Category Archives: Uncategorized

AMD Ryzen 9 3950X Folding@Home Review: Part 1: PPD vs # of Threads

Welcome back everyone. Over the last month, I’ve been experimenting with my new Folding@Home benchmark machine to see how effectively AMD’s flagship Ryzen processor (Ryzen 9 3950X) can fight diseases such as COVID-19, Cancer, and Alzheimer’s. I’ve been running Folding@Home, a charitable distributed computing project, which provides scientists with valuable computing resources to study diseases and learn how to combat them.

This blog is typically focused on energy efficiency, where I try to show how to do the most science for the least amount of power consumption possible. In this post, I’m stepping away from that (at least for now) in order to understand something much simpler: how does the Folding@Home CPU client scale with # of processor threads?

I’d previously investigated Folding@Home performance and efficiency vs. # of CPU cores on an old Intel Q6600. I’ve also done a few CPU articles on AMD’s venerable Phenom II X6 1000T and my previous processor, the AMD FX-8320e. These CPU articles were few and far-between however, as I typically focus on using graphics cards (GPUs). The reason is twofold. Historically, graphics cards have produced many more points per day (PPD) for a given amount of power, thanks to their massively parallel architecture, which is well-suited for running single precision molecular dynamics problems such as those used by Folding@Home. Also, graphics cards are much easier to swap out, so it was relatively easy to make a large database of GPU performance and efficiency.

Still, CPU folding is just as important, because there are certain classes of problems that can only be efficiently computed on the CPU. Folding@Home, while originally a project that ran exclusively on CPUs, obtains the bulk of its computational power from GPU donors these days. However, the CPU folders sill play a key part, running work units that cannot be solved on GPUs, thus providing a complete picture of the molecular dynamics.

In my last article, I highlighted the need for me to build a new benchmark machine for testing out GPUs, since my old rig would soon become a bottleneck and slow the GPUs down (thus potentially affecting any comparison plots I make). Now that this Ryzen-based 16-core monster of a desktop is complete, I figured I’d revisit CPU folding once more to see just what a modern enthusiast-class processor like the $749 Ryzen 9 3950X is capable of. For this first part of a multi-part review, I am simply looking at the preliminary results from running Folding@Home on the CPU. Instead of running with the default thread settings, I manually set up the client, examining just how performance results scale from the 1 to 32 available threads on the Ryzen 9 3950x.

Test Setup

Testing was performed in Windows 10 Home, using the latest Folding@Home client (7.6.13). Points Per Day were estimated from the client window for each setting of # of CPU threads. These instantaneous estimates have a lot of variability, so future testing will investigate the effect of averaging (running multiple tests at each setting) on the results.

Benchmark Machine Hardware:

Case Raidmax Sagitta (2006)
Power Supply Seasonic Prime 750 Titanium
Fresh Air 2 x 120 mm Enermax Front Intake
Rear Exhaust 1 x 120 mm Scythe Gentile Typhoon
Side Exhaust 1 x 80 mm Noctua
Top Exhaust 1 x 120 mm (Seasonic PSU)
CPU Cooler Noctua NH-D15 SE AM4
Thermal Paste Arctic MX-4
CPU AMD Ryzen 9 3950X 16 Core 32 Thread (105W TDP)
Motherboard ASUS Prime X570-P Socket AM4
Memory 32 GB (4 x 8 GB) Corsair Vengeance LPX DDR4 3600 MHz
GPU Zotac Nvidia GeForce 1650
OS Drive Samsung 970 Evo Plus 512 GB NVME SSD
Storage #1 Samsung 860 Evo 2 TB SSD
Storage #2 Western Digital Blue 256 GB NVME SSD (for Linux)
Optical Samsung SH-B123L Blu-Ray Drive
OS Windows 10 Home, Ubuntu Linux (on 2nd NVME)

Processor Settings:

The AMD Ryzen 9 3950x is a beast. With 16 cores and 32 threads, it has a nominal power consumption of 105 watts, but can easily double that when overclocked. With the factory Core Performance Boost (CPB) enabled, the processor will routinely draw 150+ watts when loaded due to the individual cores turboing as high as 4.7 GHz, up from the 3.5 GHz base clock. Under heavy multi-threaded work loads, the processor supports an all-core overclock of up to 4.3 GHz, assuming sufficient cooling and motherboard power delivery.

