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AMD Radeon RX 480 Folding@Home Review

I’ve been reviewing a lot of Nvidia cards lately, so it’s high time I mixed it up a bit. The 4xx series of cards from AMD were released in June 2016, and featured AMD’s new Polaris 14nm architecture. The flagship card, the RX 480, was available in a 4 GB and 8 GB version. The Polaris architecture, which in the RX 480 features 2034 stream processors at a base clock rate of 1120 MHz (1266 boost) and a TDP of 150 watts, was designed to be more efficiency than the aging Fiji architecture used in the R5/R7/R9 300 series.

Now that these cards can be obtained relatively inexpensively on eBay. I picked up a second hand 8 GB card from XFX for $90. Let’s see how it folds compared to some similar graphics cards from Nvidia from that time period. Namely the 1050 and 1060.

 

IMG_20190202_165117036

XFX Radeon RX 480 – 8GB – 150 Watt TDP

Folding@Home testing was done with in Windows 10 on my AMD FX-based test system. The folding@home client was version 7.5.1. The GPU slot options were configured as usual for maximum points per day (PPD) jobs:

Name: client-type  Value: advanced

Name: max-packet-size Value: big

The video driver used was Crimson ReLive 17.7, which includes an essential option for running compute jobs like Folding@Home. This is the ‘compute’ mode for GPU Workload. As previously reported by other folders, this setting can offer significant performance improvement vs. the default gaming setting. I tested it both ways.

AMD Compute Mode

Make sure to set GPU Workload to ‘Compute’ for running Folding@Home Work Units!

Monitoring of the card while folding was done with MSI Afterburner. My particular version of the card by XFX got up to about 76 degrees C when folding, which is pretty warm but not dangerous. The fan settings were on auto, and it was spinning nice and quietly at a touch over 50% speed. The GPU workload % was nicely maxed out at 100 percent, which is something not typically seen on Nvidia cards in Windows. As expected, Folding@Home doesn’t use the full 150 watt TDP. The power usage, as reported at the card, bounced around but was centered at about 110 watts. Although it is expected that the actual power usage would be less than the TDP, this is a lot less, especially considering the 100% GPU usage. I suspect something might be fishy, considering my total system power consumption was pretty high (more on that later).

RX 480 Stock Settings

RX 480 Settings while Folding

Initially, I tested out the driver setting to see if there was a difference between ‘graphics’ and ‘compute’ mode. Although I didn’t see much of a power consumption change (hard to tell since it bounces around), the PPD as reported from the client did change. Note for this testing, I just flipped the switch and observed the time-averaged PPD results as reported from the client. The key here is the project (14152) was the same in both cases, so the result is directly comparable.

In Graphics Mode:

PPD (Estimated) = 290592, TPF (Estimated) = 3 minutes 12 seconds

In Compute Mode:

PPD (Estimated) = 304055, TPF (Estimated) = 2 minutes 59 seconds

That is a pretty significant increase in performance by just flipping a switch. In short, on AMD cards running Folding@Home, always use compute mode.

Here are the screen shots from the client to back this up:

RX 480 Graphics Mode Client View

AMD RX 480 – Graphics Mode

RX 480 Compute Mode Client View

AMD RX 480 – Compute Mode

If you’ve been following along, you know I don’t like to rely on the client’s estimated values for overall PPD numbers. The reason is that it is just an estimate, and it varies a lot between work units. However, for this quick test of graphics vs. compute mode on the same work unit, the results are consistent with those found by other testers.

Overall Performance and Efficiency

I like to run cards for a few days on a variety of work units in order to get some statistics, which I can average to provide more certain results. In this case, I ran folding@home on my RX 480 for over three days. Here are the stats from Stanford’s server, as reported by the kind folks over at Extreme Overclocking.

RX 480 Stats History

Folding @ Home Server Statistics – AMD RX 480 Over 3 Days

As you can see, the average PPD of about 245K PPD wasn’t that impressive, although to be fair the other cards on this plot are all in higher performance price points, except possibly the 1060. I also think this card has potential to churn out over 300k PPD as estimated by the client. This thread seems to suggest this is possible, although the card in that test was overclocked to 1328 MHz vs the 1288 MHz I was running (I didn’t have time to do any overclock testing on mine).

