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Dynamic Radial View Based Culling for Continuous Self-Collision Detection
"... atomic clusters to improve the overall performance of continuous self-collision detection over RVBC. The speedup of our method compared to RVBC is 5.2 × in this octopus example. The radial view-based culling (RVBC) method has been presented for continuous self-collision detection to efficiently cull ..."
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atomic clusters to improve the overall performance of continuous self-collision detection over RVBC. The speedup of our method compared to RVBC is 5.2 × in this octopus example. The radial view-based culling (RVBC) method has been presented for continuous self-collision detection to efficiently cull away non-colliding regions. While this technique mainly relies on the seg-mented clusters of the reference pose and the associated fixed ob-server points, it has several drawbacks during the animation and the reduced cost of executing collision detection is limited. We thus present a modified framework to improve the culling efficiency of RVBC. At the preprocessing stage, we segment the closed de-formable mesh according to not only the attached skeleton but also the triangle orientations, in order to minimize the collision checks of triangles in a cluster. At the runtime stage, we dynamically merge adjacent clusters and update the positions of observer points if the merged shape is nearly convex. This strategy minimizes the number of triangles in different clusters that required collision check. Our framework can be easily integrated with bounding vol-ume hierarchies to boost the culling efficiency. Experimental results show that our framework achieves up to 5.2 times speedup over the original RVBC method and even more times over the recent tech-niques.
InK-Compact: In-Kernel Stream Compaction and Its Application to Multi-Kernel Data Visualization on General-Purpose GPUs
"... Stream compaction is an important parallel computing primitive that produces a reduced (compacted) output stream consisting of only valid elements from an input stream containing both invalid and valid elements. Computing on this compacted stream rather than the mixed input stream leads to improveme ..."
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Stream compaction is an important parallel computing primitive that produces a reduced (compacted) output stream consisting of only valid elements from an input stream containing both invalid and valid elements. Computing on this compacted stream rather than the mixed input stream leads to improvements in performance, load balancing and memory footprint. Stream compaction has numerous applications in a wide range of domains: e.g. deferred shading, isosurface extraction and surface voxelization in computer graphics and visualization. We present a novel In-Kernel stream compaction method, where compaction is completed before leaving an operating kernel. This contrasts with conventional parallel compaction methods that require leaving the kernel and running a prefix sum kernel followed by a scatter kernel. We apply our compaction methods to ray-tracing-based visualization of volumetric data. We demonstrate that the proposed In-Kernel compaction outperforms the standard out-of-kernel Thrust parallel-scan method for performing stream compaction in this real-world application. For the data visualization, we also propose a novel multi-kernel ray-tracing pipeline for increased thread coherency and show that it outperforms a conventional single-kernel approach.
A Modular Framework for Deformation and Fracture using GPU Shaders
"... Advances in the graphical realism of modern video games have been achieved mainly through the development of the GPU (Graphics Processing Unit), providing a dedicated graphics co-processor and framebuffer. The most recent GPU's are extremely capable and so flexible that it is now possible to i ..."
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Advances in the graphical realism of modern video games have been achieved mainly through the development of the GPU (Graphics Processing Unit), providing a dedicated graphics co-processor and framebuffer. The most recent GPU's are extremely capable and so flexible that it is now possible to implement a wide range of algorithms on graphics hardware that were previously confined to the computer's CPU (Central Processing Unit). We present a modular framework for real-time simulation of deformation and fracture for use in computer games that, rather than employing a General Purpose GPU (GPGPU) Framework, implements aspects of the simulation within shader programs on recent GPU's.
Discrete Differential Geometry of Thin Materials for Computational Mechanics
, 2013
"... Instead of applying numerical methods directly to governing equations, another ap-proach to computation is to discretize the geometric structure specific to the problem first, and then compute with the discrete geometry. This structure-respecting discrete-differential-geometric (DDG) approach often ..."
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Instead of applying numerical methods directly to governing equations, another ap-proach to computation is to discretize the geometric structure specific to the problem first, and then compute with the discrete geometry. This structure-respecting discrete-differential-geometric (DDG) approach often leads to new algorithms that more accurately track the physically behavior of the system with less computational effort. Thin objects, such as pieces of cloth, paper, sheet metal, freeform masonry, and steel-glass structures are particularly rich in geometric structure and so are well-suited for DDG. I show how under-standing the geometry of time integration and contact leads to new algorithms, with strong correctness guarantees, for simulating thin elastic objects in contact; how the performance of these algorithms can be dramatically improved without harming the geometric structure, and thus the guarantees, of the original formulation; how the geometry of static equilibrium can be used to efficiently solve design problems related to masonry or glass buildings; and how discrete developable surfaces can be used to model thin sheets undergoing isometric deformation.
Interactive Continuous Collision Detection for Topology Changing Models Using Dynamic Clustering
"... Figure 1 : This simulation is generated using finite element solver on a mesh with about 9K triangles [Tang et al. 2011b]. Our novel continuous collision detection (CCD) algorithm takes about 1.1 second (on average) on a single CPU core to perform all intra-object and self-collision queries. It is ..."
