Tag Archives: Q6600

F@H Efficiency on Dell Inspiron 1545 Laptop

Laptops!  

When browsing internet forums looking for questions that people ask about F@H, I often see people asking if it is worth folding on laptops (note that I am talking about normal, battery-life optimized laptops, not Alienware gaming laptops / desktop replacements).  In general, the consensus from the community is that folding on laptops is a waste of time.  Well, that is true from a raw performance perspective.  Laptops, tablets, and other mobile devices are not the way to rise to the top of the Folding at Home leader boards.  They’re just too slow, due to the reduced clock speeds and voltages employed to maximize battery life.

But wait, didn’t you say that low voltage is good for efficiency?

I did, in the last article.  By undervolting and slightly underclocking the Phenom II X6 in a desktop computer, I was able to get close to 90 PPD/Watt while still doing an impressive twelve thousand PPD.

However, this raised the interesting question of what would happen if someone tried to fold on a computer that was optimized for low voltage, such as a laptop.  Lets find out!

Dell Inspiron 1545

Specs:

  • Intel T9600 Core 2 Duo
  • 8 GB DDR2 Ram
  • 250 GB spinning disk style HDD (5400 RPM, slow as molasses)
  • Intel Integrated HD Graphics (horrible for gaming, great for not using much extra electricity)
  • LCD Off during test  to reduce power

I did this test on my Dell Inspiron 1545, because it is what I had lying around.  It’s an older laptop that originally shipped with a slow socket P Intel Pentium dual core.  This 2.1 GHz chip was going to be so slow at folding that I decided to splurge and pick up a 2.8 GHz T9600 Core 2 Duo from Ebay for 25 bucks (can you believe this processor used to cost $400)?  This high end laptop processor has the same 35 watt TDP as the Pentium it is replacing, but has 6 times the total cache.  This is a dual core part that is roughly similar in architecture to the Q6600 I tested earlier, so one would expect the PPD and the efficiency to be close to the Q6600 when running on only 2 cores (albeit a bit higher due to the T9600’s higher clock speed).  I didn’t bother doing a test with the old laptop processor, because it would have been pretty bad (same power consumption but much slower).

After upgrading the processor (rather easy on this model of laptop, since there is a rear access panel that lets you get at everything), I ran this test in Windows 7 using the V7 client.  My computer picked up a nice A4 work unit and started munching away.  I made sure to use my passkey to ensure I get the quick return bonus.

Results:

The Intel T9600 laptop processor produced slightly more PPD than the similar Q6600 desktop processor when running on 2 cores (2235 PPD vs 1960 PPD). This is a decent production rate for a dual core, but it pales in comparison to the 6000K PPD of the Q6600 running with all 4 cores, or newer processors such as the AMD 1100T (over 12K PPD).

However, from an efficiency standpoint, the T9600 Core2 Duo blows away the desktop Core2 Quad by a lot, as seen in the chart and graph below.

Intel T9600 Folding@Home Efficiency

Intel T9600 Folding@Home Efficiency

Intel T9600 Folding@Home Efficiency vs. Intel Desktop Processors

Intel T9600 Folding@Home Efficiency vs. Desktop Processors

Conclusion

So, the people who say that laptops are slow are correct.  Compared to all the crazy desktop processors out there, a little dual core in a laptop isn’t going to do very many points per day.  Even modern quad cores laptops are fairly tame compared to their desktop brethren.  However, the efficiency numbers tell a different story.

Because everything from the motherboard, video card, audio circuit, hard drive, and processor are optimized for low voltage, the total system power consumption was only 39 watts (with the lid closed).  This meant that the 2235 PPD was enough to earn an efficiency score of 57.29 PPD/Watt.  This number beats all of the efficiency numbers from the most similar desktop processor tested so far (Q6600), even when the Q6600 is using all four cores.

So, laptops can be efficient F@H computers, even though they are not good at raw PPD production.  It should also be noted that during this experiment the little T9600 processor heated up to a whopping 67 degrees C. That’s really warm compared to the 40 degrees Celsius the Q6600 runs at in the desktop.  Over time, that heat load would probably break my poor laptop and give me an excuse to get that Alienware I’ve been wanting.  

PPD/Watt Shootout: Uniprocessor Client is a Bad Idea

My Gaming / Folding computer with Q6600 / GTX 460 Installed

My Gaming / Folding computer with Q6600 / GTX 460 Installed

Since the dawn of Folding@Home, Stanford’s single-threaded CPU client known as “uniprocessor” has been the standard choice for stable folding@home installations.  For people who don’t want to tinker with many settings, and for people who don’t plan on running 24/7, this has been a good choice of clients because it allows a small science contribution to be done without very much hassle.  It’s a fairly invisible program that runs in the background and doesn’t spin up all your computer’s fans and heat up your room.  But, is it really efficient?  

