Distributed multi-scale muscle simulation in a hybrid MPI–CUDA computational environment. (January 2016)

• Record Type: Journal Article
• Authors: Ivanović, Miloš; Stojanović, Boban; Kaplarević-Mališić, Ana; Gilbert, Richard; Mijailovich, Srboljub
• Is Part Of: Simulation. Volume 92:Number 1(2016:Jan.)
• Page Start: 19
• Page End: 31
• Publication Date: 2016-01
• Subjects: multi-scale -- muscle modeling -- GPU -- heterogeneous computing -- scheduling policy; Computer simulation -- Periodicals; 003.3
• DOI: 10.1177/0037549715620299
• Languages: English
• ISSNs: 0037-5497

Abstract

We present Mexie, an extensible and scalable software solution for distributed multi-scale muscle simulations in a hybrid MPI–CUDA environment. Since muscle contraction relies on the integration of physical and biochemical properties across multiple length and time scales, these models are highly processor and memory intensive. Existing parallelization efforts for accelerating multi-scale muscle simulations imply the usage of expensive large-scale computational resources, which produces overwhelming costs for the everyday practical application of such models. In order to improve the computational speed within a reasonable budget, we introduce the concept of distributed calculations of multi-scale muscle models in a mixed CPU–GPU environment. The concept is applied to a two-scale muscle model, in which a finite element macro model is coupled with the microscopic Huxley kinetics model. Finite element calculations of a continuum macroscopic model take place strictly on the CPU, while numerical solutions of the partial differential equations of Huxley's cross-bridge kinetics are calculated on both CPUs and GPUs. We present a modular architecture of the solution, along with an internal organization and a specific load balancer that is aware of memory boundaries in such a heterogeneous environment. Solution was verified on both benchmark and real-world examples, showing high utilization of involved processing units, ensuring high scalability. Speed-up results show a boost of two orders of magnitude over any previously reported distributed multi-scale muscle models. This major improvement in computational feasibility of multi-scale muscle models paves the way for new discoveries in the field of muscle modeling and future clinical applications.