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Figure 1 : This simulation is generated using finite element solver on a mesh with about 9K triangles [Tang et al. 2011b]. Our novel continuous collision detection (CCD) algorithm takes about 1.1 second (on average) on a single CPU core to perform all intra-object and self-collision queries. It is about 5X faster than prior CCD algorithms for deformable models. Abstract We present a fast algorithm for continuous collision detection between deformable models. Our approach performs no precomputation and can handle general triangulated models undergoing topological changes. We present a fast decomposition algorithm that represents the mesh boundary using hierarchical clusters and only needs to perform inter-cluster collision checks. The key idea is to compute such clusters quickly and merge them to generate a dynamic bounding volume hierarchy. The overall approach reduces the overhead of computing the hierarchy and also reduces the number of false positives. We highlight the the algorithm's performance on many complex benchmarks generated from medical simulations and crash analysis. In practice, we observe 1.4 to 5 times speedup over prior CCD algorithms for deformable models in our benchmarks.
Fast Collision Culling in Large-Scale Environments Using GPU Mapping Function
, 2012
"... This paper presents a novel and efficient GPU-based parallel algorithm to cull non-colliding object pairs in very large-scale dynamic simulations. It allows to cull objects in less than 25ms with more than 100K objects. It is designed for many-core GPU and fully exploits multi-threaded capabilities ..."
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This paper presents a novel and efficient GPU-based parallel algorithm to cull non-colliding object pairs in very large-scale dynamic simulations. It allows to cull objects in less than 25ms with more than 100K objects. It is designed for many-core GPU and fully exploits multi-threaded capabilities and data-parallelism. In order to take advantage of the high number of cores, a new mapping function is defined that enables GPU threads to determine the objects pair to compute without any global memory access. These new optimized GPU kernel functions use the thread indexes and turn them into a unique pair of objects to test. A square root approximation technique is used based on Newton’s estimation, enabling the threads to only perform a few atomic operations. A first characterization of the approximation errors is presented, enabling the fixing of incorrect computations. The I/O GPU streams are optimized using binary masks. The implementation and evaluation is made on largescale dynamic rigid body simulations. The increase in speed is highlighted over other recently proposed CPU and GPU-based techniques. The comparison shows that our system is, in most cases, faster than previous approaches. 1.
PRACTICAL PATIENT-SPECIFIC CARDIAC BLOOD FLOW SIMULATIONS USING SPH
"... While recent developments in the field of ventricular blood flow simulations have pushed modeling to increasingly high levels of accuracy, there has been a steep cost in computation time. Current state-of-the-art simulators take days to run, which is impractical for use in a clinical setting. In thi ..."
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While recent developments in the field of ventricular blood flow simulations have pushed modeling to increasingly high levels of accuracy, there has been a steep cost in computation time. Current state-of-the-art simulators take days to run, which is impractical for use in a clinical setting. In this paper, we describe novel adaptations of the SPH algorithm to this problem to achieve an order of magnitude faster performance, while maintaining accuracy in the flow. By constructing appropriate boundary particles and wall motion and adding a fast collision detection component to an existing SPH architecture, our system is able to simulate a cardiac cycle in as little as 30 minutes. This breakthrough will, in the near future, allow the useful simulation of blood flow and its related characterization for clinically useful applications. Index Terms — Blood flow, CT, cardiac, SPH 1. INTRODUCTION AND RELATED
COMPUTER GRAPHICS forum Efficient GPU data structures and methods to solve sparse linear systems in dynamics applications
"... Figure 1: Interactive simulations with our novel GPU-based solver. (From left to right) Simulations of complex articulated bodies; volumetric deformation with 10K quadratic finite elements and 47K degrees of freedom; volumetric deformation with 65K linear finite elements and 38K degrees of freedom a ..."
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Figure 1: Interactive simulations with our novel GPU-based solver. (From left to right) Simulations of complex articulated bodies; volumetric deformation with 10K quadratic finite elements and 47K degrees of freedom; volumetric deformation with 65K linear finite elements and 38K degrees of freedom and highly-detailed cloth models. We present GPU data structures and algorithms to efficiently solve sparse linear systems which are typically required in simulations of multibody systems and deformable bodies. Thereby, we introduce an efficient sparse matrix data structure that can handle arbitrary sparsity patterns and outperforms current state-of-the-art implementations for sparse matrix vector multiplication. Moreover, an efficient method to construct global matrices on the GPU is presented where hundreds of thousands of individual element contributions are assembled in a few milliseconds. A finite element based method for the simulation of deformable solids as well as an impulse-based method for rigid bodies are introduced in order to demonstrate the advantages of the novel data structures and algorithms. These applications share the characteristic that a major computational effort consists of building and solving systems of linear equations in every time step. Our solving method results in a speed-up factor of up to 13 in comparison to other GPU methods.
