Simulation of reaction diffusion processes over biologically relevant size and time scales using multi-GPU workstations

Abstract

Simulation of in vivo cellular processes with the reaction-diffusion master equation (RDME) is a computationally expensive task. Our previous software enabled simulation of inhomogeneous biochemical systems for small bacteria over long time scales using the MPD-RDME method on a single GPU. Simulations of larger eukaryotic systems exceed the on-board memory capacity of individual GPUs, and long time simulations of modest-sized cells such as yeast are impractical on a single GPU. We present a new multi-GPU parallel implementation of the MPD-RDME method based on a spatial decomposition approach that supports dynamic load balancing for workstations containing GPUs of varying performance and memory capacity. We take advantage of high-performance features of CUDA for peer-to-peer GPU memory transfers and evaluate the performance of our algorithms on state-of-the-art GPU devices. We present parallel e ciency and performance results for simulations using multiple GPUs as system size, particle counts, and number of reactions grow. We also demonstrate multi-GPU performance in simulations of the Min protein system in E. coli. Moreover, our multi-GPU decomposition and load balancing approach can be generalized to other lattice-based problems.

Challenges of Integrating Stochastic Dynamics and Cryo-Electron Tomograms in Whole-Cell Simulations

“…10,22,23 Lattice Microbes (LM) efficiently samples particle number trajectories from the solution to the underlying RDME describing the chemical system embedded in a lattice-based representation of the system geometry. The RDME is $$\frac{\mathrm{normald}Pfalse(\mathrm{bold-italicx},tfalse)}{\mathrm{normald}t}=true\sum _v^V{\displaystyle \sum _r^R[-a_r({\mathrm{bold-italicx}}_v)P({\mathrm{bold-italicx}}_v,t)+a_r({\mathrm{bold-italicx}}_v-{\mathrm{bold-italicS}}_r)P({\mathrm{bold-italicx}}_v-{\mathrm{bold-italicS}}_r,t)]}+true\sum _v^V{\displaystyle \sum _{\xi }^{\pm \hat{i},\hat{j},\hat{k}}true\sum _{\alpha }^{N}false[-d_v^{\alpha }{x}_v^{\alpha }Pfalse(\mathrm{bold-italicx},tfalse)+d_{v+\xi }^{\alpha }false(x_{v+\xi }^{\alpha }+1false)Pfalse(\mathrm{bold-italicx}+{\mathbf{1}}_{v+\xi }^{\alpha }-{\mathbf{1}}_v^{\alpha },tfalse)false],}$$where P ( x , t ) is the probability distribution to find a configuration x at time t , and the configuration vector x contains the number of species present of each type at each subvolume.…”

“…LM is a GPU-based simulation code that was designed from the ground up to be highly computationally efficient 10 in order to reach the length and time scales necessary to study biological systems across entire cells. Reaction processes within the cell are modeled within the framework of reaction–diffusion master equations (RDME), whose specification requires kinetic parameters obtained from many disparate sources including super-resolution imaging, fluorescence, and biochemical experiments as well as other computational techniques such as molecular dynamics and Brownian dynamics.…”

Effects of DNA replication on mRNA noise

“…Simulations were performed using the Gillespie stochastic simulation algorithm (24) as implemented in the Lattice Microbes software version 2.2 (25,38). All simulations were performed using NVIDIA GPUs and analysis was written in Python, using the PyLM interface to Lattice Microbes version 1.0 (39).…”

Solvated and generalised Born calculations differences using GPU CUDA and multi-CPU simulations of an antifreeze protein with AMBER

“…Normally, MD simulations rely on serial or parallel computations executed only on CPUs, but now GPU units can be used to extend, accelerate or devise novel computational approaches for biomolecular or other studies using both single and multiple GPUs. [1][2][3][4] An increasing number of computer simulation packages have been ported to run in GPUs, for example, LAMPPS, [5] GROMACS, [6] NAMD [7]; Bindsurf [8] and also AMBER. [9,10] Independent tests comparing GPU and multi-CPU runs on the same machine have been performed with independent or in-house developed software [11] as well as by the software developers when releasing their own GPU capable implementations.…”