This automatic core turbo behavior is problematic for creating a plot of folding at home performance (PPD) vs # of threads, since for lightly threaded loads, the processor will scale up individual cores to much higher speeds. In order to make an apples to apples comparison, I disabled CPB, so that all CPU cores run at the base speed of 3.5 GHz when loaded. In future testing, I will perform this study with CPB on in order to see the effect of the factory automatic overclocking.

A note about Cores vs. Threads

Like many Intel processors with Hyper-Threading, AMD supports running multiple code execution strings (known as threads) on one CPU core. The Simultaneous Multi-Threading (SMT) on the Ryzen 9 3950x is simply AMD’s term for the same thing: a doubling of certain parts within each processor core (or sometimes the virtualization of multiple threads within one CPU core) to allow multiple thread execution (two threads per core, in this case). The historical problem with both Hyper-Threading and SMT is that it does not actually double a CPU core’s capacity to perform complex floating point mathematics, since there is only one FPU per CPU core. SMT and Hyperthreading work best when there is one large job hogging a core, and the smaller job can execute in the remaining part of the core as a second thread. Two equally intensive threads can end up competing for resuorses within a core, making the SMT-enabled processor actually slower. For example: https://www.techspot.com/review/1882-ryzen-9-smt-on-vs-off/

For the purposes of this article, I left SMT on in order to make the coolest plot possible (1-32 threads!). However, I suspect that SMT might actually hurt Folding@Home performance, for the reasons mentioned above. Thus in future testing, I will also try disabling this to see the effect.

Preliminary Results: PPD vs # Threads on Ryzen 9 3950x

So, to summarize the caveats, this test was performed once under each test condition (# of threads), so there are 32 data points for 32 threads. SMT was on (so Folding@Home can run two threads on one CPU core). CPB was off (all cores set to 3.5 GHz).

The figure below shows the results. As you can see, there is a general trend of increasing performance with # of threads, up to around the halfway point. Then, the trend appears to get messy, although by the end of the plot, it is clear that the higher thread counts realize a higher PPD.

Ryzen 9 3950X PPD vs Thread Count 1

Observations

It is clear that, at least initially, adding threads to the solution makes a fairly linear improvement in points per day. Eventually, however, the CPU cores are likely becoming saturated, and more of the work is being executed in via SMT. Due to the significant work unit variability in Folding@Home (as much as 10-20% between molecules), these results should be taken with a grain of salt. I am currently re-running all of these tests, so that I can show a plot of average PPD vs. # of Threads. I am also logging power using my watt meter, so that we can make wall power consumption and efficiency plots.

Conclusions

Seeing a processor produce nearly half a million points per day in Folding@Home was insane! My previous testing with old 4, 6, and 8-core processors was lucky to show numbers over 20K PPD. In general, allowing Folding@Home to use more processor threads increases performance, but there is significant additional work needed to verify a statistical trend. Stay tuned for Part II (averaging).

P.S.

Man, that’s a lot of cores! You’d better be scared, COVID-19…I’m coming for you!

Cores!

So Many Cores!

Ryzen Update / Consider Supporting my Writing (somewhat off topic)

For those following along, it might be a bit until I get the next article published on testing out CPU folding on the Ryzen 3950x. I’m doing a # of threads vs. PPD and PPD/Watt efficiency plot, but with 32 threads this will take a while. I’m running a minimum of 3 work units per test, to try and minimize the work unit variation. I also want to do this with and without hyper threading (SMT) on, to see the effect. So far, I’m seeing PPD of up to 400K, which is insane for a CPU!

In the mean time, I’m working on my other writing projects. For anyone who likes science-fiction, you can check out my free online web novel The Chronicles of the Starfighters over on Royal Road. You can also check out my books Sagitta and Hrain on Amazon. These are pulpy, science-fiction adventure stories (think Star Trek or Star Wars) with a teenage and / or alien protagonists. It’s a far cry from the non-fiction posts of this blog, but it is equally nerdy, I promise.

For those interested in supporting this blog, giving Chronicles a review or rating on Royal Road would be a great way to help me gain traction (honest reviews only please, I’m looking for feedback on what I can improve as well as what I do well).

Starfighter Promo

You can read more about my sci-fi writing projets at my other blog: https://starfightersf.com/

Finally, since I don’t have a Patreon (yet), anyone interested in donating to help me fund more graphics card purchases can consider buying a kindle book. Even if you don’t enjoy science fiction, you can gift copies to someone who does!