Power consumption measured at the wall varied a bit with the different work units. Spot-checking the numbers with my P3 watt meter resulted in an approximate average total system power consumption of 243 watts. This is much higher than my EVGA GTX 1060 (185 watts at the wall). Just going by the TDP of both cards, I would have guessed the wall power consumption to be somewhere around 215 watts (since the TDP of the RX 480 is 30 watts higher than the 1060).

I ended up selling this card on Ebay a lot faster than I had planned, so I wasn’t able to do detailed testing. However, I suspect the actual power consumption at the card was much higher than what was being reported in MSI Afterburner. After doing some research, it turns out the RX 480 is known to overdraw from both the PCI Express Slot and the supplemental PCI-E power cable. For a card designed to be more efficient, this one is a failure.

Performance Comparison

RX 480 Performance Plot

AMD RX 480 Folding@Home Performance Comparison

Efficiency Comparison

RX 480 Efficiency Plot

AMD RX 480 Folding@Home Efficiency Comparison

Conclusion

The AMD RX 480 produces about 245K PPD while using a surprisingly high 243 watts of system power (measured at the wall). The efficiency is thus about 1000 PPD/Watt. Although better than AMD’s older cards such as a Radeon 7970, these numbers aren’t very competitive, especially when compared to Nvidia’s GTX 1060 (a similarly-priced card from 2016). As of Feb. 2019, the RX 480 can be obtained used for about $100, and the GTX 1060 for $120. If you’re considering buying one of these older cards to do some charitable science with Folding@Home, I recommend spending the extra $20 on the Nvidia 1060, especially because with a mild overclock and a few driver tweaks (use the 372.90 drivers), the Nvidia 1060 can crank out over 350K PPD.

TL;DR: The AMD RX 480 isn’t a very efficient graphics card for running Folding@Home. However, the XFX Version has Pretty Lights…

RX 580 by XFX

Ahh, pretty lights!

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Update: Where I’ve Been

It’s been a few months (well more than a few), so I figured I should explain why there haven’t been so many articles lately. I’ve always liked writing, be it technical blogs like this one, writing for work, or writing fiction. Back in 2005, I started writing science-fiction for fun, and last year I succeeded in completing my first novel. I’ve still been blogging, although I’ve been writing about that novel-writing project instead of distributed computing. If you’re interested in learning about the realms of self-published science fiction, then please do check out my blog at starfightersf.com.

Another reason for not writing folding@home articles is because I haven’t been folding! Even with solar panels, the amount of electricity we use in our home is astonishing, and adding a F@H energy burden to that didn’t make sense, especially not in the warmer months when it increases the load on the air-conditioning (talk about an environmental double-whammy!).

Instead, I decided to wait until it is nice and cold (like right now), so that I can turn down the oil heat in my basement and crank up the folding rig. This way, the electricity serves two purposes: first, charitable disease research for Stanford, and second, heating my basement and saving oil.

In terms of being energy efficient, this is the best way to go!

So, consider this the official restart of Green F@H for the new year. I’ll be kicking things off with the 1070 I just picked up from eBay for a surprisingly palatable $200. As you might have noticed, I don’t tend to review the latest cards, and that’s simply because of the price tag. Buying last-generation’s cast-off cards used has turned out to be an immense money saver, so if earning PPD/dollar is also on your list of priorities, I highly recommend this method.

Stay tuned for the Nvidia GTX 1070 review!

 

 

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

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

Average Points Per Day

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

Folding While Doing Other Things

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

Folding on a Reduced Power Setting

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

GTX 1050 Throttled

MSI Afterburner is used to reduce GPU Power Limit

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

GTX 1050 V7 Throttled Performance

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

GTX 1050 Extended Performance Testing

Nvidia GTX 1050 TI Folding@Home Extended Performance Testing

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

Updated Charts (pending further baseline testing)

GTX 1050 PPD Underpowered

Nvidia GTX 1050 PPD Chart

GTX 1050 Efficiency Underpowered

Nvidia GTX 1050 TI Efficiency

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

Folding@Home Performance on a Budget: Nvidia GeForce GTX 1050 TI

With the release of Nvidia’s new Pascal architecture, the world of computational computing has become a lot more interesting.  Based on a 14 nm process, the GTX 10XX series of graphics cards have set records for power efficiency.  Today, we’ll be looking at the Geforce GTX 1050 TI in terms of Folding@home performance and efficiency.

