Analyzing the potential of GPGPUs for real-time explicit finite element analysis of soft tissue deformation using CUDA

As the presence of finite element implementations on General Purpose Graphics Processing Units (GPGPUs) is the literature increases, detailed and in-breadth testing of the hardware is somewhat lacking. We present an implementation and detailed analysis of an FE algorithm designed for real-time solution, particularly aimed at elasticity problems applied to soft tissue deformation.An efficient parallel implementation of Total Lagrangian Explicit Dynamics implementation is elucidated and the potential for real-time execution is examined. It is shown that in conjunction with modern computing architectures, solution times can be significantly reduced, depending on the solution strategy.The usability of the method is investigated by conducting a broad assay on ranging model sizes and different cards and comparing to an industry-proven FE code Abaqus. In doing so, we study the effect of using single/double precision computation, quantify and present error measurements as a function of the number of time-steps. We also examine the usage of a special texture memory space and its effect on computation for different devices. Adding material complexity in the form of a tissue damage model is presented and its computational impact elucidated. The aggregate results show that, for a particular set of problems, it is possible to compute a simple set of test cases 30-120 times faster than current commercial solutions.According to the speedups achieved, an indication is provided that the GPGPU technology shows promise in the undertaking of real-time FE computation. HighlightsWe present and examine an implementation Total Lagrangian FE algorithm.Implemented for many-core GPGPU devices to dramatically increase execution speed.Detailed performance analyses and error measures are put forth.Algorithm and devices' performance is compared to an industry-proven code.The paper examines and demonstrates the potential in real-time FE usage.

[1]  Naga K. Govindaraju,et al.  A Survey of General‐Purpose Computation on Graphics Hardware , 2007 .

[2]  Karol Miller,et al.  Suite of finite element algorithms for accurate computation of soft tissue deformation for surgical simulation , 2009, Medical Image Anal..

[3]  Mark A. Ganter,et al.  Real-time finite element modeling for surgery simulation: an application to virtual suturing , 2004, IEEE Transactions on Visualization and Computer Graphics.

[4]  Karol Miller,et al.  Real-Time Nonlinear Finite Element Computations on GPU - Application to Neurosurgical Simulation. , 2010, Computer methods in applied mechanics and engineering.

[5]  Karol Miller,et al.  Computation of intra-operative brain shift using dynamic relaxation. , 2009, Computer methods in applied mechanics and engineering.

[6]  K. Miller,et al.  Total Lagrangian explicit dynamics finite element algorithm for computing soft tissue deformation , 2006 .

[7]  Alan Liu,et al.  A Survey of Surgical Simulation: Applications, Technology, and Education , 2003, Presence: Teleoperators & Virtual Environments.

[8]  MeierU.,et al.  Real-time deformable models for surgery simulation , 2005 .

[9]  Thomas Sangild Sørensen,et al.  A GPU accelerated spring mass system for surgical simulation. , 2005, Studies in health technology and informatics.

[10]  LiuAlan,et al.  A survey of surgical simulation , 2003 .

[11]  Ted Belytschko,et al.  A survey of numerical methods and computer programs for dynamic structural analysis , 1976 .

[12]  T. Belytschko,et al.  Computational Methods for Transient Analysis , 1985 .

[13]  James Demmel,et al.  Benchmarking GPUs to tune dense linear algebra , 2008, HiPC 2008.

[14]  M. Bro-Nielsen,et al.  Finite element modeling in surgery simulation , 1998, Proc. IEEE.

[15]  Stephane Cotin,et al.  Efficient Nonlinear FEM for Soft Tissue Modelling and Its GPU Implementation within the Open Source Framework SOFA , 2008, ISBMS.

[16]  Herve Delingette,et al.  Real-Time Elastic Deformations of Soft Tissues for Surgery Simulation , 1999, IEEE Trans. Vis. Comput. Graph..

[17]  Hervé Delingette,et al.  Non-linear anisotropic elasticity for real-time surgery simulation , 2003, Graph. Model..

[18]  Sébastien Ourselin,et al.  NiftySim: A GPU-based nonlinear finite element package for simulation of soft tissue biomechanics , 2014, International Journal of Computer Assisted Radiology and Surgery.

[19]  Hervé Delingette,et al.  Nonlinear and anisotropic elastic soft tissue models for medical simulation , 2001, Proceedings 2001 ICRA. IEEE International Conference on Robotics and Automation (Cat. No.01CH37164).

[20]  Eric Darve,et al.  Assembly of finite element methods on graphics processors , 2011 .

[21]  Aaftab Munshi,et al.  The OpenCL specification , 2009, 2009 IEEE Hot Chips 21 Symposium (HCS).

[22]  Karol Miller,et al.  An efficient hourglass control implementation for the uniform strain hexahedron using the Total Lagrangian formulation , 2007 .

[23]  Karol Miller,et al.  An adaptive dynamic relaxation method for solving nonlinear finite element problems. Application to brain shift estimation , 2011, International journal for numerical methods in biomedical engineering.

[24]  Eric F Darve,et al.  A new sparse matrix vector multiplication graphics processing unit algorithm designed for finite element problems , 2015 .

[25]  Mariano Alcañiz Raya,et al.  Real-time deformable models for surgery simulation: a survey , 2005, Comput. Methods Programs Biomed..

[26]  Thomas Sangild Sørensen,et al.  Exploring Parallel Algorithms for Volumetric Mass-Spring-Damper Models in CUDA , 2008, ISBMS.