel the mysteries of terrible diseases.

But, there is a cost. If you are reading this blog, then you know the cost of scientific computing projects such as Folding@Home is environmental. In trying to save ourselves from the likes of cancer and Alzheimer’s disease, we are running a piece of software that causes our computers to use more electricity. In the case of dedicated folding@home computers, this can be hundreds of watts of power consumed 24/7. It adds up to a lot of consumed power, that in the end exits your computer as heat (potentially driving up your air conditioning costs as well).

Folding on Graphics Card Thermal

FLIR Thermal Cam – Folding@Home on Graphics Card

If Stanford reaches their goal of 1 million active folders, then we have an order of magnitude more power consumption on our hands. Let’s do some quick math, assuming each folder contributes 200 watts continuous (low compared to the power draw of most dedicated Folding@home machines). In this case, we have 200 watts/computer * 24 hours/day * 365 days/year * 1,000,000 computers *1 kilowatt-hour/1000 watt-hours = 1,752,000,000 kilowatt-hours of power consumed in a year, in the name of Science!

That’s almost two billion kilowatt-hours, people.  It’s 1.75 terawatt-hours (TWh)! Using the EPA’s free converter can put that into perspective. Basically, this is like driving 279 thousand extra cars for a year, or burning 1.5 billion pounds of coal.  Yikes!

https://www.epa.gov/energy/greenhouse-gas-equivalencies-calculator

F@H Energy Equivalence

Potential Folding@Home Environmental Impact

Is all this disease research really harming the planet? If it is, is it worth it? I don’t know. It depends on the outcome of the research, the potential benefit to humans, and the detriment to humans, animals, and the environment caused by that research. This opens up all sorts of what-if scenarios.

For example: what if Folding@Home does help find a future cure for many diseases, which results in extended life-spans. Then, the earth gets even more overpopulated than it is already. Wouldn’t the added environmental stresses negatively impact people’s health? Conversely, what if Folding@Home research results in a cure for a disease that allows a little girl or boy to grow to adulthood and become the inventor of some game-changing green technology?

It’s just not that easy to quantify.

Then, there is the topic of Folding@home vs. other distributed computing projects. Digital currency, for example. Bitcoin miners (and all the spinoffs) suck up a ton of power. Current estimates put Bitcoin alone at over 40 TWH a year.

Source: https://www.theguardian.com/technology/2018/jan/17/bitcoin-electricity-usage-huge-climate-cryptocurrency

That’s more power than some countries use, and twenty times more than my admittedly crude future Folding@home estimate. When you consider that the cryptocurrency product has only limited uses (many of which are on the darkweb for shady purposes), it perhaps helps cast Folding@home in a better light.

There is always room for improvement thought. That is the point of this entire blog. If we crazies are committed to turning our hard-earned dollars into “points”, we might as well do it in the most efficient way possible. And, while we’re at it, we should consider the environmental cost of our hobby and think of ways to offset it (that goes for the Bitcoin folks too).

I once ran across a rant on another online blog about how Folding@home is killing the planet. This was years ago, before the Rise of the Crypto. I wish I could find that now, but it seems to have been lost in the mists of time, long since indexed, ousted, and forgotten by the Google Search Crawler. In it, the author bemoaned over how F@H was murdering mother earth in the name of science. I recall thinking to myself, “hey, they’ve got a point”. And then I realized that I had already done a bunch of things to help combat the rising electric bill, and I bet most distributed computing participants have done some of these things too.

These things are covered elsewhere in this blog, and range from optimizing the computer doing the work to going after other non-folding@home related items to help offset the electrical and environmental cost. I started by switching to LED light-bulbs, then went to using space heaters instead of whole house heating methods in the winter. As I upgraded my Folding@home computer, I made it more energy efficient not just for F@H but for all tasks executed on that machine.

In the last two years, my wife and I bought a house, which gave us a whole other level of control over the situation. We had one of those state-subsidized energy audits done. They put in some insulation and air-sealed our attic, thus reducing our yearly heating costs. Eventually, we even decided to put solar panels on the roof and get an electric car (these last two weren’t because I felt guilty about running F@H, but because my wife and I are just into green technologies). We even use our Folding@home computer as a space heater in the winter, thus offsetting home heating oil use and negating any any environmental arguments against F@H in the winter months.

In conclusion, there is no doubt that distributed projects have an environmental cost. However, to claim that they are a waste of electricity or that they are killing the planet might be taking it too far. One has to ask if the cause is worth the environmental impact, and then figure out ways to lessen that impact (or in some cases get motivated to offset it completely. Solar powered folding farm, anyone?)

Solar Panel in Basement

LG 320 Solar Panel in my basement, awaiting roof install.

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