当前位置: X-MOL 学术Astron. Comput. › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
Fast period searches using the Lomb–Scargle algorithm on Graphics Processing Units for large datasets and real-time applications
Astronomy and Computing ( IF 2.5 ) Pub Date : 2021-05-13 , DOI: 10.1016/j.ascom.2021.100472
M. Gowanlock , D. Kramer , D.E. Trilling , N.R. Butler , B. Donnelly

Computing the periods of variable objects is well-known to be computationally expensive. Modern astronomical catalogs contain a significant number of observed objects. Therefore, even if the period ranges for particular classes of objects are well-constrained due to expected physical properties, periods must be derived for a tremendous number of objects. In this paper, we propose a GPU-accelerated Lomb–Scargle period finding algorithm that computes periods for single objects or for batches of objects as is necessary in many data processing pipelines. We demonstrate the performance of several optimizations, including comparing the use of shared and global memory GPU kernels and using multiple CUDA streams to copy periodogram data from the GPU to the host. Also, we quantify the difference between 32-bit and 64-bit floating point precision on two classes of GPUs, and show that the performance degradation of using 64-bit over 32-bit is greater on the CPU than a GPU designed for scientific computing. We find that the GPU algorithm achieves superior performance over the baseline parallel CPU implementation, achieving a speedup of up to 174.53×. The Vera C. Rubin Observatory will carry out the Legacy Survey of Space and Time (LSST). We perform an analysis that shows we can derive the rotation periods of batches of Solar System objects at LSST scale in near real-time, which will be employed in a future LSST event broker. All source codes have been made publicly available.



中文翻译:

在图形处理单元上使用 Lomb-Scargle 算法对大型数据集和实时应用程序进行快速周期搜索

众所周知,计算变量对象的周期在计算上是昂贵的。现代天文目录包含大量观测对象。因此,即使特定类别对象的周期范围由于预期的物理特性而受到很好的约束,也必须为大量对象导出周期。在本文中,我们提出了一种 GPU 加速的 Lomb-Scargle 周期查找算法,该算法可以根据许多数据处理管道的需要计算单个对象或成批对象的周期。我们展示了几种优化的性能,包括比较共享和全局内存 GPU 内核的使用以及使用多个 CUDA 流将周期图数据从 GPU 复制到主机。还,我们量化了两类 GPU 上 32 位和 64 位浮点精度之间的差异,并表明在 CPU 上使用 64 位而不是 32 位的性能下降比专为科学计算设计的 GPU 更大。我们发现,GPU算法比基线并行CPU实施具有更高的性能,可将速度提高至174.53×. Vera C. Rubin 天文台将进行时空遗产调查 (LSST)。我们进行了一项分析,表明我们可以近实时地推导出 LSST 规模的太阳系对象批次的旋转周期,这将在未来的 LSST 事件代理中使用。所有源代码都已公开。

更新日期:2021-05-30
down
wechat
bug