M11---High-performance computing methods for computational genomics

2006 ◽  
Author(s):  
Srinivas Aluru ◽  
David Bader ◽  
Ananth Kalyanaraman
Keyword(s):  
High Performance Computing ◽  
High Performance ◽  
Computing Methods ◽  
Computational Genomics ◽  
Performance Computing

scholarly journals galapagos-c: analysis of galaxy morphologies using high-performance computing methods

2014 ◽  
Vol 444 (4) ◽  
pp. 3089-3117 ◽  
Author(s):  
Andreas Hiemer ◽  
Marco Barden ◽  
Lee S. Kelvin ◽  
Boris Häußler ◽  
Sabine Schindler
Keyword(s):  
High Performance Computing ◽  
High Performance ◽  
Computing Methods ◽  
Performance Computing

Comparison of Parallel Computing Methods for Fast Cone-Beam Reconstruction with Similar Optimization Strategies

2014 ◽  
Vol 519-520 ◽  
pp. 85-89
Author(s):  
Xiang Zhang ◽  
Bin Yan ◽  
Lei Li ◽  
Feng Zhang ◽  
Xiao Qi Xi ◽  
...  
Keyword(s):  
Parallel Computing ◽  
High Performance Computing ◽  
High Performance ◽  
Computing Methods ◽  
Experimental Results ◽  
Cone Beam ◽  
Cone Beam Reconstruction ◽  
Fdk Algorithm ◽  
Performance Computing

To investigate the performance of acceleration technologies for FDK algorithm, two of the most common high-performance computing hardware, multi-core CPU and GPU, are involved in our experiment. Both runtime and accuracy are regarded as the standards to evaluate the performance of four different programming methods: OpenMP, GLSL, CUDA and OpenCL. All the methods are estimated with comparable optimization strategies. The experimental results show that GPU has higher efficiency than multi-core CPU for fast cone-beam reconstruction, meanwhile CUDA is the best choice for programming on the multi-processor featured GPU.

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