We don’t usually think of chemicals as having shapes. But from the simple V of a water molecule to the intricate folds of a protein, the shape of a chemical compound plays a critical role in how it reacts with other molecules. For example, many drugs work by binding to specific receptors in cells, a process that depends on a precise match between the shape of the drug molecule and of the receptor.
The shape of a molecule is determined by the interactions of the electrons in its constituent atoms, ultimately at the level of quantum physics. In the simple case of water, the physics causes the two hydrogen atoms to bond to oxygen at a 105° angle. Proteins, however, can contain thousands of atoms arranged in a helix that twists and turns into complex shapes.
As in so many other fields, the immense power of computers to complete in seconds. computations that would take human beings lifetimes has revolutionized chemistry, giving rise to the collaboration of chemistry, physics, mathematics and computer science known as computational chemistry. Pharmaceutical research, for example, used to be a hit-or-miss process testing thousands of chemicals for pharmacological effects, with far more misses than hits. Now researchers are more likely to figure out what sort of molecule they need, set out to design it, and then figure out a way to synthesize it.
For large molecules–and proteins and other biologically active molecules that are often very large–this becomes a daunting computational task. Fortunately, it is one that lends itself well to the efficiencies of parallel computing. Most of this work used to be carried out on supercomputers or custom-designed clusters of workstations and servers. More recently, the work is moving to massively multi-core graphics processing units, such as NVIDIA’s Tesla.
The results of this can be very impressive. TeraChem, from PetaChem LLC is a quantum chemistry software package optimized for GPUs. Running analyses of several molecules on a workstation with four Tesla GPUs, TeraChem performed 8 to 50 times faster than the widely used General Atomic and Molecular Structure System (GAMESS) software running on a cluster of 256 quad core CPUs. A quad Tesla workstation is hardly your garden variety desktop—the 240-core Tesla C1060s go for about $1,300 apiece—but the setup outperformed far more expensive and complex hardware.
Harvard chemist Alán Aspuru-Guzic is a convert to GPU computing. His quantum chemistry research group analyses molecultes using electron correlation. This approach requires solutions to Schrödinger equations, differential equations that describe changes in the state of the system over time. An exact solution to a Schrödinger equation requires knowing all possible quantum states at the same time, a complicated version of the famous mind experiment of Schrödinger’s cat, which may be alive, dead, or both inside a sealed box. That’s a problem that can only be solved on a quantum computer, a device that unfortunately exists only in computer science labs, and there only in a primitive and not very usable state. Eventually, quantum computers will be available to computational chemists. But, says Aspuru-Guzik, “it will take a decade, maybe two decades. It’s hard to predict.”
Lacking quantum computers, researchers have to settle for close approximations to the exact solutions, but even these require tremendous computational effort. “In the meantime we have the GPU,” says Aspuru-Guzik. “The GPU is a very attractive alternative because it is cheap. It’s the future of computing.”
Aspuru-Guzik’s toolkit includes Q-Chem 3.1, a commercial quantum chemistry program, and CUBLAS, a high-powered linear algebra system based on CUDA, NVIDIA’s technology for general computing on GPUs. They found that using GPUs to assist in the multiplication of large matrices, another job ideally suited to parallel processing, sped the task by a factor of 13 over the use of the CPU alone. Most of us, of course, will never try to figure out the interactions of electrons in a molecule, nor will we have any use for a quantum computer. But the result of this work is important to all of us because it means better understanding of basic chemical processes and ultimately such things as faster development of new drugs. You can even get involved through a Stanford University project called Folding@home. that uses idle time on thousands of computers to compute protein folding. And if your computer has a modern GPU, you’ll become part of the parallel revolution.
This post is an entry in The World Isn’t Flat, It’s Parallel series running on nTersect, focused on the GPU’s importance and the future of parallel processing. Today, GPUs can operate faster and more cost-efficiently than CPUs in a range of increasingly important sectors, such as medicine, national security, natural resources and emergency services. For more information on GPUs and their applications, keep your eyes on The World Isn’t Flat, It’s Parallel.