AMD Radeon RX 580 8GB Folding@Home Review

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.

Nvidia Geforce GTX 1050 Folding@Home Quick Review

For today’s review, I’m taking a quick look at a little 75 watt graphics card from a few years ago: the Nvidia GTX 1050 2GB. As far as the now somewhat aged Pascal architecture goes, this one is pretty near the bottom of the pile in terms of performance. But, that means you can also get it at a decent discount. I got mine earlier this year for a mere $75 shipped (the card’s MSRP was about $110 back in October 2016).

GeForce GTX 1050 Purchase

GeForce GTX 1050 Purchase Screen Shot

Geforce_1050_2gb_stats

Nvidia GeForce GTX 1050 2GB Stats

As you can see above, I picked up a beefy version…a big dual fan open cooler design by Gigabyte. This is massive overkill for a card with a meager 75 watt TDP (it doesn’t even have a connection for supplemental PCI Express power).

So, you might ask why I would bother reviewing a card of this type, given that all my testing to date has shown that the higher-end cards are also more efficient. Well, in this case I realized that summer in New England means I run a lot of air conditioning, and any extra wattage my computer uses is ultimately dumped into the house as heat. In the winter that’s a good thing, but in the summer it just makes air conditioners do more work removing that waste heat. By running a low-end card, I hoped to continue contributing to Folding@Home while minimizing the electric bill and environmental impact.

Testing was done in the usual manner, using my standard test rig (Windows 10 running the V7 Client) and measuring power at the wall with a watt meter. Normally this is the point where I put up a bunch of links to those previous articles, or sometimes describe in detail the methods and hardware used. But, today I’m feeling lazy so I’m skipping all that. The key is that I am as consistent as I can be (although now I am on Windows 10 due to Windows 7 approaching end of life). If you would like more info on how I run my tests, just go back a few posts to the 1080 and 1070 testing.

Results:

GTX 1050 Point History

GTX 1050 Point History

The circled region on the plot shows what happens in terms of Points Per Day when I “downgraded” from a 1080 to the 1050. The average PPD of about 150K pales in comparison to the 1080. However, when you think about the fact that this is similar performance to cards like the 300 Watt dual-GPU GTX 690 Ti from 2012 (a $1000 card even back then), things don’t seem so bad.

Here’s how the little vanilla 1050 stacks up against the other cards I have tested:

GTX 1050 Production

GTX 1050 Production

From a performance standpoint, this card isn’t going to win any races. However, as I mentioned above, it is actually pretty good compared to high-end old cards (the only one of which I have tested is the Radeon 7970, which cost $550 back in December 2011 and uses nearly three times the power as the 1050.

Efficiency is where things get interesting:

GTX 1050 Efficiency

GTX 1050 Efficiency

Here, you can see that the GTX 1050 has actually leapfrogged up the chart by one place, and is actually slightly more efficient than my copy of an AMD RX 480, which uses the 2016 Polaris architecture–something supposedly designed for efficiency according to AMD. Still, for about $10 more on eBay, you can get a used 1050 Ti, which shows a marked efficiency improvement as well as performance improvement vs the 1050. In my case, I found that both the GTX 1050 and GTX 1050 Ti drew the same amount of power at the wall (140 watts for the whole system). Thus, for my summertime folding, I would have been better off keeping the old GTX 1050 Ti since it does more work for exactly the same amount of wall power consumption.

Conclusion

Nvidia’s GTX 1050 is a small, cheap card that most people use for casual gaming. As a compute card for molecular dynamics, the best things about it is the price of acquiring one used ($75 or less as of 2019), the small size, and no need for an external PCI-Express power connector. Thus, people with low-end desktops and small form-factor PC’s could probably add one of these and without much trouble. I’m going to miss how easy this thing fits in my computer case…

GTX 1050 Easy Fit.jpg

At the end of the day, it was a slow card, making only 150K PPD. The efficiency wasn’t that good either. If you’re going to burn the electricity doing charitable science, it would be good to get more science per watt than the 1050 provides. Given the meager price difference in the used market of this card vs. it’s big Titanium edition brother, go with the Ti (exact same environmental impact but more performance), or better yet, a GTX 1060.

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

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.

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

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.

 

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

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!

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!

Nvidia GeForce GTX 1070 Ti Folding@Home Review

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

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

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

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

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.