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A Versatile Automated Platform for Micro-scale Cell Stimulation Experiments
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SIMENGINE: a low-cost, high-performance platform for embedded biophysical simulations.

Randall K Weinstein1, Christopher T Church, Carl S Lebsack

  • 1Simatra Modeling Technologies, Atlanta, Georgia 30308, USA. info@simatratechnologies.com

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
|December 8, 2009
PubMed
Summary
This summary is machine-generated.

SimEngine accelerates numerical simulations for dynamical systems, making high-performance computing accessible. This platform enhances simulation speeds and targets real-time embedded applications, including medical devices.

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Area of Science:

  • Computational Science
  • Embedded Systems Engineering
  • High-Performance Computing

Background:

  • Numerical simulations of dynamical systems are crucial but underutilized due to technical and financial barriers.
  • Existing platforms often lack real-time capabilities required for embedded systems, particularly in medical devices.
  • High-performance computing (HPC) hardware is not readily accessible for many modelers.

Purpose of the Study:

  • Introduce simEngine, a novel platform designed to simplify numerical simulations of dynamical systems.
  • Enable faster simulation speeds and facilitate the integration of HPC into real-time embedded systems.
  • Reduce the programming effort required for modelers to leverage HPC resources.

Main Methods:

  • Developed a platform comprising a high-level mathematical language for simulation description.
  • Implemented a compiler and resource scheduler to generate high-performance simulation code.
  • Utilized field-programmable gate arrays (FPGAs) on a network-attached embedded computing device for HPC.

Main Results:

  • Achieved simulation speeds 10-100 times faster than conventional microprocessors.
  • Demonstrated the platform's capability for real-time, high-performance computing in embedded applications.
  • Presented an example model implementation showcasing significant performance gains.

Conclusions:

  • SimEngine effectively lowers barriers to HPC for dynamical system simulations.
  • The platform is suitable for real-time and embedded applications, with potential for medical devices.
  • Future development aims to further enhance system performance and expand applicability.