Exploring Parallel Algorithms for Volumetric Mass-Spring-Damper Models in CUDA

Rasmusson, Allan G.; Mosegaard, Jesper; Sørensen, Thomas Sangild

doi:10.1007/978-3-540-70521-5_6

Cited by 18 publications

(11 citation statements)

References 7 publications

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“…In the present case, through Eqs. (15) and (2). In the testing cases of this paper, a static set of coefficients computed with a 0.8 weighting factor on initial stable time-step proved sufficient in our aim to ensure stability, not optimal convergence rates.…”

Section: Future Workmentioning

confidence: 98%

“…By combining (1) and (2) and with some algebraic manipulations around the known forces and the sought displacements:…”

Section: Total Lagrangian Explicit Dynamicmentioning

confidence: 99%

“…One can say that at one end of the spectrum there are FEM-based models -very accurate yet prohibitively slow; on the other end of the spectrum the spring-mass model is fast and often-used for surgical training. However, fast heuristic models like the spring mass method [1,2] generally lack the ability to use nonlinear material models or material models in general beyond simple springs and dampers. For an overview of methods seen in literature, the reader is referred to [3,4].…”

Section: Modeling For Soft Tissue Applicationsmentioning

confidence: 99%

See 2 more Smart Citations

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

Štrbac

Sloten

Famaey

2015

Finite Elements in Analysis and Design

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a b s t r a c tAs 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.

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Section: Future Workmentioning

confidence: 98%

“…By combining (1) and (2) and with some algebraic manipulations around the known forces and the sought displacements:…”

Section: Total Lagrangian Explicit Dynamicmentioning

confidence: 99%

Section: Modeling For Soft Tissue Applicationsmentioning

confidence: 99%

See 1 more Smart Citation

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

Štrbac

Sloten

Famaey

2015

Finite Elements in Analysis and Design

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show abstract

“…Graphics processing unit (GPU) technology is proposed for medical image segmentation (Smistad et al, 2015), or deformable models using spring-mass-damper or tensor-mass methods (Rasmusson et al, 2008) as well as implicit FE (Cecka et al, 2011;Wong et al, 2015). The Total Lagrangian Explicit Dynamic FE algorithm (TLED) (Miller et al, 2007) fits the GPU programming model well given: (1) most element and nodal operations are pleasingly parallel (independent of one another), and (2) memory requirements are very low.…”

Section: Gpu-accelerated Explicit Fe Analysismentioning

confidence: 99%

Rupture risk in abdominal aortic aneurysms: A realistic assessment of the explicit GPU approach

Štrbac

Pierce

Rodríguez-Vila

et al. 2017

Journal of Biomechanics

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Accurate estimation of peak wall stress (PWS) is the crux of biomechanically motivated rupture risk assessment for abdominal aortic aneurysms aimed to improve clinical outcomes. Such assessments often use the finite element (FE) method to obtain PWS, albeit at a high computational cost, motivating simplifications in material or element formulations. These simplifications, while useful, come at a cost of reliability and accuracy. We achieve research-standard accuracy and maintain clinically applicable speeds by using novel computational technologies. We present a solution using our custom finite element code based on graphics processing unit (GPU) technology that is able to account for added complexities involved with more physiologically relevant solutions, e.g. strong anisotropy and heterogeneity. We present solutions up to 17x faster relative to an established finite element code using state-of-the-art nonlinear, anisotropic and nearly-incompressible material descriptions. We show a realistic assessment of the explicit GPU FE approach by using complex problem geometry, biofidelic material law, doubleprecision floating point computation and full element integration. Due to the increased solution speed without loss of accuracy, shown on five clinical cases of abdominal aortic aneurysms, the method shows promise for clinical use in determining rupture risk of abdominal aortic aneurysms.

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“…GPGPUs have already been applied to solve numerical problems for a large variety of applications. In many cases a superior computational performance has been obtained by these cards, when compared to regular CPUs ABCM (Ryoo et al, 2008;Rasmusson et al, 2008;Stantchev et al, 2008;Stantchev et al, 2009;Mesquita et al, 2009). The developments of GPGPU also induced the development of new APIs (Application Programming Interfaces).…”

Section: Parallel Computing On Graphics Hardwarementioning

confidence: 99%

Constant Boundary Elements on graphics hardware: a GPU-CPU complementary implementation

Labaki¹,

Ferreira²,

Mesquita³

2011

J. Braz. Soc. Mech. Sci. & Eng.

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Exploring Parallel Algorithms for Volumetric Mass-Spring-Damper Models in CUDA

Cited by 18 publications

References 7 publications

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

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

Rupture risk in abdominal aortic aneurysms: A realistic assessment of the explicit GPU approach

Constant Boundary Elements on graphics hardware: a GPU-CPU complementary implementation

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