Download e-book for iPad: GPU Solutions to Multi-scale Problems in Science and by Matthew G. Knepley, David A. Yuen (auth.), David A. Yuen,
By Matthew G. Knepley, David A. Yuen (auth.), David A. Yuen, Long Wang, Xuebin Chi, Lennart Johnsson, Wei Ge, Yaolin Shi (eds.)
This ebook covers the recent subject of GPU computing with many purposes concerned, taken from different fields corresponding to networking, seismology, fluid mechanics, nano-materials, data-mining , earthquakes ,mantle convection, visualization. it is going to convey the general public why GPU computing is critical and straightforward to take advantage of. it's going to provide a for the reason that GPU computing turns out to be useful and the way to enforce codes in a daily situation.
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Extra info for GPU Solutions to Multi-scale Problems in Science and Engineering
Zhoujun Liu, IACAS, China Particle-based Simulation of sand-ripples and Interaction with Obstacle using GPU technique Ms. Xiaohua Wan, ICTCAS, China Three-dimensional reconstruction of electron tomography using graphic processing units (GPUs) 28 Mr. Weile Jia, SCCAS, CNIC, China X. Lang et al. N-body simulation on GPU cluster Mr. Boris Galvan, Bonn University, German GPU simulation of evolving fracture networks in a poro-elastoplastic medium with pressure-dependent permeability Posters 4. July 29th: Sightseeing On the last day we went touring at three sites around greater Harbin, which included the tiger zoo, Russian Church and had Natural park on Sun Island by the river.
The most energy efficient GPU accelerated system achieved an efficiency of 958 MF/W, while the most energy efficient system using the CBE for acceleration achieved 773 MF/W (Homberg 2009). This system used an experimental interconnection network connecting nodes via the CBE internal high-speed bus. Non-accelerated systems using the latest generation IA-32 CPUs achieved an energy efficiency of about 350– 400 MF/W for HPL (Fig. 7). 3 GPU Integration and Programming Programming and code generation for both CPUs and GPUs today requires effective exploitation of parallelism for high efficiency.
Since matrix multiplication requires 2N3 operations but only 3N2 data elements need to be transferred, the data transfer time decreases in significance as N increases. The peak of the on GPU performance with CUBLAS is about 82 %, which drops to a peak of about 76 % if data transfers are included. These results are in agreement with the results reported in Phillips and Fatica (2010). Sparse Matrix-Vector Multiplication For sparse matrix-vector multiplication the results are shown in Fig. 19. 5 % of peak, a higher fraction of peak than on the CPU.
GPU Solutions to Multi-scale Problems in Science and Engineering by Matthew G. Knepley, David A. Yuen (auth.), David A. Yuen, Long Wang, Xuebin Chi, Lennart Johnsson, Wei Ge, Yaolin Shi (eds.)