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Proceedings of the ACM SIGGRAPH/EUROGRAPHICS conference on Graphics hardware
FPGA Acceleration of Rigid Molecule Interactions
FCCM '04 Proceedings of the 12th Annual IEEE Symposium on Field-Programmable Custom Computing Machines
FFT and Convolution Performance in Image Filtering on GPU
IV '06 Proceedings of the conference on Information Visualization
Rigid molecule docking: FPGA reconfiguration for alternative force laws
EURASIP Journal on Applied Signal Processing
High performance discrete Fourier transforms on graphics processors
Proceedings of the 2008 ACM/IEEE conference on Supercomputing
Bandwidth intensive 3-D FFT kernel for GPUs using CUDA
Proceedings of the 2008 ACM/IEEE conference on Supercomputing
Drug design issues on the cell BE
HiPEAC'08 Proceedings of the 3rd international conference on High performance embedded architectures and compilers
Parameter Sweep Workflows for Modelling Carbohydrate Recognition
Journal of Grid Computing
Journal of Computational Physics
Analyzing program flow within a many-kernel OpenCL application
Proceedings of the Fourth Workshop on General Purpose Processing on Graphics Processing Units
Energy-aware metrics for benchmarking heterogeneous systems
ACM SIGMETRICS Performance Evaluation Review - Special issue on the 1st international workshop on performance modeling, benchmarking and simulation of high performance computing systems (PMBS 10)
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Accelerating dock6's amber scoring with graphic processing unit
ICA3PP'10 Proceedings of the 10th international conference on Algorithms and Architectures for Parallel Processing - Volume Part I
MEGADOCK-GPU: Acceleration of Protein-Protein Docking Calculation on GPUs
Proceedings of the International Conference on Bioinformatics, Computational Biology and Biomedical Informatics
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Modeling the interactions of biological molecules, or docking, is critical to both understanding basic life processes and to designing new drugs. Here we describe the GPU-based acceleration of a recently developed, complex, production docking code. We show how the various functions can be mapped to the GPU and present numerous optimizations. We find which parts of the problem domain are best suited to the different correlation methods. The GPU-accelerated system achieves a speedup of at least 17.7x with respect to a single core and 6.1x with respect to four cores for all likely problems sizes. This makes it competitive with FPGA-based systems for small molecule docking, and superior for proteinprotein docking.