Michael J. Hallock, John E. Stone, Elijah Roberts, Corey Fry, and Zaida
Luthey-Schulten.
Simulation of reaction diffusion processes over biologically relevant
size and time scales using multi-GPU workstations.
Parallel Computing, 40:86-99, 2014.
(PMC: PMC4039640)
HALL2014-ZLS
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 efficiency 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.
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