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Environment setup Figure 1 shows the programming structure we have decided to follow. The platform selected is WIN32 1 because it was the first platform to offer SLI enabled drivers. As of today, neither the
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We present the work in progress of a collision detection (CD) algorithm on a multi-threaded environment and based on two graphics processor units (GPUs); the first GPU is used as a normal graphics processor, and the second GPU is doubled as a 'collision detection' coprocessor. Our approach makes use of the stream parallel engine provided by modern...
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... section presents an overview of the system setup followed by a set of basic assumptions for our approach and a detailed description of the Multi-pass Multi-stage Multi-GPU Collision Detection or M 3 CD algorithm Figure 1 shows the programming structure we have decided to follow. The platform selected is WIN32 1 because it was the first platform to offer SLI enabled drivers. ...
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Citations
... GPU-based algorithms for self-collision and cloth animation have also been introduced by Govindaraju et al. [GLM05a,GLM05b]. Juarez-Comboni et al. [JMJC05] describe the use of several GPUs during collision detection process. One GPU is in charge of the collision detection process using a simple boundary volume collision query. ...
... GPU-based algorithms for self-collision and cloth animation have also been introduced by Govindaraju et al. [11,12]. An article describes the use of several GPUs during collision detection process where only one GPU is in charge of the physical pipeline, the other ones are in charge of the rendering operations [18]. Recent works use thread and data parallelism on a single GPU to perform fast hierarchy construction, updating, and traversal using tight-fitting bounding volumes such as oriented bounding boxes (OBB) and rectangular swept spheres (RSS) [24]. ...
Figure 1: A sample of environments used to perform tests and to compare algorithmic performances. ABSTRACT We present a first parallel and adaptive collision detection pipeline running on a multi-core architecture. This pipeline integrates a first global synchronization-free parallelization of its major steps and enables to dynamically adapt the parallelism repartition during the simulation. We propose to break the sequentiality of the pipeline by simultaneously executing the two main phases (broad and narrow). We introduce and use a new buffer structure to share objects pairs between threads. To fully exploit multi-core performance, we propose a new dynamic load balancing technique to distribute threads among phases of the pipeline. This dynamic threads balancing acts on the broad and narrow phases in relation to their computation time. This technique favors the longest phase by giving it more CPU threads to run in parallel. Results show that this new generation of parallel pipeline enables to adapt computations to the simulation scenario evolution and to the run-time architecture. We tested our solution on a 8*cores architecture and performance measurements show that this first parallel pipeline is well-suited for the collision detection problem and enables to significantly reduce computation time compared to the sequential one.
Continuing advances in virtual reality (VR) technology with respect to the new addition of force and touch feedbacks have enhanced VR realism and led to the development of many useful and accessible VR systems. One of the emerging research fields is in rehabilitation training. This paper introduces a virtual reality-based hand manipulation training system with three applications: virtual writing, virtual painting and virtual dialing. The system is mainly for training hand movement precision, speed, force, and direction control. A haptic device — PHANTOM Premium 1.0 is used to give the user immediate force feedbacks to feel immersed in the virtual environment during the training session. A new collision detection method is developed for accurate and rapid calculation of the interaction between the haptic and virtual environments. The implementation performances are calculated and given to the user in real time. The practicing results are also saved for evaluation and supervision by the specialist.
