دانلود رایگان مقاله روش موازی LU-SGS بر اساس معادله انتشار راکتور هسته ای در کلاستر GPU

Abstract

GPU not only is used in the field of graphic technology but also has been widely used in areas needing a large number of numerical calculations. In the energy industry, because of low carbon, high energy density, high duration and other characteristics, the development of nuclear energy cannot easily be replaced by other energy sources. Management of core fuel is one of the major areas of concern in a nuclear power plant, and it is directly related to the economic benefits and cost of nuclear power. The large-scale reactor core expansion equation is large and complicated, so the calculation of the diffusion equation is crucial in the core fuel management process. In this paper, we use CUDA programming technology on a GPU cluster to run the LU-SGS parallel iterative calculation against the background of the diffusion equation of the reactor. We divide one-dimensional and two-dimensional mesh into a plurality of domains, with each domain evenly distributed on the GPU blocks. A parallel collision scheme is put forward that defines the virtual boundary of the grid exchange information and data transmission by non-stop collision. Compared with the serial program, the experiment shows that GPU greatly improves the efficiency of program execution and verifies that GPU is playing a much more important role in the field of numerical calculations.

5. Conclusion and future work

By comparison, we can sum up the advantages of GPU for floating point computing and reasonable scheduling of memory. With a high-performance price ratio, the power of GPU floating point computing is approximately 10 times that of CPU, the bandwidth is 5 times that of CPU, but the cost of GPU is only 3–4 times that of CPU. In addition, GPU has good portability, ordinary desktop or notebook computers can support general floating point computing. Due to the characteristics of graphics processing and general computing, the results can be directly displayed by visual devices. One- or two-dimensional LU-SGS iteration in different memory indicates that the power of GPU for complex floating point computing is very strong. By analyzing the correlation between texture memory and the global memory execution rate and setting reasonable a kernel function, we reduce the scheduling overhead between blocks. GPU will play an increasingly large role in the field of mathematical computing for material and energy that require a large number of complex calculations. Future research may focus on the optimization of large-scale applications, algorithm and system structure, and speeding up the pace of GPU in the development of application software.