Abstract
High Energy Photon Source(HEPS) Experiment is expected to produce large amount of data and have diverse computing requirements for data analysis. Generally, scientists need to spend several days to setup their experimental environment, which greatly reduce the scientists’ work efficiency. In response to the above problems, we introduce a remote data analysis system for HEPS. The system provides users a web-based interactive interface based Jupyter, which makes scientists are able to process data analysis anytime and anywhere. Particularly, we discuss the system architecture as well as the key points of this system. A solution of managing and scheduling heterogeneous computing resources (CPU and GPU) is proposed, which adopts Kubernetes to achieve centralized heterogeneous resources management and resource expansion on demand. An improved Kubernetes resource scheduler is discussed, which dispatches upper applications to nodes combining with the computing cluster status. The system can transparently and quickly deploy the data analysis environment for users in seconds and reach the maximum resource utilization. We also introduce an automated deployment solution to improve the work efficiency of developers and help deploy multidisciplinary applications faster and automatically. A unified certification is illustrated to make sure the security of remote data access and data analysis. Finally, we will show the running status of the system.
Highlights
High Energy Photon Source (HEPS)[1] is the fourth-generation synchrotron radiation source with the highest spectral brightness in the world
This paper is organized as follows: The rest of Section 1 introduces an overview of HEPS computing requirements and dataflow
IHEP Single Sign On service (IHEPSSO) is an authentication system based on Oauth2.0[9], and users can access WEB-based information service as well as computing system after authentication
Summary
High Energy Photon Source (HEPS)[1] is the fourth-generation synchrotron radiation source with the highest spectral brightness in the world. In order to process such massive data in a timely manner, a large amount of heterogeneous computing resources such as CPU, GPU, FPGA are required to accelerate the calculation process. To address those issues, this paper proposed a web-based interactive data processing platform. Users can select application services through WEB browser, and make resource application and environment configuration through one-click action. This paper is organized as follows: The rest of Section 1 introduces an overview of HEPS computing requirements and dataflow.
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