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FAQ on the third generation of our GPU software

Table of Contents

Frequently Asked Questions

What are GPUs and how can they help FAH?

GPUs are Graphics Processing Units -- chips used in the graphics cards of today's PCs to help speed high performance graphics, such as 3D games or 3D scientific visualization. GPUs have the possibility to perform an enormous number of Floating Point OPerations (FLOPs). We have been able to write a highly optimized molecular dynamics code for GPUs, achieving a 30x to 40x speed increase over comparable CPU code. As a major part of the scientific benefit is dependent on rapid turnaround of Work Units, this means that we will be able to make an enormous advance over what we could do only just a few years ago.

How can I run Folding@home on my GPU?

Originally the GPU software was a seperate standalone. Over time, drivers and other underlying software matured, and running FAH on the GPU became easier and more reliable. Now our V7 software will check for supported hardware and software, and it can automatically set up a GPU "slot" for you. Please see the V7 FAQ and the Installation Guides for more information.

What GPUs are supported?

For ATI

• 5xxx/6xxx/7xxx ATI Video Card, or newer
• ATI Driver v12.8 driver or newer
• Windows operating system, XP or newer

Note that only 5xxx GPUs and newer are supported due to ATI dropping support for the Brook programming language. They have since moved to OpenCL, which is not supported on 3xxx-class GPUs, and the 4xxx series is not supported enough for efficient FAH calculations. See this blog post for more information.

For Nvidia

• GeForce 8/9 Series
• GeForce 100/300 Series (most)
• GeForce 200/400 Series
• Quadro FX 360, 370, 570, 1600, 1700, 3600, 3700, 4600, 5600
• Quadro NVS 130, 135, 140, 290, 320
• Tesla C870*, 20 Series, T10/C1060/S1070
• MCP77/78*
• NVIDIA GeForce G*

This list comprises most of the hardware supported by NVIDIA's CUDA. However, for best performance, we recommend the more recent series (GeForce G*). Due to the complexities of CUDA support on older hardware, we may likely depreciate support for the older boards.

V7 won't fold on my GPU or it says that my GPU is unsupported. What do I do?

We currently maintain a whitelist of supported GPUs. The list is contained in a file called GPUs.txt. Sometimes there is a delay in adding GPUs to this list, especially for very new models. If your GPU is not supported on this list, please report it in our Folding Support Forum. V7 should automatically download the latest version of this whitelist file periodically.

How are the points determined for the GPU platform?

We have centralized the point information in our Points FAQ.

What about visualization?

Visualization for the GPU platform has been incorporated into the V7 client. Simply click the "View" button to see a cyclic animation of the protein you are currently working on. This is based on the visualization for the (now retired) PlayStation 3 platform.

Thanks to ATI, NVIDIA, Adam Beberg, and Joseph Coffland for their help with developing this visualization.

What about multi-GPU support?

The V7 client is usually able to determine multi-GPU setups and use them appropriately. Each GPU will work on seperate Work Units. Typically, multiple GPUs from the same vendor will not cause any problems, but sometimes V7 can get confused with a mixture of GPU vendors. This usually manifests itself with Work Units being assigned to the wrong GPU and immediately crashing. If this happens, visit the Folding Support Forum and the volunteers there can help you adjust your configuration.

What's the different between the GPU2 (second generation) and the GPU3 (third generation) client?

The differences from the donor perspective are relatively minimal. However, the underlying scientific differences are fairly dramatic. GPU3 is more stable, more efficient, and has a larger set of scientific capabilities. We now use OpenMM, our open-source molecular simulation toolkit. We also utilize OpenCL for ATI GPUs and CUDA for Nvidia.

Please see these two blog posts for more information: Prepping for GPU3 release and Open Beta Release of GPU3.

Can I still use my GPU when the client is running?

Yes. Unlike the CPU, there is no priority-based processing for GPUs. Sometimes you may experience lag from time to time. This is especially noticable for other applications that make heavy use of the GPU, such as graphics rendering or high-powered games. For these press the Pause button in FAHControl to suspend processing until you hit Fold.

What operating systems are supported?

Right now, only the Windows family of OSs, from XP onward. We are working on GPU cores from Linux and OS-X, so they may be a possibility in the near future.

Can I run the GPU as a service?

Due to a Microsoft security feature introduced in Vista, it is no longer possible to install a system service to use the GPU for Folding@home.

A Brief History of FAH: From Tinker to Gromacs and the power of the GPU

Introduction

Since 2000, Folding@home (FAH) has lead to a major jump in the capabilities of molecular simulation. By joining together hundreds of thousands of PCs throughout the world, calculations which were previously considered impossible have now become routine. FAH has targeted the study of protein folding and protein folding diseases, and numerous scientific advances have come from the project.

In 2006, we began looking forward to another major advance in capabilities. This advance utilizes the new, high performance Graphics Processing Units (GPUs) from ATI to achieve performance previously only possible on supercomputers. With this new technology, as well as the new Cell processor in Sony's PlayStation 3, we sought to attain performance on the scale of 100 gigaflop per computer. With this new software and hardware, we pushed Folding@home a major step forward.

Our goal is to apply new technology to dramatically advance the capabilities of Folding@home, applying our simulations to further study of protein folding and related diseases, including Alzheimer's disease, Huntington's disease, and certain forms of cancer. With your help, coupled with new simulation methodologies to harness the new techniques, we will be able to address questions previously considered impossible to tackle computationally, and make even greater impacts on our knowledge of folding and folding related diseases.