The question, more specifically targeted for folding freaks reading this blog, is this:  Does the uniprocessor client make sense for an efficient 24/7 folding@home rig?  My answer:  a resounding NO!  Kill that process immediately!

A basic Google search on this will show that you can get vastly more points per day running the multicore client (SMP), a dedicated graphics card client (GPU), or both.  Just type “PPD Uniprocessor SMP Folding” into Google and read for about 20 minutes and you’ll get the idea.  I’m too lazy to point to any specific threads (no pun intended), but the various forum discussions reveal that the uniprocessor client is slower than slow.  This should not be surprising.  One CPU core is slower than two, which is slower than three!  Yay, math!

Also, Stanford’s point reward system isn’t linear but exponential.  If you return a work unit twice as fast, you get more than twice as many points as a reward, because prompt results are very valuable in the scientific world.  This bonus is known as the Quick Return Bonus, and it is available to users running with a passkey (a long auto-generated password that proves you are who you say you are to Stanford’s servers).  I won’t regurgitate all that info on passkeys and points here, because if you are reading this site then you most likely know it already.  If not, start by downloading Stanford’s latest all-in-one client known as Client V7.  Make sure you set yourself up with a username as well as a passkey, in case you didn’t have one.  Once you return 10 successful work units using your passkey, you can get the extra QRB points.  For the record, this is the setup I am using for this blog at the moment: V7 Client Version 7.3.6, running with passkey.

Unlike the older 6.x client interfaces, the new V7 client lets you pick the specific work package type you want to do within one program.  “Uniprocessor” is no longer a separate installation, but is selectable by adding a CPU slot within the V7 client and telling it how many threads to run.  V7 then downloads the correct work unit to munch on.

I thought I was talking efficiency!  Well, to that end, what we want to do is maximize the F@H output relative to the input.  We want to make as many Points per Day while drawing the fewest watts from the wall as possible.  It should be clear by now where this is going (I hope).  Because Stanford’s points system heavily favors the fast return of work units, it is often the case that the PPD/Watt increases as more and more CPU cores or GPU shaders are engaged, even though the resulting power draw of the computer increases.

Limiting ourselves to CPU-only folding for the moment, let’s have a look at what one of my Folding@Home rigs can do.  It’s Specs Time (Yay SPECS!). Here are the specs of my beloved gaming computer, known as Sagitta (outdated picture was up at the top).

  • Intel Q6600 Quad Core CPU @ 2.4 GHz
  • Gigabyte AMD Radeon HD 7870 Gigahertz Edition
  • 8 GB Kingston DDR2-800 Ram
  • Gigabyte 965-P S3 motherboard
  • Seasonic X-650 80+ Gold PSU
  • 2 x 500 GB Western Digital HDDs RAID-1
  • 2 x 120 MM Intake Fans
  • 1 x 120 MM Exhaust Fan
  • 1 x 80 MM Exhaust Fan
  • Arctic Cooling Freezer 7 CPU Cooler
  • Generic PCI Slot centrifugal exhaust fan
Ancient Pic of Sagitta (2006 Vintage).  I really need to take a new pic of the current configuration.

Ancient Pic of Sagitta (2006 Vintage). I really need to take a new pic of the current configuration.

You’ll probably say right away that this system, except for the graphics card, is pretty out of date for 2014, but for relative A to B comparisons within the V7 client this doesn’t matter.  For new I7 CPUs, the relative performance and efficiency differences seen by increasing the number of CPU cores for Folding reveals the same trend as will be shown here.  I’ll start by just looking at the 1-core option (uniprocessor) vs a dual-core F@H solve.

Uniprocessor Is Slow

As you can see, switching to a 2-CPU solve within the V7 client yields almost twice as many PPD (12.11 vs 6.82).  And, this isn’t even a fair comparison, because the dual-core work unit I received was one of the older A3 cores, which tend to produce less PPD than the A4 work units.

In conclusion, if everyone who is out there running the uniprocessor client switched to a dual-core client, FOLDING AT HOME WOULD BECOME TWICE AS EFFICIENT!  I can’t scream this loud enough.  Part of the reason for this is because it doesn’t take many more watts to feed another core in a computer that is already fired up and folding.  In the above example, we really started getting twice the amount of work done for only 13 more watts of power consumed.  THIS IS AWESOME, and it is just the beginning.  In the next article, I’ll look at the efficiency of 3 and 4 CPU Folding on the Q6600, as well as 6-CPU folding on my other computer, which is powered by a newer processor (AMD Phenom II X6 1100T). I’ll then move on to dual-CPU systems (non BIGADV at this point for those of you who know what this means, but we will get there too), and to graphics cards.  If you think 12 PPD/Watt is good, just wait until you read the next article!

Until next time…

-C