VFFVA: dynamic load balancing enables large-scale flux variability analysis.

ABSTRACT:

Background

Genome-scale metabolic models are increasingly employed to predict the phenotype of various biological systems pertaining to healthcare and bioengineering. To characterize the full metabolic spectrum of such systems, Fast Flux Variability Analysis (FFVA) is commonly used in parallel with static load balancing. This approach assigns to each core an equal number of biochemical reactions without consideration of their solution complexity.

Results

Here, we present Very Fast Flux Variability Analysis (VFFVA) as a parallel implementation that dynamically balances the computation load between the cores in runtime which guarantees equal convergence time between them. VFFVA allowed to gain a threefold speedup factor with coupled models and up to 100 with ill-conditioned mo

SUBMITTER: Guebila MB 

PROVIDER: S-EPMC7523073 | biostudies-literature | 2020 Sep

REPOSITORIES: biostudies-literature