天文学进展Issue(4)：469-478,10.DOI:10.3969/j.issn.1000-8349.2013.04.05
GPU并行计算技术在赫歇尔天文台远红外巡天数据处理中的应用
Applying GPU Parallel Computing Technologies to Process Herschel Far Infrared Galactic Plane Survey Data
摘要
Abstract
The Hi-GAL (Herschel infrared Galactic Plane Survey) images provide data with extraor-dinary spatial coverage and resolution for studying the FIR emission in the Galactic Plane. Graphics processing unit (GPU) parallel computing technologies are well suitable for accelerating processing and mining of this massive data. We illustrate the application of GPU parallel computing technolo-gies in two examples of Hi-GAL data processing. We spare the unnecessary physical details and focus on the method of using GPU in Herschel infrared data processing. <br> In the first example, we demonstrate a simple and straightforward application of GPU parallel computing technologies by fitting the far-infrared spectral energy distribution of the dust continuum emission in the Hi-GAL l = 30◦field. There are over 3 × 105 pixels in image of the l = 30◦field. The fitting procedure for every pixel is performed in parallel by a GPU. Comparing the time-cost for fitting the entire image, the acceleration factor of the build-in GPU on a low performance laptop is 68, and a specialized GPU is 5513 times faster than a Xeon E5620 with one core. <br> In the second example, we demonstrate a more sophisticated application of GPU parallel com-puting technologies. Based on the Hi-GAL l = 30◦field data, the distribution of molecular clouds derived from GRS (Galactic Ring Survey) data, and the properties of H II regions, we construct a 3D model of the interstellar medium to calculate the absorption of dust grains associated with molecular clouds. The resolution of the 3D model is 100 pc × 0.2◦× 0.2◦for a spherical grid. For this reso-lution, there are 4493 cells in total responsible for absorbing FUV photons. The absorption of these cells is calculated in parallel by a GPU. The resulted absorption is then compared with observations using Monte Carlo fitting method. In every iteration, CPU samples the free parameters and computes the goodness of fitting. The GPU part of the calculation is 95%of the total time. Comparing the time-cost for one iteration, NVIDIA C2075 GPU is 9535 times as fast as a Xeon E5620.关键词
GPU/并行计算/赫歇尔银道面红外巡天/数据处理方法Key words
GPU/parallel computing/Hi-GAL/data analysis分类
自科综合引用本文复制引用
朱佳丽,黄茂海..GPU并行计算技术在赫歇尔天文台远红外巡天数据处理中的应用[J].天文学进展,2013,(4):469-478,10.基金项目
中国科学院知识创新工程重要方向项目(KJCX2-YW-T20) （KJCX2-YW-T20）