Nvidia GTX 1050 TI Overview

The GTX 1050 TI is the second from the lowest-end Pascal card (there is a non-TI version with slightly fewer active cores).  One of the most attractive features about this card is its relatively low price ($130 for the EVGA SC version being reviewed here today).  In addition, this card does not require any external power connections, being supplied by the PCI Express slot power alone (about 70 watts).  Online review websites have raved about this card’s impressive gaming potential, small size, and overall efficiency.

Specs (EVGA 1050 TI SC):

Performance

  • NVIDIA GTX 1050 Ti
  • 768 Pixel Pipelines
  • 1354 MHz Base Clock
  • 1468 MHz Boost Clock
  • 65GT/s Texture Fill Rate

Memory

  • 4096 MB, 128 bit GDDR5
  • 7008 MHz (effective)
  • 112.16 GB/s Memory Bandwidth

Interface

  • PCI-E 3.0 16x
  • DVI-D, DisplayPort, HDMI
  • Total Power Draw : 75 Watts

Folding@Home Test Setup

For this test, I swapped out the Radeon 7970 in my gaming computer for the GTX 1050 TI. The first thing I noticed was the incredible size difference between these two cards.  Of course it’s a bit of an apples to oranges comparison…the 7970 is an old top of the line graphics card, whereas the 1050 is a lightweight, entry-level gaming card.

Size Comparison (is bigger always better?  We’ll find out):

Graphics Card Showdown: EVGA Nvidia Geforce GTX 1050 TI vs. Gigabyte AMD Radeon HD 7970 GHz Edition

Graphics Card Showdown: EVGA Nvidia Geforce GTX 1050 TI vs. Gigabyte AMD Radeon HD 7970 GHz Edition

Testing was done using Stanford’s V7 client on Windows 7 64-bit running FAH Core 21 work units.  The video driver version used was 381.65.  All power consumption measurements were taken at the wall and are thus full system power consumption numbers.

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

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

Information on my watt meter readings can be found here:

I Got a New Watt Meter!

Folding@Home Test Results – 192K PPD and 1400 PPD/Watt!

One of the recurring themes of this blog is that using new, basic hardware to run Folding@home often leads to much better performance and energy efficiency than running on dated high-end hardware.  The results here sum that up nicely.  As you can see, the Nvidia GTX 1050 TI is a computational powerhouse in a tiny package.  It dominates in terms of raw F@H PPD as well as PPD/Watt efficiency.  It is also worth mentioning that the total system power draw of only 140 watts is very impressive.

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

Nvidia GTX 1050 TI Folding@Home Performance

192K PPD Reported for 1050 TI!

Points Per Day (PPD) Performance and PPD/Watt Efficiency:

Nvidia GTX 1050 TI Folding@Home PPD Chart

The Nvidia GTX 1050 TI produces about 190K Points Per Day and is faster than all hardware tested so far, including the AMD Radeon 7970

Nvidia GTX 1050 TI Folding@Home Efficiency Chart

The Nvidia Geforce GTX 1050 TI is a very efficient graphics card, resulting in the highest PPD/Watt of all hardware tested so far.

Detailed Table:

Nvidia GTX 1050 TI Folding@Home Stats Table

Real-World Performance Note

One thing I noticed when running on the GTX 1050 was the sensitivity to running other graphics operations and / or CPU folding.  After posting four work-units with an average PPD of over 185K PPD, my wife and I started using the computer for other tasks while leaving folding running.  We noticed significant lag with anything graphical, from scrolling through web pages to streaming video.  In addition, re-enabling 8-core CPU folding seemed to hurt PPD more than it helped (no formal testing numbers to document this at this time, sorry!).

The performance hit of not having a dedicated F@H computer is evident in the following PPD chart, where the high data point is the result of dedicated folding on the graphics card for a day, and the subsequent data points are the result of trying to do other things on the computer as well.  Eventually, we decided to pause F@H to get any respectable streaming quality out of our evening shows.  This wasn’t noticed when folding on the AMD Radeon 7970.  It probably is a consequence of every last bit of the GTX 1050’s compute horsepower being prioritized for F@H, which is one of the reasons why this card does so well when left to fold to its heart’s content.  It looks like an article on building a low-cost dedicated folding box is in order!

