Fast and Scalable CPU/GPU Collision Detection for Rigid and Deformable Surfaces

Pabst, Simon; Koch, Artur; Straßer, Wolfgang

doi:10.1111/j.1467-8659.2010.01769.x

Cited by 76 publications

(59 citation statements)

References 23 publications

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“…Parallelizing the broad phase effectively is more complicated, so at present we opt for a simple staggering scheme that allows us to run a sequential broad phase while still making use of all available cores by allowing the simulation to proceed to the next rollback window in parallel. A number of better methods have been proposed for efficient parallel collision detection [Kim et al 2009;Pabst et al 2010;Tang et al 2010;Tang et al 2011], and we hope to incorporate this into our framework in the future. In our current implementation, whenever we need to perform collision detection, we run it in parallel with optimistic simulation of the next rollback window.…”

Section: Parallelizing Collision Detectionmentioning

confidence: 99%

Speculative parallel asynchronous contact mechanics

et al. 2012

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We extend the Asynchronous Contact Mechanics algorithm [Harmon et al. 2009] and improve its performance by two orders of magnitude, using only optimizations that do not compromise ACM's three guarantees of safety, progress, and correctness. The key to this speedup is replacing ACM's timid, forward-looking mechanism for detecting collisions-locating and rescheduling separating plane kinetic data structures-with an optimistic speculative method inspired by Mirtich's rigid body Time Warp algorithm [2000]. Time warp allows us to perform collision detection over a window of time containing many of ACM's asynchronous trajectory changes; in this way we cull away large intervals as being collision free. Moreover, by replacing force processing intermingled with KDS rescheduling by windows of pure processing followed by collision detection, we transform an algorithm that is very difficult to parallelize into one that is embarrassingly parallel.

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Section: Parallelizing Collision Detectionmentioning

confidence: 99%

Speculative parallel asynchronous contact mechanics

et al. 2012

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“…Avril et al (2011) presented a technique that dynamically adapt the first step (broad phase) of the collision detection process on GPU platform during simulation. Pabst et al (2010) proposed a hybrid CPU/GPU collision detection technique for rigid and deformable objects based on spatial subdivision. Liu et al (2010) demonstrated an algorithm of SAP for collision detection between very large numbers of moving bodies using GPUs.…”

Section: Discussionmentioning

confidence: 99%

FPGA-Based High-Performance Collision Detection: An Enabling Technique for Image-Guided Robotic Surgery

et al. 2016

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Collision detection, which refers to the computational problem of finding the relative placement or configuration of two or more objects, is an essential component of many applications in computer graphics and robotics. In image-guided robotic surgery, realtime collision detection is critical for preserving healthy anatomical structures during the surgical procedure. However, the computational complexity of the problem usually results in algorithms that operate at low speed. In this paper, we present a fast and accurate algorithm for collision detection between Oriented-Bounding-Boxes (OBBs) that is suitable for real-time implementation. Our proposed Sweep and Prune algorithm can perform a preliminary filtering to reduce the number of objects that need to be tested by the classical Separating Axis Test algorithm, while the OBB pairs of interest are preserved. These OBB pairs are re-checked by the Separating Axis Test algorithm to obtain accurate overlapping status between them. To accelerate the execution, our Sweep and Prune algorithm is tailor-made for the proposed method. Meanwhile, a high-performance scalable hardware architecture is proposed by analyzing the intrinsic parallelism of our algorithm and is implemented on FPGA platform. Results show that our hardware design on the FPGA platform can achieve around 8× higher running speed than the software design on a CPU platform. As a result, the proposed algorithm can achieve a collision frame rate of 1 kHz and fulfill the requirement for the medical surgery scenario of Robot-Assisted Laparoscopy.

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“…A survey on collision detection methods is available in [60] and GPU implementations in [69] and [96].…”

Section: Collision Detectionmentioning

confidence: 99%

A Survey on Parallel Computing and its Applications in Data-Parallel Problems Using GPU Architectures

Navarro

Hitschfeld

Mateu

2014

Commun. comput. phys.

161

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Abstract. Parallel computing has become an important subject in the field of computer science and has proven to be critical when researching high performance solutions. The evolution of computer architectures (multi-core and many-core) towards a higher number of cores can only confirm that parallelism is the method of choice for speeding up an algorithm. In the last decade, the graphics processing unit, or GPU, has gained an important place in the field of high performance computing (HPC) because of its low cost and massive parallel processing power. Super-computing has become, for the first time, available to anyone at the price of a desktop computer. In this paper, we survey the concept of parallel computing and especially GPU computing. Achieving efficient parallel algorithms for the GPU is not a trivial task, there are several technical restrictions that must be satisfied in order to achieve the expected performance. Some of these limitations are consequences of the underlying architecture of the GPU and the theoretical models behind it. Our goal is to present a set of theoretical and technical concepts that are often required to understand the GPU and its massive parallelism model. In particular, we show how this new technology can help the field of computational physics, especially when the problem is data-parallel. We present four examples of computational physics problems; n-body, collision detection, Potts model and cellular automata simulations. These examples well represent the kind of problems that are suitable for GPU computing. By understanding the GPU architecture and its massive parallelism programming model, one can overcome many of the technical limitations found along the way, design better GPU-based algorithms for computational physics problems and achieve speedups that can reach up to two orders of magnitude when compared to sequential implementations.

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Fast and Scalable CPU/GPU Collision Detection for Rigid and Deformable Surfaces

Cited by 76 publications

References 23 publications

Speculative parallel asynchronous contact mechanics

Speculative parallel asynchronous contact mechanics

FPGA-Based High-Performance Collision Detection: An Enabling Technique for Image-Guided Robotic Surgery

A Survey on Parallel Computing and its Applications in Data-Parallel Problems Using GPU Architectures

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