Lattice microbes: High-performance stochastic simulation method for the reaction-diffusion master equation

January 6th, 2013


Spatial stochastic simulation is a valuable technique for studying reactions in biological systems. With the availability of high-performance computing (HPC), the method is poised to allow integration of data from structural, single-molecule and biochemical studies into coherent computational models of cells. Here, we introduce the Lattice Microbes software package for simulating such cell models on HPC systems. The software performs either well-stirred or spatially resolved stochastic simulations with approximated cytoplasmic crowding in a fast and efficient manner. Our new algorithm efficiently samples the reaction-diffusion master equation using NVIDIA graphics processing units and is shown to be two orders of magnitude faster than exact sampling for large systems while maintaining an accuracy of ∼0.1%. Display of cell models and animation of reaction trajectories involving millions of molecules is facilitated using a plug-in to the popular VMD visualization platform. The Lattice Microbes software is open source and available for download at

(Elijah Roberts, John E. Stone and Zaida Luthey-Schulten: “Lattice Microbes: High-Performance Stochastic Simulation Method for the Reaction-Diffusion Master Equation”, Journal of Computational Chemistry, 34:245-255, 2013. [DOI])

CfP: Symposium on chemical computations on GP-GPUs

February 8th, 2010

The symposium will provide technical presentations from the companies advancing the development of GPUs, discussions of the challenges involved in effectively programming GPUs, and presentations on the use of GPUs in a range of chemical applications.

The deadline for submissions is 04/05/2010, and more information can be found at

New OpenMM release provides C and Fortran wrappers

September 7th, 2009

OpenMM is an open-source library that enables molecular dynamics (MD) simulations to be accelerated on high performance computer architectures, such as GPUs. This latest release adds support for:

  • A complete set of C and Fortran wrappers
  • Energy computations on GPUs
  • Ewald summation
  • A faster algorithm for handling constraints
  • And more!

Download the latest version of OpenMM from

Path to Petascale: Adapting GEO/CHEM/ASTRO Applications for Accelerators and Accelerator Clusters

June 4th, 2009

The goal of this workshop, held at the National Center for Supercomputing Applications, University of Illinois at Urbana-Champaign, was to help computational scientists in the geosciences, computational chemistry, and astronomy and astrophysics communities take full advantage of emerging high-performance computing resources based on computational accelerators, such as clusters with GPUs and Cell processors.

Slides are now available online and cover a wide range of topics including

  • GPU and Cell programming tutorials
  • GPU and Cell technology
  • Accelerator programming, clusters, frameworks and building blocks such as sparse matrix-vector products, tree-based algorithms and in particular accelerator integration into large-scale established code bases
  • Case studies and posters from geosciences, computational chemistry and astronomy/astrophysics such as the simulation of earthquakes, molecular dynamics, solar radiation, tsunamis, weather predictions, climate modeling and n-body systems as well as Monte-Carlo, Euler, Navier-Stokes and Lattice-Boltzmann type of simulations

(National Center for Supercomputing Applications, University of Illinois at Urbana-Champaign: Path to Petascale workshop presentations, organized by Wen-mei Hwu, Volodymyr Kindratenko, Robert Wilhelmson, Todd Martínez and Robert Brunner)

Path to Petascale: Adapting GEO/CHEM/ASTRO Applications for Accelerators and Accelerator Clusters

April 13th, 2009

The workshop “Path to PetaScale: Adapting GEO/CHEM/ASTRO Applications for Accelerators and Accelerator Clusters” was held at the National Center for Supercomputing Applications (NCSA), University of Illinois Urbana-Champaign, on April 2-3, 2009. This workshop, sponsored by NSF and NCSA, helped computational scientists in the geosciences, computational chemistry, and astronomy and astrophysics communities take full advantage of emerging high-performance computing accelerators such as GPUs and Cell processors. The workshop consisted of joint technology sessions during the first day and domain-specific sessions on the second day. Slides from the presentations are now online.

Quantum Monte Carlo on GPUs

September 10th, 2007

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)

Two-electron Integral Evaluation on the Graphics Processor Unit

August 16th, 2007

Abstract: We propose the algorithm to evaluate the Coulomb potential in the ab initio density functional calculation on the graphics processor unit (GPU). The numerical accuracy required for the algorithm is investigated in detail. It is shown that GPU, which supports only the single-precision floating number natively, can take part in the major computational tasks. Because of the limited size of the working memory, the Gauss-Rys quadrature to evaluate the electron repulsion integrals (ERIs) is investigated in detail. The error analysis of the quadrature is performed. New interpolation formula of the roots and weights is presented, which is suitable for the processor of the single-instruction multiple-data type. It is proposed to calculate only small ERIs on GPU. ERIs can be classified efficiently with the upper-bound formula. The algorithm is implemented on NVIDIA GeForce 8800 GTX and the Gaussian 03 program suite. It is applied to the test molecules Taxol and Valinomycin. The total energies calculated are essentially the same as the reference ones. The preliminary results show the considerable speedup over the commodity microprocessor. (Two-electron integral evaluation on the graphics processor unit. Koji Yasuda. Journal of Computational Chemistry. July 5, 2007.)