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Manuscript Title: AESS: Accelerated Exact Stochastic Simulation
Authors: David D. Jenkins, Gregory D. Peterson
Program title: AESS
Catalogue identifier: AEJW_v1_0
Distribution format: tar.gz
Journal reference: Comput. Phys. Commun. 182(2011)2580
Programming language: C for processors, CUDA for NVIDIA GPUs.
Computer: Developed and tested on various x86 computers and NVIDIA C1060 Tesla and GTX 480 Fermi GPUs. The system targets x86 workstations, optionally with multicore processors or NVIDIA GPUs as accelerators.
Operating system: Tested under Ubuntu Linux OS and CentOS 5.5 Linux OS.
Keywords: Stochastic Simulation, Gillespie Algorithm, Systems Biology, Monte Carlo, Computational Ecology.
PACS: 07.05.Tp.
Classification: 3, 16.12.

Nature of problem:
Simulation of chemical systems, particularly with low species populations, can be accurately performed using Gillespie's method of stochastic simulation. Numerous variations on the original stochastic simulation algorithm have been developed, including approaches that produce results with statistics that exactly match the chemical master equation (CME) as well as other approaches that approximate the CME.

Solution method:
The Accelerated Exact Stochastic Simulation (AESS) tool provides implementations of a wide variety of popular variations on the Gillespie method. Users can select the specific algorithm considered most appropriate. Comparisons between the methods and with other available implementations indicate that AESS provides the fastest known implementation of Gillespie?s method for a variety of test models. Users may wish to execute ensembles of simulations to sweep parameters or to obtain better statistical results, so AESS supports acceleration of ensembles of simulation using parallel processing with MPI, SSE vector units on x86 processors, and/or using NVIDIA GPUs with CUDA.