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Journal of Zhejiang University SCIENCE C 1998 Vol.-1 No.-1 P.

http://doi.org/10.1631/FITEE.2100284


TEES: a topology-aware execution environment service for fast and agile application deployment in HPC


Author(s):  Mingtian SHAO, Kai LU, Wanqing CHI, Ruibo WANG, Yiqin DAI, Wenzhe ZHANG

Affiliation(s):  College of Computer, National University of Defense Technology, Changsha 410073, China

Corresponding email(s):   lukainudt@163.com, zhangwenzhe@nudt.edu.cn

Key Words:  Execution environment, Application deployment, HPC, Container, P2P, Network topology


Mingtian SHAO, Kai LU, Wanqing CHI, Ruibo WANG, Yiqin DAI, Wenzhe ZHANG. TEES: a topology-aware execution environment service for fast and agile application deployment in HPC[J]. Frontiers of Information Technology & Electronic Engineering, 1998, -1(-1): .

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publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.2100284"
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Abstract: 
High-performance computing (HPC) systems are about to reach new heights: exascale. application deployment is becoming an increasingly prominent problem. container technology solves the problem of encapsulation and migration of applications and their execution environment. However, the container image is too large, and it is time-consuming to deploy on many compute nodes. Although the peer-to-peer (p2P) approach brings higher transmission efficiency, it introduces larger network loads. All of these issues lead to high startup latency of the application. To solve these problems, we propose the Topology-aware execution environment Service (TEES) for fast and agile application deployment on HPC systems. TEES creates a more lightweight execution environment for users, and uses a more efficient topology-aware p2P approach to reduce deployment time. Combined with a split-step transport and launch-in-advance mechanism, TEES reduces application startup latency. In the Tianhe HPC system, TEES realized the deployment and startup of a typical application on 17,560 compute nodes within 3 s. Compared to container-based application deployment, the speed is increased 12-fold, and the network load is reduced by 85%.

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