Kindle book links:

Sagitta (story about a human warp ship that changes the course of an alien war)

Hrain (story about a young telepathic alien who beats people up and stuff)

Thanks! And hopefully I’ll have that Ryzen data up soon.

-Chris

Folding@Home Computer Horsepower Quadruples from COVID-19 Donors, but Now There Are No More Work Units!

Seriously, where did all the GPU Work Units Go?

Folding@Home, a distributed computing project started by Stanford University, is one place where people can donate computer time to help scientists cure disease. The project recently announced that new models were being developed to help researchers understand the latest coronavirus that is wreaking havoc across the globe.

https://www.theverge.com/2020/3/2/21161131/folding-home-volunteers-researchers-coronavirus

As a result, tons of new people have downloaded the F@H client and started chewing away at molecular dynamics problems. This is great, because it means a lot more geeky computational charity is happening. Just check out the graph below. X-axis is time. Y-axis is performance. When people realized they could join one of the world’s largest supercomputers to fight COVID-19, BOOM! Instant quadrupling of computer horsepower. That’s sweet.

FAH Gone to Plaid

But, as it turns out, there’s a twist. All those new computers ate up all of the existing work units, so now the F@H Consortium’s servers have run dry.

That’s right, as of the writing of this post, it is impossible to get GPU work units, just like it is impossible to get toilet paper, hand sanitizer, and anything made by Clorox. For people who incidentally use Folding@Home to heat their house in the winter, this is really annoying!

Here’s a screen shot of one of my seven empty Folding@Home computers…just like those shelves at the supermarket, there’s nothing here.

No Work COVID-19

The Folding@Home forums are rife with people noting this problem as of 3/14/2020.

COVID-19 Issues

Here’s the official announcement:

https://foldingforum.org/viewtopic.php?f=24&t=32424

Thankfully, just as my local supermarket recently announced a new shipment of toilet paper, F@H has announced that more COVID-19 projects should be hitting the streets. So, here’s to hoping these come my way soon, so that I can fight this virus with my computer as well as with my hand sanitizer (yes, I have some, but I’m not telling you where).

Update: 3/14/2020 at 9:00 PM

I’ve got heat in my bedroom again from my small 1070 Ti based space heater. So, I went downstairs and found that my dual GPU benchmark machine is up and running with fresh work on both the 1080 Ti and the 980 Ti. Here’s to hoping this makes a difference, and that the scientists behind the project can benefit from all this added computational capacity (and keep the WUs flowing!).

Thank you to all the donors (veterans and new COVID-19 donors alike) and the F@H researchers and volunteers!

 

GTX 460 Graphics Card Review: Is Folding on Ancient Hardware Worth It?

Recently, I picked up an old Core 2 duo build on Ebay for $25 + shipping. It was missing some pieces (Graphics card, drives, etc), but it was a good deal, especially for the all-metal Antec P182 case and included Corsair PSU + Antec 3-speed case fans. So, I figured what the heck, let’s see if this vintage rig can fold!

Antec 775 Purchase

To complement this old Socket 775 build, I picked up a well loved EVGA GeForce GTX 460 on eBay for a grand total of $26.85. It should be noted that this generation of Nvidia graphics cards (based on the Fermi architecture from back in 2010) is the oldest GPU hardware that is still supported by Stanford. It will be interesting to see how much science one of these old cards can do.

GTX 460 Purchase

I supplied a dusty Western Digital 640 Black Hard Drive that I had kicking around, along with a TP Link USB wireless adapter (about $7 on Amazon). The Operating System was free (go Linux!). So, for under $100 I had this setup:

  • Case: Antec P182 Steel ATX
  • PSU: Corsair HX 520
  • Processor: Intel Core2duo E8300
  • Motherboard: EVGA nForce 680i SLI
  • Ram: 2 x 2 GB DDR2 6400 (800 MHz)
  • HDD: Western Digital Black 640GB
  • GPU: EVGA GeForce GTX 460
  • Operating System: Ubuntu Linux 18.04
  • Folding@Home Client: V7

I fired up folding, and after some fiddling I got it running nice and stable. The first thing I noticed was that the power draw was higher than I had expected. Measured at the wall, this vintage folding rig was consuming a whopping 220 Watts! That’s a good deal more than the 185 watts that my main computer draws when folding on a modern GTX 1060. Some of this is due to differences in hardware configuration between the two boxes, but one thing to note is that the older GTX 460 has a TDP of 160 watts, whereas the GTX 1060 has a TDP of only 120 Watts.

