An Enhanced Parallel Loop Self-Scheduling Scheme for Cluster Environments
The Journal of Supercomputing
Locality and Loop Scheduling on NUMA Multiprocessors
ICPP '93 Proceedings of the 1993 International Conference on Parallel Processing - Volume 02
Speeding up Mutual Information Computation Using NVIDIA CUDA Hardware
DICTA '07 Proceedings of the 9th Biennial Conference of the Australian Pattern Recognition Society on Digital Image Computing Techniques and Applications
Dynamic partitioning of loop iterations on heterogeneous PC clusters
The Journal of Supercomputing
Computer simulation of intracardiac potential with whole-heart model
International Journal of Bioinformatics Research and Applications
Parallel Loop Self-Scheduling for Heterogeneous Cluster Systems with Multi-core Computers
APSCC '08 Proceedings of the 2008 IEEE Asia-Pacific Services Computing Conference
IEEE Transactions on Parallel and Distributed Systems
A Compute Unified System Architecture for Graphics Clusters Incorporating Data Locality
IEEE Transactions on Visualization and Computer Graphics
Accelerating large graph algorithms on the GPU using CUDA
HiPC'07 Proceedings of the 14th international conference on High performance computing
Toward real-time simulation of cardiac dynamics
Proceedings of the 9th International Conference on Computational Methods in Systems Biology
Parallel evolutionary computation in bioinformatics applications
Computer Methods and Programs in Biomedicine
GPU-based acceleration of an RNA tertiary structure prediction algorithm
Computers in Biology and Medicine
Computer Methods and Programs in Biomedicine
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Biological computations like electrocardiological modelling and simulation usually require high-performance computing environments. This paper introduces an implementation of parallel computation for computer simulation of electrocardiograms (ECGs) in a personal computer environment with an Intel CPU of Core (TM) 2 Quad Q6600 and a GPU of Geforce 8800GT, with software support by OpenMP and CUDA. It was tested in three parallelization device setups: (a) a four-core CPU without a general-purpose GPU, (b) a general-purpose GPU plus 1 core of CPU, and (c) a four-core CPU plus a general-purpose GPU. To effectively take advantage of a multi-core CPU and a general-purpose GPU, an algorithm based on load-prediction dynamic scheduling was developed and applied to setting (c). In the simulation with 1600 time steps, the speedup of the parallel computation as compared to the serial computation was 3.9 in setting (a), 16.8 in setting (b), and 20.0 in setting (c). This study demonstrates that a current PC with a multi-core CPU and a general-purpose GPU provides a good environment for parallel computations in biological modelling and simulation studies.