Category Archives: General

New Folding@Home Benchmark Machine: It’s RYZEN TIME!

Folding@Home, the distributed computing project that fights diseases such as COVID-19 and cancer, has hit an all-time high in popularity. I’m stunned to find that my blog is now getting more views every day than it did every month last year. With that said, this is a perfect opportunity to reach out and see if all the new donors are interested in tuning their computers for efficiency, to save a little on power, lighten the burden on your wallet, and hopefully produce nearly the same amount of science. If this sounds interesting to you, let me know in the comments below!

In my last post, I noted that the latest generation of graphics cards are starting to push the limits of what my primary GPU Folding@Home benchmark rig can do. That computer is based on an 11-year-old chipset (AMD 880), and only supports PCI-Express 2.0. In order for me to keep testing modern fast graphics cards in Windows 10, I wanted to make sure that PCI-Express slot bandwidth wasn’t going to artificially bottleneck me.

So, without further ado, let me present the new, re-built Folding@Home rig, SAGITTA:

Sagitta Desktop

I’ve (re)created a monster!

This build leverages the Raidmax Sagitta case that I’ve had since 2006. This machine has hosted multiple builds (Pentium D 805, Core 2 Duo e8600, Core 2 Quad Q6600, Phenom II X6 1100T, and the most recent FX-8320e Bulldozer). There have been too many graphics cards to count, but the latest one (Nvidia GTX 1650 by Zotac) was carried over for some continuity testing. The case fans and power supply (initially) were also the same since the previous FX build (they aren’t the same ones from back in 2006…those got loud and died long ago). I also kept my Blu-Ray drive and 3.5 inch card reader. That’s where the similarities end. Here is a specs comparison:

Sagitta Rebuild Benchmark Machine Specs

  • Note I ended up updating the power supply to the one shown in the table. More on that below…

System Power Consumption

Initially, the power consumption at idle of the new Ryzen 9 build, measured with my P3 Kill A Watt Meter, was 86 watts. The power consumption while running GPU Folding was 170 watts (and the all-core CPU folding was over 250 watts, but that’s another article entirely).

Using the same Nvidia GeForce GTX 1650 graphics card, these idle and GPU folding power numbers were unfortunately higher than the old benchmark machine, which came in at 70 watts idle and 145 watts load. This is likely due to the overkill hardware that I put into the new rig (X570 motherboards alone are known to draw twice the power of a more normal board). The system’s power consumption difference of 25 watts while folding was especially problematic for my efficiency testing, since new plots compared to graphics cards tested on the old benchmark machine would not be comparable.

To solve this, I could either:

A: Use a 25 watt offset to scale the new GPU F@H efficiency plots

B: Do nothing and just have less accurate efficiency comparisons to previous tests

C: Reduce the power consumption of the new build so that it matches the old one

This being a blog about energy efficiency, I decided to go with Option C, since that’s the one that actually helps the environment. Lets see if we can trim the fat off of this beast of a computer!

Efficiency Boost #1: Power Supply Upgrade

The first thing I tried was to upgrade the power supply. As noted here, the power supply’s efficiency rating is a great place to start when building an energy efficient machine. My old Seasonic X-650 is a very good power supply, and caries an 80+ Gold rating. Still, things have come a long way, and switching to an 80+ Titanium PSU can gain a few efficiency percentage points, especially at low loads.

80+ Table

80+ Efficiency Table

With that 3-5% efficiency boost in mind, I picked up a new Seasonic 750 Watt Prime 80+ Titanium modular power supply. At $200, this PSU isn’t cheap, but it provides a noticeable efficiency improvement at both idle and load. Other nice features were the additional 100 watts of capacity, and the fact that it supported my new motherboard’s dual pin (8 + 4) CPU aux power connection. That extra 4-pin isn’t required to make the X570 board work, but it does allow for more overclocking headroom.

Disclaimer: Before we get into it, I should note that these power readings are “eyeball” readings, taken by glancing at the watt meter and trying to judge the average usage. The actual number jumps around a bit (even at idle) as the computer executes various background tasks. I’d say the measurement precision on any eyeball watt meter readings is +/- 5 watts, so take the below with a grain of salt. These are very small efficiency improvements that are difficult to measure, and your mileage may vary. 

