GPU Accelerated Reconstruction in Compton Scattering Tomography Using Matrix Compression

2014 ◽  
Vol 519-520 ◽  
pp. 102-107
Author(s):  
Yu Fei Yu ◽  
Bin Yan ◽  
Biao Wang ◽  
Lei Li ◽  
Yu Han ◽  
...  

An acceleration strategy for TV-ADM reconstruction algorithm in Compton scattering tomography (CST) is proposed. By analyzing the sparse characteristic of CST projection matrixes, firstly, the sparse matrix vector CSR format and ELL format are used to store them, which greatly reduce the memory consumption. Then, a Sparse Matrix Vector multiplication (SpMV) method is utilized to accelerate the projector and back projector process. Finally, based on the parallel features, the TV-ADM is computed with Graphics Processing Unit (GPU). Numerical experiments show that the TV-ADM with the presented acceleration strategy could achieve a 96 times speedup ratio and 224 times memory compression ratio without precision loss.

2013 ◽  
Vol 61 (4) ◽  
pp. 949-954 ◽  
Author(s):  
J. Gołębiowski ◽  
J. Forenc

Abstract Using models and algorithms presented in the first part of the article, a spatio-temporal distribution of the step response of a floor heater was determined. The results have been presented in the form of heating curves and temperature profiles of the heater in the selected time moments. The computations results were verified through comparing them with the solution obtained with the use of a commercial program - NISA. Additionally, the distribution of the average time constant of thermal processes occurring in the heater was determined. The analysis of the use of a graphics processing unit in numerical computations based on the conjugate gradient method was done. It was proved that the use of a graphics processing unit is profitable in the case of solving linear systems of equations with dense coefficient matrices. In the case of a sparse matrix, the speed-up depends on the number of its non-zero elements.


2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Ronglin Jiang ◽  
Shugang Jiang ◽  
Yu Zhang ◽  
Ying Xu ◽  
Lei Xu ◽  
...  

This paper introduces a (finite difference time domain) FDTD code written in Fortran and CUDA for realistic electromagnetic calculations with parallelization methods of Message Passing Interface (MPI) and Open Multiprocessing (OpenMP). Since both Central Processing Unit (CPU) and Graphics Processing Unit (GPU) resources are utilized, a faster execution speed can be reached compared to a traditional pure GPU code. In our experiments, 64 NVIDIA TESLA K20m GPUs and 64 INTEL XEON E5-2670 CPUs are used to carry out the pure CPU, pure GPU, and CPU + GPU tests. Relative to the pure CPU calculations for the same problems, the speedup ratio achieved by CPU + GPU calculations is around 14. Compared to the pure GPU calculations for the same problems, the CPU + GPU calculations have 7.6%–13.2% performance improvement. Because of the small memory size of GPUs, the FDTD problem size is usually very small. However, this code can enlarge the maximum problem size by 25% without reducing the performance of traditional pure GPU code. Finally, using this code, a microstrip antenna array with16×18elements is calculated and the radiation patterns are compared with the ones of MoM. Results show that there is a well agreement between them.


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