Interactive rigid body simulation is an important part of many modern computer tools, which no authoring tool nor game engine can do without. Such high-performance computer tools open up new possibilities for changing how designers, engineers, modelers and animators work with their design problems. This paper is a self contained state-of-the-art report on the physics, the models, the numerical methods and the algorithms used in interactive rigid body simulation all of which have evolved and matured over the past 20 years. Furthermore, the paper communicates the mathematical and theoretical details in a pedagogical manner. This paper is not only a stake in the sand on what has been done, it also seeks to give the reader deeper insights to help guide their future research.

Interactive Simulation of Rigid Body Dynamics in Computer Graphics

FPGAs (Field Programmable Gate Arrays) are making their way into data centers (DCs) and are used to offload and accelerate specific services, but they are not yet available to cloud users. This puts the cloud deployment of compute-intensive workloads at a disadvantage compared with on-site infrastructure installations, where the performance and energy efficiency of FPGAs are increasingly being exploited for application-specific accelerators and heterogeneous computing. The cloud is housed in DCs, and DCs are based on ever shrinking servers. Today, we observe the emergence of hyper scale data centers, which are based on densely packaged servers. The shrinking form factor brings the potential to deploy FPGAs on a large scale in such DCs. These FPGAs must be deployed as independent DC resources, and they must be accessible to the cloud users. Therefore, we propose to change the traditional paradigm of the CPU-FPGA interface by decoupling the FPGA from the CPU and connecting the FPGA as a standalone resource to the DC network. This allows cloud vendors to offer an FPGA to users in a similar way as a standard server. As existing infrastructure-as-a-service (IaaS) mechanisms are not suitable, we propose a new Open Stack (open source cloud computing software) service to integrate FPGAs in the cloud. This proposal is complemented by a framework that enables cloud users to combine multiple FPGAs into a programmable fabric. The proposed architecture and framework address the scalability problem that makes it difficult to provision large numbers of FPGAs. Together, they offer a novel solution for processing large and heterogeneous data sets in the cloud.

Enabling FPGAs in Hyperscale Data Centers

Dynamics analysis and GNC design of flexible systems for space debris active removal

We describe a process that constructs robot-specific circuitry for motion planning, capable of generating motion plans approximately three orders of magnitude faster than existing methods. Our method is based on building collision detection circuits for a probabilistic roadmap. Collision detection for the roadmap edges is completely parallelized, so that the time to determine which edges are in collision is independent of the number of edges. We demonstrate planning using a 6-degreeof-freedom robot arm in less than 1 millisecond.

/pdf/robot-motion-planning-on-a-chip-45kjtqssni.pdf

Robot Motion Planning on a Chip

We cull collisions between very large numbers of moving bodies using graphics processing units (GPUs) To perform massively parallel sweep-and-prune (SaP), we mitigate the great density of intervals along the axis of sweep by using principal component analysis to choose the best sweep direction, together with spatial subdivisions to further reduce the number of false positive overlaps Our algorithm implemented entirely on GPUs using the CUDA framework can handle a million moving objects at interactive rates As application of our algorithm, we demonstrate the real-time simulation of very large numbers of particles and rigid-body dynamics

Real-time collision culling of a million bodies on graphics processing units

Broad phase collision detection is a vital task in most interactive simulations, but it remains computationally expensive and is frequently an impediment to efficient implementation of realtime graphics applications. To overcome this hurdle, we propose a novel microarchitecture for performing broad phase collision detection using Axis-Aligned Bounding Boxes (AABBs), which exploits the parallelism available in the algorithms. We have implemented our microarchitecture on a Field-Programmable Gate Array (FPGA) and our results show that this implementation is capable of achieving an acceleration of up to 1.5× over the broad phase component of the SOLID collision detection library, when considering the communication overhead between the CPU and the FPGA. Our results further indicate that significantly higher accelerations are achievable using a more sophisticated FPGA or by implementing our microarchitecture on an Application-Specific Integrated Circuit (ASIC).

http://www.tara.tcd.ie/bitstream/handle/2262/36011/vriphys.pdf;sequence=1

Hardware Accelerated Broad Phase Collision Detection for Realtime Simulations

Collision detection is a vital component of applications spanning myriad fields, yet there exists no means for developers to analyse the suitability of their collision detection algorithms across the spectrum of scenarios that could be encountered. To rectify this, we propose a framework for benchmarking interactive collision detection, which consists of a single generic benchmark that can be adapted using a number of parameters to create a large range of practical benchmarks. This framework allows algorithm developers to test the validity of their algorithms across a wide test space and allows developers of interactive applications to recreate their application scenarios and quickly determine the most amenable algorithm. To demonstrate the utility of our framework, we adapted it to work with three collision detection algorithms supplied with the Bullet Physics SDK. Our results demonstrate that those algorithms conventionally believed to offer the best performance are not always the correct choice. This demonstrates that conventional wisdom cannot be relied on for selecting a collision detection algorithm and that our benchmarking framework fulfils a vital need in the collision detection community. The framework has been made open source, so that developers do not have to reprogram the framework to test their own algorithms, allowing for consistent results across different algorithms and reducing development time.

/pdf/a-framework-for-benchmarking-interactive-collision-detection-10op83fv84.pdf

A framework for benchmarking interactive collision detection

We present a hybrid system spanning a fixed-function microarchitecture and a general-purpose microprocessor, designed to amplify the throughput and decrease the power dissipation of collision detection relative to what can be achieved using CPUs or GPUs alone. The primary component is one of the two novel microarchitectures designed to perform the principal elements of broad-phase collision detection. Both microarchitectures consist of pipelines comprising a plurality of memories, which rearrange the input into a format that maximises parallelism and bandwidth. The two microarchitectures are combined with the remainder of the system through an original method for sharing data between a ray tracer and the collision-detection microarchitectures to minimise data structure construction costs. We effectively demonstrate our system using several benchmarks of varying object counts. These benchmarks reveal that, for over one million objects, our design achieves an acceleration of 812 × relative to a CPU and an acceleration of 161 × relative to a GPU. We also achieve energy efficiencies that enable the mitigation of silicon power-density challenges, while making the design amenable to both mobile and wearable computing devices.

/pdf/a-hybrid-fixed-function-and-microprocessor-solution-for-high-4zmkw3m6r9.pdf

A hybrid fixed-function and microprocessor solution for high-throughput broad-phase collision detection

embark initiative Investing in People and Ideas Algorithm Myriad broad phase collision detection data structures and algorithms exist. We chose the simplest data structure, the Axis-Aligned Bounding Box (AABB), as its simplicity permits efficient hardware implementation. In software, AABBs are typically tested for collision using a sweep and prune algorithm (Cohen, Lin, Manocha & Ponamgi 1995), which achieves efficiency by considering spatial coherence between frames. However, we chose the more elementary O(n2) brute force method as it permits a more optimized hardware implementation. The brute force algorithm is Input: n AABBs Output: Indices of colliding AABB pairs

http://www.tara.tcd.ie/bitstream/handle/2262/36007/siggraph.pdf;sequence=1

Hardware accelerated broad phase collision detection

Towards a Field-Programmable Physics Processor (FP?)

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