Abstract
Stochastic, spatial reaction-diffusion simulations have been widely used in systems biology and computational neuroscience. However, the increasing scale and complexity of models and morphologies have exceeded the capacity of any serial implementation. This led to the development of parallel solutions that benefit from the boost in performance of modern supercomputers. In this paper, we describe an MPI-based, parallel operator-splitting implementation for stochastic spatial reaction-diffusion simulations with irregular tetrahedral meshes. The performance of our implementation is first examined and analyzed with simulations of a simple model. We then demonstrate its application to real-world research by simulating the reaction-diffusion components of a published calcium burst model in both Purkinje neuron sub-branch and full dendrite morphologies. Simulation results indicate that our implementation is capable of achieving super-linear speedup for balanced loading simulations with reasonable molecule density and mesh quality. In the best scenario, a parallel simulation with 2,000 processes runs more than 3,600 times faster than its serial SSA counterpart, and achieves more than 20-fold speedup relative to parallel simulation with 100 processes. In a more realistic scenario with dynamic calcium influx and data recording, the parallel simulation with 1,000 processes and no load balancing is still 500 times faster than the conventional serial SSA simulation.
Highlights
Recent research in systems biology and computational neuroscience, such as the study of Purkinje cell calcium dynamics (Anwar et al, 2014), has significantly boosted the development of spatial stochastic reaction-diffusion simulators
Simulations reported in this paper were run on OIST’s high performance cluster, “Sango.” Each computing node on Sango has two 12-core 2.5 GHz Intel Xeon E5-2680v3 processors, sharing 128 GiB of system memory
For simulations with the simple model (Section Reaction-Diffusion Simulation with Simple Model and Geometry) we were able to limit the number of cores used per processor to 10
Summary
Recent research in systems biology and computational neuroscience, such as the study of Purkinje cell calcium dynamics (Anwar et al, 2014), has significantly boosted the development of spatial stochastic reaction-diffusion simulators. These simulators can be separated into two major categories, voxel-based and particle-based. Particle-based simulators, for example, Smoldyn (Andrews and Bray, 2004) and MCell (Kerr et al, 2008), track the Brownian motion of individual molecules, and simulate molecular reactions caused by collisions. While tracking individual molecules is not required for voxel-based simulators, the exact solution of Gillespie SSA is highly sequential and inefficient for large-scale simulation due to the massive amount of SSA events (Dematté and Mazza, 2008)
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
