Long Distance Geographically Distributed InfiniBand Based Computing

K. Niedzielewski, Marcin Semeniuk, Jaroslaw Skomial, J. Proficz, Piotr Sumioka, Bartosz Pliszka, M. Michalewicz
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Abstract

Collaboration between multiple computing centres, referred as federated computing is becoming important pillar of High Performance Computing (HPC) and will be one of its key components in the future. To test technical possibilities of future collaboration using 100Gb optic fiber link (Connection was 900 km in length with 9ms RTT time) we prepared two scenarios of operation. In the first one, Interdisciplinary Centre for Mathematical and Computational Modelling (ICM) in Warsaw and Centre of Informatics - Tricity Academic Supercomputer & networK (CI-TASK) in Gdansk prepared a long distance geographically distributed computing cluster. System consisted of 14 nodes (10 nodes at ICM facility and 4 at TASK facility) connected using InfiniBand. Our tests demonstrate that it is possible to perform computationally intensive data analysis on systems of this class without substantial drop in performance for a certain type of workloads. Additionally, we show that it is feasible to use High Performance Parallex [1], high level abstraction libraries for distributed computing, to develop software for such geographically distributed computing resources and maintain desired efficiency. In the second scenario, we prepared distributed simulation-postprocessing-visualization workflow using ADIOS2 [2] and two programming languages (C++ and python). In this test we prove capabilities of performing different parts of analysis in seperate sites.
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远距离地理分布的InfiniBand计算
多个计算中心之间的协作,称为联邦计算,正在成为高性能计算(HPC)的重要支柱,并将成为其未来的关键组成部分之一。为了测试使用100Gb光纤链路(连接长度为900公里,RTT时间为9ms)进行未来合作的技术可能性,我们准备了两种操作方案。在第一个项目中,华沙的跨学科数学和计算建模中心(ICM)和格但斯克的信息学中心-三重学术超级计算机和网络(CI-TASK)准备了一个远距离地理分布式计算集群。系统由14个节点组成(10个节点在ICM设施,4个节点在TASK设施),使用InfiniBand连接。我们的测试表明,对于特定类型的工作负载,在此类系统上执行计算密集型数据分析而不会导致性能大幅下降是可能的。此外,我们表明,使用高性能并行[1],分布式计算的高级抽象库,为这种地理上分布式的计算资源开发软件并保持所需的效率是可行的。在第二种场景中,我们使用ADIOS2[2]和两种编程语言(c++和python)编写了分布式仿真-后处理-可视化工作流。在这个测试中,我们证明了在不同的地点执行不同部分分析的能力。
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