After upgrading the power supply, idle power dropped an impressive 10 watts, from 86 watts to 76. This is an awesome 11% efficiency improvement. This might be due to the new 80+ Titanium power supply having an efficiency target at very low loads (90% efficiency at 10% load), whereas the old 80+ Gold spec did not have a low load efficiency requirement. Thus, even though I used a large 750 watt power supply, the machine can still remain relatively efficient at idle.

Under moderate load (GPU folding), the new 80+ titanium PSU provided a 4% efficiency improvement, dropping the power consumption from 170 watts to 163. This is more in line with expectations.

Efficiency Boost #2: Processor Underclock / Undervolt

Thanks to video gaming mentality, enthusiast-grade desktop processors and motherboards are tuned out of the box for performance. We’re talking about blistering fast, competition-crushing benchmark scores. For most computing tasks (such as running Folding@Home on a graphics card), this aggressive CPU behavior is wasting electricity while offering no discernible performance benefit. Despite what my kid’s shirt says, we need to reel these power hungry CPUs in for maximum GPU folding efficiency.

Never Slow Down

Kai Says: Never Slow Down

One way to improve processor efficiency is to reduce the clock rate and associated voltage. I’d previously investigated this here. It takes exponentially more voltage to support high frequencies, so just by dropping the clock rate by 100 MHz or so, you can lower the voltage a bunch and save on power.

With the advent of processors that up-clock and up-volt themselves (as well as going in the other direction), manual tuning can be a bit more difficult. It’s far easier to first try the automatic settings, to see if some efficiency can be gained.

But wait, this is a GPU folding benchmark rig? Why does the CPU’s frequency and power settings matter?

For GPU folding with an Nvidia graphics card, one CPU core is fully loaded per GPU slot in order to “feed” the card. This is because Nvidia’s implementation of open CL support using a polling (checking) method. In order to keep the graphics card chugging along, the CPU constantly checks on the GPU to see if it needs any data. This polling loop is not efficient and burns unnecessary power. You can read more about it here: https://foldingforum.org/viewtopic.php?f=80&t=34023. In contrast, AMD’s method (interrupts) is a much more graceful implementation that doesn’t lock up a CPU core.

The constant polling loop drives modern gaming-oriented processors to clock up their cores unnecessarily. For the most part, the GPU does not need work at every waking moment. To save power, we can turn down the frequency, so that the CPU is not constantly knocking on the GPU’s metaphorical door.

To do this, I disabled AMD’s Core Performance Boost (CPB) in the AMD Overclocking section of the BIOS (same thing as Intel’s Turbo Boost). This caps the processor speed at the base maximum clock rate (3.5 GHz for the Ryzen 9 3950x), and also eliminates any high voltage values required to support the boost clocks.

Success! GPU folding total system power consumption is now much lower. With less superfluous power draw from the CPU, the wattage is much more comparable to the old Bulldozer rig.

Ryzen 9 3950x Power Reduction Table

It is interesting that idle power consumption came down as well. That wasn’t expected. When the computer isn’t doing anything, the CPU cores should be down-clocked / slept out. Perhaps my machine was doing something in the background during the earlier tests, thus throwing the results off. More investigation is needed.

GPU Benchmark Consistency Check

I fired up GPU folding on the Nvidia GeForce GTX 1650, a card that I have performance data for from my previous benchmark desktop. After monitoring it for a week, the Folding@Home Points Per Day performance was so similar to the previous results that I ended up using the same value (310K PPD) as the official estimate for the 1650’s production. This shows that the old benchmark rig was not a bottleneck for a budget card like the GeForce GTX 1650.

Using the updated system power consumption of nominally 140 watts (vs 145 watts of the previous benchmark machine), the efficiency plots (PPD/Watt) come out very nearly the same. I typically consider power measurements of + / – 5 watts to be within the measurement accuracy of my eyeball on the watt meter anyway, due to normal variations as the system runs. The good news is that even with this variation, it doesn’t change the conclusion of the figure (in terms of graphics card efficiency ranking).

GTX 1650 Efficiency on Ryzen 9

* Benchmark performed on updated Ryzen 9 build

Conclusion

I have a new 16-core beast of a benchmark machine. This computer wasn’t built exclusively for efficiency, but after a few tweaks, I was able to improve energy efficiency at low CPU loads (such as Windows Idle + GPU Folding).

