Stepping into Fully GPU Accelerated Biomedical Applications

  • Caroline Mendonca Costa
  • Gundolf Haase
  • Manfred Liebmann
  • Aurel Neic
  • Gernot Plank
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8353)


We present ideas and first results on a GPU acceleration of a non-linear solver embedded into the biomedical application code CARP. The linear system solvers have been transferred already in the past and so we concentrate on how to extend the GPU acceleration to larger portions of the code. The finite element assembling of stiffness and mass matrices takes at least 50 % of the CPU time and therefore we investigate this process for the bidomain equations but with focus on later use in non-linear and/or time-dependent problems. The CUDA code for matrix calculation and assembling is faster by a factor up to \(90\) compared to a single CPU core. The routines were integrated to CARP’s main code and they are already used to assemble the FE matrices of the bidomain model. Further performance studies are still required for the bidomain-mechanics model.


Mass Matrice Memory Bandwidth Matrix Entry Linear Solver Conductivity Tensor 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Caroline Mendonca Costa
    • 2
  • Gundolf Haase
    • 1
  • Manfred Liebmann
    • 1
  • Aurel Neic
    • 1
  • Gernot Plank
    • 2
  1. 1.Institute for Mathematics and Scientific ComputingUniversity of GrazGrazAustria
  2. 2.Institute of BiophysicsMedical University of GrazGrazAustria

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