scholarly journals Comparison of High-performance Computing Approaches in the Python Environment for a Five-point Stencil Test Problem

2021 ◽  
Author(s):  
Eduardo F. Miranda ◽  
Stephan Stephany
Keyword(s):  
High Performance Computing ◽  
High Performance ◽  
Test Problem ◽  
Specific Test ◽  
Multiple Cores ◽  
Fortran 90 ◽  
High Level ◽  
Python Programming ◽  
Performance Computing ◽  
Point Stencil

Several of the most important high-performance computing approaches available in the Python programming environment of the LNCC Santos Dumont supercomputer, are compared using a specific test problem. Python includes specific libraries, implementations, development tools, documentation, optimization and parallelization resources. It provides a straightforward way to program using a high level of abstraction, but the parallelization features for exploring multiple cores, processors, or accelerators such as GPUs, are diverse and may not be easily chosen by the user. Serial and parallel implementations of a test problem in Fortran 90 are taken as benchmarks to compare performance. This work is a primer for the use of HPC resources in Python.

HPC cloud services based on the Proxmox VE platform

2019 ◽  
Author(s):  
А.В. Баранов ◽  
Е.А. Киселёв
Keyword(s):  
High Performance Computing ◽  
Management System ◽  
High Performance ◽  
Virtual Machines ◽  
Cloud Services ◽  
Job Management ◽  
Job Management System ◽  
High Level ◽  
Performance Computing ◽  
Hpc Cloud

Организация облачных сервисов для высокопроизводительных вычислений затруднена, во-первых, по причине высоких накладных расходов на виртуализацию, во-вторых, из-за специфики систем управления заданиями и ресурсами в научных суперкомпьютерных центрах. В настоящей работе рассмотрен подход к построению облачных сервисов видов PaaS и SaaS, основанных на совместном функционировании облачной платформы Proxmox VE и системы управления прохождением параллельных заданий, применяемой в качестве менеджера ресурсов в Межведомственном суперкомпьютерном центре РАН. Purpose. The purpose of this paper is to develop methods and technologies for building high-performance computing cloud services in scientific supercomputer centers. Methodology.To build a cloud environment for high-performance scientific calculations (HPC), the corresponding three-level model and the method of combining flows of supercomputer tasks of various types were applied. Results.A high-level HPC cloud services technology based on the free Proxmox VE software platform has been developed. The Proxmox VE platform has been integrated with the domestic supercomputer job management system called SUPPZ. Experimental estimates of the overheads introduced in the high-performance computing process by the Proxmox components are obtained. Findings.An approach to the integration a supercomputer job management system and a virtualization platform is proposed. The presented approach is based on the representation of the supercomputer jobs as virtual machines or containers. Using the Proxmox VE platform as an example, the influence of a virtual environment on the execution time of parallel programs is investigated experimentally. The possibility of applying the proposed approach to building cloud services of the PaaS and SaaS type in scientific supercomputing centers of collective use is substantiated for a class of applications for which the overhead costs introduced by the Proxmox components are acceptable.

scholarly journals Artificial Intelligence: An Energy Efficiency Tool for Enhanced High performance computing

Symmetry ◽  
2020 ◽  
Vol 12 (6) ◽  
pp. 1029
Author(s):  
Anabi Hilary Kelechi ◽  
Mohammed H. Alsharif ◽  
Okpe Jonah Bameyi ◽  
Paul Joan Ezra ◽  
Iorshase Kator Joseph ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Energy Efficiency ◽  
High Performance Computing ◽  
High Performance ◽  
Large Data ◽  
Computing Systems ◽  
Computing Power ◽  
Product Delivery ◽  
High Level ◽  
Performance Computing

Power-consuming entities such as high performance computing (HPC) sites and large data centers are growing with the advance in information technology. In business, HPC is used to enhance the product delivery time, reduce the production cost, and decrease the time it takes to develop a new product. Today’s high level of computing power from supercomputers comes at the expense of consuming large amounts of electric power. It is necessary to consider reducing the energy required by the computing systems and the resources needed to operate these computing systems to minimize the energy utilized by HPC entities. The database could improve system energy efficiency by sampling all the components’ power consumption at regular intervals and the information contained in a database. The information stored in the database will serve as input data for energy-efficiency optimization. More so, device workload information and different usage metrics are stored in the database. There has been strong momentum in the area of artificial intelligence (AI) as a tool for optimizing and processing automation by leveraging on already existing information. This paper discusses ideas for improving energy efficiency for HPC using AI.

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