For most of the graphics cards I have tested so far, the massive upgrade in system hardware will not likely affect performance or efficiency results. Very fast cards, such as the 1080 Ti, might benefit from the new benchmark rig’s faster hardware, especially that PCI-Express 4.0 x16 graphics card slot. Most importantly, future tests of blistering fast graphics cards (2080 Ti, 3080 Ti, etc) will probably not be limited by the benchmark machine’s background hardware.

Oh, I can also now encode my backup copies of my blu-ray movies at 40 fps in H.265 in Handbrake (old speed was 6.5 fps on the FX-8320e). That’s a nice bonus too.

Efficiency Note (for GPU Folding@Home Users)

Disabling the automatic processor frequency and voltage scaling (Turbo Boost / Core Performance Boost) didn’t have any effect on the PPD being generated by the graphics card. This makes sense; even relatively slow 2.0 GHz CPU cores are still fast enough to feed most GPUs, and my modern Ryzen 9 at 3.5 GHz is no bottleneck for feeding the 1650. By disabling CPB, I shaved 23 watts off of the system’s power consumption for literally no performance impact while running GPU folding. This is a 16 percent boost in PPD/Watt efficiency, for free!

This also dropped CPU temps from 70 degrees C to 55, and resulted in a lower CPU cooler fan speed / quieter machine. This should promote longevity of the hardware, and reduce how much my computer fights my air conditioning in the summer, thus having a compounding positive effect on my monthly electric bill.

Future Articles

  • Re-Test the 1080 Ti to see if a fast graphics card makes better use of the faster PCI-Express bus on the AM4 build
  • Investigate CPU folding efficiency on the Ryzen 9 3950x

 

Shout out to the helpers…Kai and Sam

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!

 

Is Folding@Home a Waste of Electricity?

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

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

Folding on Graphics Card Thermal

FLIR Thermal Cam – Folding@Home on Graphics Card

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

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

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

F@H Energy Equivalence

Potential Folding@Home Environmental Impact

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

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

It’s just not that easy to quantify.

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

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

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

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

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

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

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

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

Solar Panel in Basement

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

Where I’ve Been

So two years later and I’m finally posting.  Phew!  It was hard enough just finding time to write this.  The short of it is that life happened, and I just didn’t have the time to keep going with the blog.  Actually, I stopped folding as well, due to very high electricity costs in Connecticut (averaging about 18 cents per kWh, which is insane).

But now that our second child is a little less cranky, and now that we are out of that tiny apartment (we bought a house), I think I’m finally feeling settled enough to resume this blog.

Consider this a second kick-off.

As some of you have mentioned, the real computational power these days is in graphics cards. Actually, even when I was writing regularly two years ago, GPUs were the ticket to massive PPD and better efficiency.  The reason I wasn’t talking about them was because I felt it was important to start where F@H started and discuss CPUs.

Over the years I have folded on many graphics cards.  The list, as I recall it, goes as follows:

  • NVidia Geforce 8400 GS (PCI)
  • Nvidia Geforce 240 GT
  • AMD Radeon 3870
  • AMD Radeon 4870
  • AMD Radeon 5870
  • NVidia Geforce 460 GTX
  • AMD Radeon 7970 HD

You’re probably looking at this list and thinking, wow, those are some old GPU’s.  Well you’re right!  Originally I was going to write a blog post about each one of them, and include tuning info and lots of pictures.  Since I don’t have any of those GPUs anymore, with the exception of the 7970, that’s not going to happen.  Oh well…

The takeaway of all those articles though would have been this:  any of those GPUs (with the exception of the wimpy 8400) offered better performance and efficiency than the contemporary CPUs in the similar price range.  The higher end graphics cards (7970) offer significantly more points per day performance, and although power consumption is typically higher than a CPU-only folding rig, the performance  gains are exponential and efficiency is greater.  This is because the massively parallel architecture of today’s graphics cards offers tremendous floating point computational capability compared to central processors.

Going forward, I plan to take a look at new graphics cards (think 2017 vintage).  These cards generate anywhere from 100K PPD up to well over a million PPD.  But first I need to describe my new power meter, which will be the focus of the next post.

Second Intro

My Space Heaters in Winter!

My Space Heaters in Winter!