Here’s a quick comparison of the GTX 460 vs the GTX 1060. At the time of their release, both of these cards were Nvidia’s baseline GTX model, offering serious gaming performance for a better price than the more aggressive GTX -70 and -80-series variants. I threw a GTX 1080 into the table for good measure.

GTX 460 Spec Comparison

GTX 460 Specification Comparison

The key takeaways here are that six years later, the equivalent graphics card to the GTX 460 was over three and a half times faster while using forty watts less power.

Power Consumption

I typically don’t report power consumption directly, because I’m more interested in optimizing efficiency (doing more work for less power). However, in this case, there is an interesting point to be made by looking at the wattage numbers directly. Namely, the GTX 460 (a mid-range card) uses almost as much power as a modern high-end GTX 1080, and uses seriously more power than the modern GTX 1060 mid-range card. Note: these power consumption numbers must be taken with a grain of salt, because the GTX 460 was installed in a different host system (the Core2 Duo rig) as the other cards, but the resutls are still telling. This is also consistent with the advertised TDP of the GTX 460, which is 40 watts higher than the GTX 1060.

GTX 460 Power Consumption (Wall)

Total System Power Consumption

Folding@Home Results

Folding on the old GTX 460 produced a rough average of 20,000 points per day, with the normal +/- 10% variation in production seen between work units. Back in 2006 when I was making a few hundred PPD on an old Athlon 64 X2 CPU, this would have been a huge amount of points! Nowadays, this is not so impressive. As I mentioned before, the power consumption at the wall for this system was 220 Watts. This yields an efficiency of 20,000 PPD / 220 Watts = 90 PPD/Watt.

Based off the relative performance, one would think the six-year newer GTX 1060 would produce somewhere between 3 and 4 times as many PPD as the older 460 card. This would mean roughly 60-80K PPD. However, my GTX 1060 frequently produces over 300K PPD. This is due to Stanford’s Quick Return Bonus, which essentially rewards donors for doing science quickly. You can read more about this incentive-based points system at Stanford’s website. The gist is, the faster you return a work unit to the scientists, the sooner they can get to developing cures for diseases. Thus, they award you more points for fast work. As the performance plot below shows, this quick return bonus really adds up, so that someone doing 3-4 times more (GTX 1060 vs. GTX 460 linear benchmark performance) results in 15 times more F@H performance.

GTX 460 Performance and Efficiency

Old vs. New Graphics Card Comparison: Folding@Home Efficiency and PPD

This being a blog about energy-conscious computing, I’d be remiss if I didn’t point out just how inefficient the ancient GTX 460 is compared to the newer cards. Due to the relatively high power consumption for a midrange card, the GTX 460 is eighteen times less efficient than the GTX 1060, and a whopping thirty three times less efficient than the GTX 1080.

Conclusion

Stanford eventually drops support for old hardware (anyone remember PS3 folding?), and it might not be long before they do the same for Fermi-based GPUs. Compared with relatively modern GPUs, the GTX 460 just doesn’t stack up in 2020. Now that the 10-series cards are almost four years old, you can often get GTX 1060s for less than $200 on eBay, so if you can afford to build a folding rig around one of these cards, it will be 18 times more efficient and make 15 times more points.

Still, I only paid about $100 total to build this vintage folding@home rig for this experiment. One could argue that putting old hardware to use like this keeps it out of landfills and still does some good work. Additionally, if you ignore bonus points and look at pure science done, the GTX 460 is “only” about 4 times slower than its modern equivalent.

Ultimately, for the sake of the environment, I can’t recommend folding on graphics cards that are many years out of date, unless you plan on using the machine as a space heater to offset heating costs in the winter. More on that later…

Addendum

Since doing the initial testing and outline for this article, I picked up a GTX 480 and a few GTX 980 Ti cards. Here are some updated plots showing these cards added to the mix. The GTX 480 was tested in the Core2 build, and the GTX 980 Ti in my standard benchmark rig (AMD FX-based Socket AM3 system).

Various GPU Power Consumption

GTX 980 and 480 Performance

GTX 980 and 480 Efficiency

I think the conclusion holds: even though the GTX 480 is slightly faster and more efficient than it’s little brother, it is still leaps and bounds worse than the more modern cards. The 980 Ti, being a top-tier card from a few generations back, holds its own nicely, and is almost as efficient as a GTX 1060. I’d say that the 980 Ti is still a relatively efficient card to use in 2020 if you can get one for cheap enough.

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!

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!

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…