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The precise failure mechanisms of bone implants are still incompletely understood. Micro-computed tomography in combination with finite element analysis appears to be a potent methodology to determine the mechanical stability of bone-implant constructs. To assess this microstructural finite element (µFE) analysis approach, pull-out tests were designed and conducted on ten sheep vertebral bodies into which orthopedic screws were inserted. µFE models of the same bone-implant constructs were then built and solved, using a large-scale linear FE-solver. µFE calculated pull-out strength correlated highly with the experimentally measured pull-out strength (r 2 = 0.87) thereby statistically supporting the µFE approach. These results suggest that bone-implant constructs can be analyzed using µFE in a detailed and unprecedented way. This could potentially facilitate the development of future implant designs leading to novel and improved fracture fixation methods.Keywords Bone-implant competence · Micro-finite element analysis (µFEA) · Bone microstructure · Peri-implant bone quality · Mechanical testing · Pull-out strength

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Osteoporosis is a disease that affects a growing number of people by increasing the fragility of their bones. To improve the understanding of the bone quality, large scale computer simulations are applied. A fast, scalable and memory efficient solver for such problems is ParOSol. It uses the preconditioned conjugate gradient algorithm with a multigrid preconditioner. A modification of ParOSol is presented that profits from the exorbitant compute capabilities of recent generalpurpose graphics processing units (GPGPUs). Adaptations of data structures for the GPGPU are discussed. The fastest implementation on a GPGPU achieves a speedup of more than five compared with the CPU implementation and scales from 1 to at least 256 GPGPUs.

Coupling recent imaging capabilities with microstructural finite element (µFE) analysis offers a powerful tool to determine bone stiffness and strength. It shows high potential to improve individual fracture risk prediction, a tool much needed in the diagnosis and treatment of osteoporosis that is, according to the WHO 1 , second only to cardiovascular disease as a leading health care problem. We adapted a multilevel preconditioned conjugate gradient method to solve the very large voxel models that arise in µFE bone structure analysis. The intricate microstructure properties of bone lead to sparse matrices with billions of rows, thus rendering this application to be an ideal candidate for massively parallel architectures such as the BG/L Supercomputer. In this work we present our progress as well as the challenges we were able to identify in our quest to achieve scalability to thousands of BG/L cores.

We present a novel architecture for hardware-accelerated rendering of point primitives. Our pipeline implements a refined version of EWA splatting, a high quality method for antialiased rendering of point sampled representations. A central feature of our design is the seamless integration of the architecture into conventional, OpenGL-like graphics pipelines so as to complement triangle-based rendering. The specific properties of the EWA algorithm required a variety of novel design concepts including a ternary depth test and using an on-chip pipelined heap data structure for making the memory accesses of splat primitives more coherent. In addition, we developed a computationally stable evaluation scheme for perspectively corrected splats. We implemented our architecture both on reconfigurable FPGA boards and as an ASIC prototype, and we integrated it into an OpenGL-like software implementation. Our evaluation comprises a detailed performance analysis using scenes of varying complexity.