Welcome back to the fold!  After reading my first post, I realized that I did a terrible job at explaining what this blog is about.  So here’s a second attempt.

Folding at Home (F@H) is a distributed computing project hosted by Stanford University.  It’s basically computer charity, although it’s not an effort to save computers from viruses or from the Seven Slow Deaths of Obsolescence.  F@H uses your computer to turn electricity into scientific advancements that help people.

By running computer simulations for Stanford, we help researchers discover secrets about cancer and other diseases.  F@H is the largest distributed supercomputer in the world, with over 150 thousand active physical CPUs spread across the globe.  Many of the people who participate have a personal connection to the cause, such as a relative with Alzheimer’s, a Mad Cow, or cancer.  Others just wanted a unique way to give to charity.  No matter what the specific case is, F@H contributors often explain their reasoning as:

1.  I want to help people!

2. I am a huge geek!

For the typical contributor, the process is simple.  They download the uniprocessor client from Stanford, install it, and let it run it in the background on their computer during the day.  That’s great, however as with many things in life there is a downside.  If you have ever seriously researched Folding@Home, you might have come across a group of haters on the internet that bash the program for being an environmental disaster.  They say that Folding@Home is actually killing people & the planet at the same time.  With thousands upon thousands of computers eagerly juggling protein molecules, there are millions upon millions of watts being sucked out of wall outlets all over the world–outlets that connect back to power plants that burn fossil fuels and split atoms.  These people point out that even if your computer was going to be on anyway, the Folding application requires enough resources to keep it from ever going into a low-power idle state.  Thus, by striving to understand diseases, we may be dumping more junk into the environment that causes them in the first place.  And thus the dilemma: how can we fold to save lives without hurting the planet in which we live?  This brings me to the point of this blog: computational efficiency.   On these pages we will discuss how to contribute to F@H as efficiently and as cleanly as possible, as well as general environmental tips to help offset one’s F@H carbon footprint and ease that guilty conscience.  If you become a contributor who decides to implement the principles of this blog, you can defend your reasons along these lines:

1.  I want to help people

2.  I am the Overlord of Geeks.  Bow to me, geeklings!

3.  I love you, Earth!  Come here, let me give you a hug!  Or at least let me put away this big mallet I’ve been beating you with and replace it with a smaller tack hammer…

I expect this blog will ruffle some feathers.  If you are one of those people running the F@H uniprocessor client on your laptop 24/7, allow me to pause and glare at you through your monitor for a moment.  <glare> 😦 </glare>.  OK, done glaring.  First, thank you for supporting your fellow human beings.  Now, do yourself and your earth a favor and stop that F@H client right now!  I’m going to show you how to make a real difference in a way that won’t break the bank and won’t kill your puny laptop with excessive heat.  No longer will you settle for the 500, or even the 5000 Points Per Day (PPD) that you’re getting now.  We’re shooting for 100k PPD or more!  By not running F@H on old slow computers, making minimal use of the standard uniprocessor client, and finally building a dedicated F@H Rig, you can punch cancer in the face with a much bigger fist.  You’ll also cause less overall environmental impact for the amount of cancer curing being done.  Welcome to Green Folding@Home, home of  team Nuclear Wessels (Team ID 54345, the greenest Rigs in the fleet!).  I’m Chris (_QC_Paragon on the leader boards), and I’m going to show you the many ways to make a lean green Folding machine!

Me

-Chris

Green Folding@Home is Online

Icy Opteron 4184

Welcome to Green Folding

Hi everyone, and welcome to my blog.  I’m Chris, and if you haven’t guessed already, this is going to be blog about big fans :).  Well, big fans of F@H that is.  I’m assuming if you found your way here, you know what Folding@Home is.  If not, well, I’m not going to tell you what it is.  I’m too lazy for that.  But, if things go as planned, you will eventually figure it out by reading these posts.  Or, you could save yourself the mental anguish and just go read about this charitable venture at http://folding.stanford.edu/home/.   If it leaves you with a warm fuzzy feeling, please sign up and help!  The feeling just gets better, until after eight years of contributing you just might want to start a blog about what you have learned.

First post done!  Yay + Yawn.  And I haven’t even really told you anything yet.  This is probably due to it being 4:53 in the morning, and I am positively loopy with excitement.  I promise there will be a more sane post tomorrow at some point.  Until then, FOLD ON!