Parallel optimization of the pulsar search pipeline

The connection between astronomy and high-performance computing is becoming stronger with the development of innovative observing facilities such as the Square Kilometre Array (SKA) and the proposed innovative platform for big data and high-performance computing. Astronomical computation is characterized by a large data volume and massive parallelism, particularly for pulsar search, a leading scientific direction of the SKA. In this study, we present an approach for accelerating the pulsar search pipeline based on OpenMP and multiprocessing techniques. We propose a method for solving the load imbalance problem and have successfully installed the pipeline on x86 and ARM compute nodes on the China SKA Regional Centre prototype (CSRC-P). The performance evaluation from tests on the Murchison Widefield Array (MWA) VCS observations shows that our optimization method works well on the x86 and ARM nodes, improving the relative speedup by factors of 10.4-12.2 and 24.5-25.8, respectively, compared with the original single-thread approach. The ARM platform was 1.1-1.3 times faster than the x86 platform in the tested cases, showing its great potential for SKA data processing. This optimized pulsar search pipeline deployed on the CSRC-P will be used for the pulsar survey of the southern-sky MWA rapid two-meter program for various scientific goals, including pulsar timing arrays for gravitational wave detection.