Folding@home debuts with the Tinker core (October 2000)

In October 2000, Folding@home was officially released. The main software core engine was the Tinker molecular dynamics (MD) code. Tinker was chosen as the first scientific core due to its versatility and well laid out software design. In particular, Tinker was the only code to support a wide variety of MD force fields and solvent models. With the Tinker core, we were able to make several advances, including the first folding of a small protein starting purely from sequence (subsequently published in Nature).

A major step forward: the Gromacs core (May 2003)

After many months of testing, Folding@home officially rolled out a new core based on the Gromacs MD code in May 2003. Gromacs is the fastest MD code available, and likely one of the most optimized scientific codes in the world. By using hand tuned assembly code and utilizing new hardware in many PCs and Intel-based Macs (the SSE instructions), Gromacs was considerably faster than most MD codes by a factor of about 10x, and approximately a 20x to 30x speed increase over Tinker (which was written for flexibility and functionality, but not for speed).

In 2003, Gromacs had limits to what it could do, and did not support many implicit solvent models, which played a key role in our folding simulations with Tinker. Thus, while Gromacs significantly sped certain calculations, it was not a replacement for Tinker, and so the Tinker core continued to play an important role in the science of Folding@home. For these reasons, points for Gromacs WUs were set to be consistent with points for Tinker WUs. Moreover, we switched the benchmark machine to a 2.8 GHz Pentium 4 (from a 500MHz Celeron) in order to allow us to fairly benchmark these types of WUs (as the benchmark machine needed to have hardware support for SSE).

The next major step forward: Streaming Processor cores (September 2006)

Much like the Gromacs core greatly enhanced Folding@home by a 20x to 30x speed increase via a new utilization of hardware (SSE) in PCs, in 2006, we developed a new streaming processor core to utilize another new generation of hardware: GPUs with programmable floating-point capability. By writing highly optimized, hand-tuned code to run on ATI X1900 class GPUs, the science of Folding@home will see another 20x to 30x speed increase over its previous software (Gromacs) for certain applications. This great speed increase is achieved by running essentially the complete molecular dynamics calculation on the GPU; while this is a challenging software development task, it appears to be the way to achieve the highest speed improvement on GPUs.

In addition, through collaboration with Pande Group, Sony has developed an analogous core for the PS3's Cell processor (another streaming processor), which should see a significant speed increase for the science over the types of calculations we could previously do on a x86/SSE Gromacs core as well. Following what we did with the introduction of Gromacs, we will now switch benchmark machines and include an ATI X1900XT GPU in order to be able to benchmark streaming WUs (which cannot be run on non-GPU machines). This machine will also benchmark CPU units (which continue to be of value since GPUs work only for certain simulations) without using its GPU.

The second-generation GPU core, aka GPU2, for ATI hardware (April 2008)

After running the original GPU core for quite some time and analyzing its results, we have learned a lot about running GPGPU software. For example, it has become clear that a GPGPU approach via DirectX (DX) is not sufficiently reliable for what we need to do. Also, we've learned a great deal about GPU algorithms and improvements. One of the really exciting aspects about GPU's is that not only can they accelerate existing algorithms significantly, they get really interesting in that they can open doors to new algorithms that we would never think to do on CPUs at all (due to their very slow speed on CPUs, not but GPU's).

After much effort, we took all we learned about GPUs from the first-generation client and produced a second-generation client, GPU2. This core was much more technically sophisticated than the original, but it was faster, had higher reliability, ease of use, and much more scientific calculation capabilities. The results from it were very exciting.

The second-generation GPU core for NVIDIA (June 2008)

In collaboration with NVIDIA, we released a GPU2 core for NVIDIA hardware.

The third-generation GPU core (GPU3) for NVIDIA (May 2010)

Due to its great computational abilities, our GPU2 client has had a great scientific impact so far. In Paper #72 (also see the movie), the GPU clients play a star role in allowing Folding@home to push to unprecedented levels, simulating atomic-level protein folding on the millisecond timescale -- several orders of magnitude longer than any previous atomistic folding simulation.

GPU3 brings several key new features to Folding@home. In particular, GPU3 allows for greatly enhanced science: including more accurate models, new science can be done, 2x faster execution of the science, more stable simulations, OpenCL support for run time science optimizations, and greater flexibility for adding new scientific capability. This is accomplished through the use of the http://simtk.org/home/openmm/OpenMM GPU library (which originally came from FAH GPU code, but has been significantly enhanced by Simbios staff).

GPU3 also lays down the foundation for future incorporation of OpenMM's support of OpenCL, which will also bring some very important new scientific features, especially in terms of on-the-fly runtime optimizations of the scientific code. However, at the moment, OpenCL is not supported in the GPU3 NVIDIA client.

Since GPU3 appeared for NVIDIA first (and for a while on NVIDIA only), we have used an NVIDIA GPU for benchmarking. See the Points FAQ for more details.

The third generation GPU core (GPU3) for ATI (March 2011)

OpenCL, OpenMM, fahcore_16, V7, etc.

Credits

In alphabetical order:

• Adam Beberg (Pande Lab): client modifications, GPU APIs under the hood
• Peter Eastman (Simbios)
• Dan Ensign (Pande Lab): server setup, science, testing
• Mike Houston (AMD): testing, problem solving, GPU tuning
• Mark Friedrichs (Pande Lab, Simbios): core science code updates, testing
• Timo Stich (NVIDIA): visualizer for NVIDIA hardware
• Scott LeGrand (NVIDIA): Port of GPU2 code to CUDA, performance enhancements, visualization enhancements
• Vijay Pande (Pande Lab): Project management, fitting square pegs through round holes, etc
• We would also like to thank the Folding@home Community Forum moderators for their help with this FAQ and some early testing of the software.

For More Information

• FAH FAQ
• Folding Support Forum

Last Updated on February 11, 2013, at 01:03 PM