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F@H Core 16: Taking AMD GPU Folding to the Next Level?

by Michael "SKYMTL" Hoenig     |     April 12, 2011


Some of you may remember when Folding@Home was still in its infancy, the first GPU client was introduced. Believe it or not that first (and even second) GPU client only supported ATI graphics cards with compatibility for NVIDIA’s CUDA being added much later.

Unfortunately, due to the Brooke programming language used in previous generations of AMD GPUs, they consistently took a back seat to NVIDIA’s CUDA supporting clients. GeForce graphics cards soon became the de facto kings of the Folding world but with the introduction of AMD graphics cores which support OpenCL, things may be about to change.

A few weeks ago, Stanford introduced the new Core 16 Project 11293 work units which are specifically tailored towards OpenCL-supporting AMD graphics cards and are available through the GPU3 V7 Client open beta program. In theory, this new project should represent just the tip of the iceberg when it comes to the future of AMD’s OpenCL GPU Folding@Home performance but being supported by a beta client means there are a few small hoops to jump through. As such, the any GPUs prior to the HD 5000 series can't process these new WUs and there is a limitation whereby the AMD video decoder can’t be used alongside this new client.

In this short article, we take this new client and its associated Core 16 project out for a spin while showing some comparative results between past and present WUs. By no means is this meant as an end-all for AMD Folding since the Stanford team is just getting started with OpenCL support but it should give you a good idea about where things are going.


The beta V7 GPU3 client isn’t something many of you will be accustomed to since it discards the usual interface for a design that’s a bit more icon-filled. On the positive side it brings all of the console commands to a user interface that’s user friendly but for many F@H users, certain commands can’t be utilized like they used to. Most importantly, this applies to the important –advmethods flag that must be used to receive AMD’s new P11293 WUs.

In order to actually start receiving these new Work Units, before the first project is downloaded (we recommend disabling network access until this is done) ensure Expert is selected for the client, click the Configure button and navigate to the Slots tab. Here you will need to select the appropriate GPU (in our case gpu-0) and use the Edit button to open an advanced configuration dialog box. In the last section, you will Add an new slot with a name of “client-type” and a value of “advanced” as in the picture above. This is used in place of the –advmethods flag and will ensure that you can begin downloading the new AMD WUs. However, since the new Core 16 projects seem to be in short supply, you may not receive one right away.

One of the most important things to remember here is that since their 11.2 release, AMD has been including their APP SDK within their standard driver installation package. This means once installation is complete, any HD 5000-series and prior GPU should be ready to begin folding. Just make sure you download and install the necessary APP components from within the chosen Catalyst package.

So now that we’ve done that, let’s jump straight to performance numbers. We used a very basic system with a stock i5 750 and 4GB of memory for these results and took the average of four completed WUs for each average result.


Before we go on it should be noted that our older Core 11 results were really all over the place so averages were a bit hard to glean from the numbers received. With that being said, with or without absolutely spot-on PPD numbers for the older WUs the results speak for themselves: the beta OpenCL project has done absolute wonders for AMD’s PPD across the HD 5000 and HD 6000 series of graphics cards. In every case we are seeing a near doubling of overall folding performance which is impressive to say the least.

While things look quite rosy all around, performance seems to scale in a linear fashion based on the number of Stream Processors on a given card and the graphics engine’s clock speeds. For example the HD 6870 has 1120 cores and a clock speed of 900Mhz so it can hang with a 1440 SP, 725Mhz HD 5850. Meanwhile, the 1600 core HD 5870 edges out the 1536 SP HD 6970. There also wasn’t much of a difference between identical cards sporting different memory configurations. As the new Core 16 projects mature, we’ll surely see things change a bit but for the time being it seems like the older AMD cards are more than holding their own.

There are some additional points which we should mention that may have a bearing on how some people will look at this new offering from the Stanford team. Much like other AMD clients and projects, this one seems to eat up excess CPU cycles and eats up a good amount of memory as well. The toll on system resources amounts to about 25% CPU usage on a quad core processor and a memory footprint of about 400MB. Hopefully this will be addressed with future releases but most modern systems should be able to take this kind of hit in stride and still be able to offer a lag-free multimedia experience.


When it comes to power consumption, if you absolutely have to fold with an AMD card the older HD 5870 with its 1600 cores currently looks like a good bet from a PPD / watt perspective. However, due to their sky-high PPD numbers when compared to the AMD cards, there are still several lower end NVIDIA products that will be much more appealing as dedicated folders.


Parting Thoughts


So there you have it; the new AMD Core 16 project seems to do wonders for AMD’s Folding@Home performance. Currently there is only a single project making the rounds but there are surely more in the pipeline and what we’ve seen so far is only the first step down a very long road.

The overall PPD increase we saw was certainly impressive but when put into context against NVIDIA’s current crop of GPUs (the relatively inexpensive GTX 560 Ti sports PPD values of 14,000 to 16,000) there’s obviously a long way to go before parity is achieved. Nonetheless, with a single project the Stanford team has breathed new life into AMD folding rigs. Let’s hope that more OpenCL-based projects continue to tap the once-hidden power of AMD’s GPUs because our first taste of the potential lying behind the scenes promises big things in the future.

Stay tuned for more upcoming Folding @ Home articles from Hardware Canucks.



 
 

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