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Validation of Intelligent Integrated Management Platform Capabilities based on a Large Virtual HPC Testbed
Seungwoo Rho, Jinseung Ryu, Sangwan Kim, Kwang Jin Oh, MyoungHwan Yoo
http://doi.org/10.5626/JOK.2022.49.4.276
This paper introduces an intelligent integrated management platform developed by itself to manage high-performance computers equipped with board management controller (BMC) functions, and presents large-scale virtual High Performance Computing (HPC) testbeds and experimental results to verify this platform. Intelligent integrated management platforms can monitor and control the hardware sensors of existing high-performance computers using an Intelligent Platform Management Interface (IPMI) to communicate with the BMC. In addition, a separate agent module operated within the controller was developed and applied to expand the function and performance of the BMC in a high performance computer developed in Korea. In this paper, we introduced an intelligent integrated management platform, built 1,200 virtual HPC testbeds, and verified their functions after linking them to the same integrated management platform as the actual physical server.
Enhancing the Performance of Multiple Parallel Applications using Heterogeneous Memory on the Intel"s Next-Generation Many-core Processor
Seungwoo Rho, Seoyoung Kim, Dukyun Nam, Geunchul Park, Jik-Soo Kim
http://doi.org/10.5626/JOK.2017.44.9.878
This paper discusses performance bottlenecks that may occur when executing high-performance computing MPI applications in the Intel’s next generation many-core processor called Knights Landing(KNL), as well as effective resource allocation techniques to solve this problem. KNL is composed of a host processor to enable self-booting in addition to an existing accelerator consisting of a many-core processor, and it was released with a new type of on-package memory with improved bandwidth on top of existing DDR4 based memory. We empirically verified an improvement of the execution performance of multiple MPI applications and the overall system utilization ratio by studying a resource allocation method optimized for such new many-core processor architectures.
Effective Distributed Supercomputing Resource Management for Large Scale Scientific Applications
Seungwoo Rho, Jik-Soo Kim, Sangwan Kim, Seoyoung Kim, Soonwook Hwang
Nationwide supercomputing infrastructures in Korea consist of geographically distributed supercomputing clusters. We developed High-Throughput Computing as a Service(HTCaaS) based on these distributed national supecomputing clusters to facilitate the ease at which scientists can explore large-scale and complex scientific problems. In this paper, we present our mechanism for dynamically managing computing resources and show its effectiveness through a case study of a real scientific application called drug repositioning. Specifically, we show that the resource utilization, accuracy, reliability, and usability can be improved by applying our resource management mechanism. The mechanism is based on the concepts of waiting time and success rate in order to identify valid computing resources. The results show a reduction in the total job completion time and improvement of the overall system throughput.
A Case Study of Drug Repositioning Simulation based on Distributed Supercomputing Technology
Jik-Soo Kim, Seungwoo Rho, Minho Lee, Seoyoung Kim, Sangwan Kim, Soonwook Hwang
In this paper, we present a case study for a drug repositioning simulation based on distributed supercomputing technology that requires highly efficient processing of large-scale computations. Drug repositioning is the application of known drugs and compounds to new indications (i.e., new diseases), and this process requires efficient processing of a large number of docking tasks with relatively short per-task execution times. This mechanism shows the main characteristics of a Many-Task Computing (MTC) application, and as a representative case of MTC applications, we have applied a drug repositioning simulation in our HTCaaS system which can leverage distributed supercomputing infrastructure, and show that efficient task dispatching, dynamic resource allocation and load balancing, reliability, and seamless integration of multiple computing resources are crucial to support these challenging scientific applications.
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