Interactive motion generation from examples
Proceedings of the 29th annual conference on Computer graphics and interactive techniques
Motion synthesis from annotations
ACM SIGGRAPH 2003 Papers
Scan primitives for GPU computing
Proceedings of the 22nd ACM SIGGRAPH/EUROGRAPHICS symposium on Graphics hardware
MapReduce: simplified data processing on large clusters
Communications of the ACM - 50th anniversary issue: 1958 - 2008
GPU-Quicksort: A practical Quicksort algorithm for graphics processors
Journal of Experimental Algorithmics (JEA)
Accelerator-Oriented Algorithm Transformation for Temporal Data Mining
NPC '09 Proceedings of the 2009 Sixth IFIP International Conference on Network and Parallel Computing
Accelerating SQL database operations on a GPU with CUDA
Proceedings of the 3rd Workshop on General-Purpose Computation on Graphics Processing Units
Towards chip-on-chip neuroscience: fast mining of neuronal spike streams using graphics hardware
Proceedings of the 7th ACM international conference on Computing frontiers
Debunking the 100X GPU vs. CPU myth: an evaluation of throughput computing on CPU and GPU
Proceedings of the 37th annual international symposium on Computer architecture
Multi-GPU volume rendering using MapReduce
Proceedings of the 19th ACM International Symposium on High Performance Distributed Computing
Mars: Accelerating MapReduce with Graphics Processors
IEEE Transactions on Parallel and Distributed Systems
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Many-core architecture has become an emerging and widely adopted platform for parallel computing. Computer animation researches can harness this advance in high performance computing with better understanding of the architecture and careful consideration of several important parallel algorithm design issues, such as computation-to-core mapping, load balancing and algorithm design paradigms. In this paper, we use a set of algorithms in computer animation as the examples to illustrate these issues, and provide possible solutions for handling them. We have shown in our previous research projects that the proposed solutions can greatly enhance the performance of the parallel algorithms.