In this talk I will present a parallel prioritized Jacobian based inverse kinematics algorithm for multi-threaded architectures. The approach solves damped least squares inverse kinematics using a parallel line search by identifying and sampling critical input parameters. Parallel competing execution paths are spawned for each parameter in order to select the optimum which minimizes the error criteria. The algorithm is highly scalable and can handle complex articulated bodies at interactive frame rates. The results are shown on complex skeletons consisting of more than 600 degrees of freedom while being controlled using multiple end effectors. We implement our algorithm both on multi-core and GPU architectures and demonstrate how the GPU can further exploit fine-grain parallelism not directly available on a multicore processor. The implementations are 10 - 150 times faster compared to a state-of-art serial implementations while providing higher accuracy. We also demonstrate the scalability of the algorithm over multiple scenarios and explore the GPU implementation in detail.

Pawan Harish joined the PhD program at IIIT, Hyderabad, India in 2005 where he focused on Computational Displays and on parallelizing graph algorithms on the GPU under the supervision of Prof. P. J. Narayanan. He completed his PhD in 2013 and joined University of California, Irvine as a visiting scholar. He worked at Samsung Research India as a technical lead before joining IIG EPFL as a post doctoral researcher in June 2014. His current research, in association with Moka Studios, is on designing parallel inverse kinematics algorithm on the GPU. His interests include parallel algorithms, novel displays, CHI and computer graphics.

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