| Issue |
EPJ Web of Conf.
Volume 295, 2024
26th International Conference on Computing in High Energy and Nuclear Physics (CHEP 2023)
|
|
|---|---|---|
| Article Number | 02001 | |
| Number of page(s) | 6 | |
| Section | Online Computing | |
| DOI | https://doi.org/10.1051/epjconf/202429502001 | |
| Published online | 06 May 2024 | |
https://doi.org/10.1051/epjconf/202429502001
A High-Speed Asynchronous Data I/O Method for HEPS
1 Institute of High Energy Physics, CAS, 100049 Beijing, China
2 National Science Library, CAS, 100190 Beijing, China
3 University of Chinese Academy of Sciences, 100049 Beijing, China
4 TIANFU Cosmic Ray Research Center, 610041 Chengdu, Sichuan, China
* e-mail: fusy@ihep.ac.cn
Published online: 6 May 2024
The High Energy Photon Source (HEPS) is expected to produce a substantial volume of data, lead to immense data I/O pressure during computing. Inefficient data I/O can significantly impact computing performance.
To address this challenge, firstly, we have developed a data I/O framework for HEPS. This framework consists of three layers: data channel layer, distributed memory management layer, and I/O interface layer. It mask the underlying data differences in formats and sources, while implementing efficient I/O methods. Additionally, it supports both stream computing and batch computing.
Secondly, we have designed a data processing pipeline scheme aimed at reducing I/O latency and optimizing I/O bandwidth utilization during the processing of high-throughput data. This involves breaking down the computing task into several stages, including data loading, data pre-processing, data processing, and data writing, which are executed asynchronously and in parallel.
Finally, we introduce the design of stream data I/O process. The primary objective of stream data I/O is to enable real-time online processing of raw data, avoiding I/O bottlenecks caused by disk storage. This approach ensures the stability of data transmission and integrates distributed memory management to guarantee data integrity in memory.
© The Authors, published by EDP Sciences, 2024
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.