Name
Abstract | ||
|---|---|---|
Modern graphics processing units (GPUs) have recently become fully programmable. Thus a powerful and cost-efficient new computational platform for surgical simulations has emerged. A broad selection of publications has shown that scientific computations obtain a significant speedup if ported from the CPU to the GPU. To take advantage of the GPU however, one must understand the limitations inherent in its design and devise algorithms accordingly. We have observed that many researchers with experience in surgical simulation find this a significant hurdle to overcome. To facilitate the transition from CPU- to GPU-based simulations, we review the most important concepts and data structures required to realise two popular deformable models on the GPU: the finite element model and the spring-mass model |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1007/11790273_11 | ISBMS |
Keywords | Field | DocType |
significant hurdle,popular deformable model,spring-mass model,gpu-based simulation,cost-efficient new computational platform,surgical simulation,broad selection,data structure,significant speedup,finite element model,cost efficiency,scientific computing | Graphics,Data structure,Central processing unit,Memory bandwidth,Computer science,Texture memory,Computational science,General-purpose computing on graphics processing units,OpenGL,Speedup | Conference |
Volume | ISSN | ISBN |
4072 | 0302-9743 | 3-540-36009-3 |
Citations | PageRank | References |
10 | 1.17 | 26 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Thomas Sangild Sørensen | 1 | 42 | 5.73 |
| Jesper Mosegaard | 2 | 50 | 6.82 |