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TEES: a topology-aware execution environment service for fast and agile application deployment in HPC
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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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author="Mingtian SHAO, Kai LU, Wanqing CHI, Ruibo WANG, Yiqin DAI, Wenzhe ZHANG",
journal="Frontiers of Information Technology & Electronic Engineering",
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year="1998",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.2100284"
}
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%T TEES: a topology-aware execution environment service for fast and agile application deployment in HPC
%A Mingtian SHAO
%A Kai LU
%A Wanqing CHI
%A Ruibo WANG
%A Yiqin DAI
%A Wenzhe ZHANG
%J Journal of Zhejiang University SCIENCE C
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%@ 2095-9184
%D 1998
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.2100284
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A1 - Mingtian SHAO
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A1 - Wenzhe ZHANG
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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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