Nvidia GTX 1050 TI Folding@Home Extended Testing PPD

Dedicated F@H Performance vs. Multi-Tasking

Conclusion

The Nvidia Geforce GTX 1050 TI excels at Folding@home.  Thanks to its low power consumption (75 watts) and advanced architecture, this card can produce up to 190K PPD in a desktop consuming a mere 140 watts of power from the wall.  This results in a F@H efficiency of nearly 1400 PPD/watt, which is twice that of the AMD Radeon 7970 that was the previous workhorse in my desktop.  Undoubtedly the higher-end GTX 10XX series graphics cards such as the 1070 offer even more performance, albeit at higher power consumption and much higher entry price than the $130 for the GTX 1050. For this price, you can’t go wrong if your goal is to do the most science for the least amount of power consumed while sticking to a tight budget.

Folding on Graphics Cards

After focusing on CPUs only, it’s time to turn up the performance and discuss graphics card folding.  Today’s graphics cards are massively parallel, and lend themselves to molecular dynamics problems more so than general CPUs.  Folding@home has benefited from developing projects to run on graphics cards.  Gamers, naturally competitive creatures by nature, have taken the F@H stats by storm.  Except for a few incredibly complex multi-CPU systems, high-end folding rigs are almost entirely GPU based at this point in time.

GPUs offer increased performance and efficiency compared to CPUs.  In order to offer a fair comparison to the CPU hardware tested on this blog (all very old by 2017 standards), I loaded up F@H on my 5-year-old Sapphire RADEON HD 7970 to see how it compares to the elderly hardware I’ve tested so far.  The results speak for themselves (production plot courtesy of http://folding.extremeoverclocking.com/)

7970 Graph

GPU vs CPU Table

I ran F@H for multiple days in order to get some good averaging on the results.  As you can see from the production graph, some projects return more points than others, but at an average PPD of nearly 150K, the Radeon 7970 destroys the CPU-based competition. More importantly, it does so with much more efficiency than processors.

Performance Summary: GPU vs CPU

Performance 7970 Efficiency Summary: GPU vs CPU

Efficiency 7970

Conclusion

Even though more total power was consumed, running Folding@home on a high-end graphics card results in much more science for a given amount of power.  Next time, we’ll put a modern mid-range graphics card to the test to see how far things have come in the past 5 years…

F@H Efficiency: Overclock or Undervolt?

Efficiency Tweaking

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

AMD Phenom II X6: Efficiency Improved through Undervolting

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

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

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

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

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

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

Conclusion

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

Blog Vision Going Forward / Join Our Team!

Nuclear Wessels (Team 54345) Folding Production Graph Feb 2014--Please Join to Make us Faster!

Nuclear Wessels (Team 54345) Folding Production Graph Feb 2014–Please Join to Make us Faster!

I feel I’ve now covered enough introductory material, so it’s time to start writing the type of content that will end up being 90% of this blog.  The next few articles will investigate the F@H performance and efficiency of various computer configurations.  Ultimately, I hope to have a page with charts of PPD/Watt for various CPUs and GPUs.  That will probably take a long time (it is a never ending project as long as F@H is operational).  But, I don’t plan to go back in time and look at every processor or graphics card since Fred Flintstone was folding.  I’ll stick to new parts that are available now.  In the next post I’ll do a quick comparison of new vs. old hardware, which should be enough to dissuade people from claiming efficiency with processors and graphics cards from two years ago.  I’ll also do a quick comparison of the uniprocessor CPU client vs the multicore SMP client to show that, for a 24/7 folding rig, it just doesn’t make sense to restrict yourself to 1 CPU core.  

Also, I’d like to invite anyone who wants to join a Folding@Home team to join ours.  We’re called Nuclear Wessels, and we are team # 54345.  We’re currently ranked 464 out of almost 220,000 teams, but the competition is crazy at the top of the Leaderboards, and Nuclear Wessels needs more power to break into the top 300!