We describe an extension of our graphics processing unit (GPU) electronic structure program TeraChem to include atom-centered Gaussian basis sets with d angular momentum functions. This was made possible by a “meta-programming” strategy that leverages computer algebra systems for the derivation of equations and their transformation to correct code. We generate a multitude of code fragments that are formally mathematically equivalent, but differ in their memory and floating-point operation footprints. We then select between different code fragments using empirical testing to find the highest performing code variant. This leads to an optimal balance of floating-point operations and memory bandwidth for a given target architecture without laborious manual tuning. We show that this approach is capable of similar performance compared to our hand-tuned GPU kernels for basis sets with s and p angular momenta. We also demonstrate that mixed precision schemes (using both single and double precision) remain stable and accurate for molecules with d functions. We provide benchmarks of the execution time of entire self-consistent field (SCF) calculations using our GPU code and compare to mature CPU based codes, showing the benefits of the GPU architecture for electronic structure theory with appropriately redesigned algorithms. We suggest that the meta-programming and empirical performance optimization approach may be important in future computational chemistry applications, especially in the face of quickly evolving computer architectures.
(Alexey V Titov , Ivan S. Ufimtsev , Nathan Luehr and Todd J. Martínez: “Generating Efficient Quantum Chemistry Codes for Novel Architectures”, accepted for publication in the Journal of Chemical Theory and Computation, 2012. [DOI])
Ivan Ufimtsev and Todd Martínez at the University of Illinois at Urbana-Champaign have implemented an efficient method of calculating two-electron repulsion integrals over Gaussian basis functions on the GPU. Virtually all modern quantum chemical calculations require evaluating millions to billions of these integrals. This problem turns out to be well-suited to the massively parallel architecture of GPUs by an appropriate partitioning of the problem. A benchmark test performed for the evaluation of approximately one million (ss|ss) integrals over contracted s-orbitals showed that a naïve algorithm implemented on the GPU achieves up to 130-fold speedup over a traditional CPU implementation on an AMD Opteron. Subsequent calculations on a 256-atom DNA strand show that the GPU advantage is maintained for basis sets including higher angular momentum functions. (Quantum Chemistry on Graphical Processing Units. 1. Strategies for Two-Electron Integral Evaluation, Ivan S. Ufimtsev and Todd J. Martínez, J. Chem. Theory Comput., 4 (2), 222 -231, 2008. doi:10.1021/ct700268q)
In this paper we describe a modification of a general purpose code for quantum mechanical calculations of molecular properties (Q-Chem) to use a graphical processing unit. We report a 4.3x speedup of the resolution-of-the-identity second-order Møller-Plesset perturbation theory execution time for single point energy calculation of linear alkanes. Furthermore, we obtain the correlation and total energy for n-octane conformers as the torsional angle of central bond is rotated to show that precision is not lost for these types of calculations. This code modification is accomplished using the NVIDIA CUDA Basic Linear Algebra Subprograms (CUBLAS) library for an NVIDIA Quadro FX 5600 graphics card. Finally, we anticipate further speedups of other matrix algebra based electronic structure calculations using a similar approach. (Accelerating Resolution-of-the-Identity Second-Order Møller-Plesset Quantum Chemistry Calculations with Graphical Processing Units. Vogt, L., Olivares-Amaya, R., Kermes, S., Shao, Y., Amador-Bedolla, C., and Aspuru-Guzik, A. J. Phys. Chem. A, 2008, DOI: 10.1021/jp0776762)
This paper by Anderson et al at Caltech describes a method to use GPUs to accelerate Quantum Monte Carlo on a GPU. QMC is among the most accurate (and expensive) methods in the quantum chemistry zoo. Primarily, this involves the investigation of tricks available to this algorithm to speed up matrix multiplication. That is, as a statistical algorithm, the authors studied the performance enhancements available when multiplying many matrices simultaneously. Additionally, the paper explores the Kahan Summation Formula to improve the accuracy of GPU matrix multiplication. (Quantum Monte Carlo on Graphical Processing Units. Amos G. Anderson, William A Goddard III, Peter Schroder. Computer Physics Communications)