Abstract:

[Objective] With the advancement of high-energy physics experiments and the development of advanced detectors, the generation of scientific big data has significantly increased. By analyzing and simulating these data, we can discover the laws of the universe and further explore the origins of the universe. [Context] The explosive growth of scientific data poses greater demands on the scale and performance of computing resources. For example, since the operation of the Large High Altitude Air Shower Observatory (LHAASO) in 2020, its cosmic ray event simulation has been running on an Intel X86 cluster. However, due to limited CPU resources, only a portion of the planned data for the first stage was produced. [Methods] Based on the demand for computing resources and changes in the international situation, we explore the application of heterogeneous computing service devices in the field of high-energy physics using an ARM architecture computing cluster located in Dongguan, Guangdong Province, China. [Results] This article builds a complete application ecosystem that supports offline data processing for high-energy physics, and ports the offline software of experiments based on the Square Kilometer Array (KM2A), Water Cherenkov Detector Array (WCDA), and Wide Field of View Cherenkov Telescope Array (WFCTA) to run on ARM machines. This article also develops data transfer and job scheduling strategies across different sites and heterogeneous computing clusters. Furthermore, this article evaluates the performance and power consumption differences of simulation jobs between Intel X86 and ARM clusters. [Conclusions] In this environment, the ported LHAASO simulation jobs can run correctly on the ARM computing cluster. Although the single-core performance of Intel X86 CPUs is better than that of ARM CPUs, for the entire server with a multi-core architecture, ARM servers provide better performance.

Key words: scientific big data, data processing, heterogeneous computing, ARM architecture