The Challenge of Providing a High-Level Programming Model for High-Performance Computing

2006 ◽  
pp. 21-49 ◽  
Author(s):  
Barbara Chapman
Keyword(s):  
High Performance Computing ◽  
High Performance ◽  
Programming Model ◽  
High Level ◽  
Performance Computing

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 Transparent fault tolerance for scalable functional computation

2016 ◽  
Vol 26 ◽  
Author(s):  
ROBERT STEWART ◽  
PATRICK MAIER ◽  
PHIL TRINDER
Keyword(s):  
Fault Tolerance ◽  
High Performance Computing ◽  
High Performance ◽  
Large Scale ◽  
Programming Model ◽  
Fault Tolerant ◽  
Fault Recovery ◽  
Actor Model ◽  
Work Stealing ◽  
Performance Computing

AbstractReliability is set to become a major concern on emergent large-scale architectures. While there are many parallel languages, and indeed many parallel functional languages, very few address reliability. The notable exception is the widely emulated Erlang distributed actor model that provides explicit supervision and recovery of actors with isolated state. We investigate scalable transparent fault tolerant functional computation with automatic supervision and recovery of tasks. We do so by developing HdpH-RS, a variant of the Haskell distributed parallel Haskell (HdpH) DSL with Reliable Scheduling. Extending the distributed work stealing protocol of HdpH for task supervision and recovery is challenging. To eliminate elusive concurrency bugs, we validate the HdpH-RS work stealing protocol using the SPIN model checker. HdpH-RS differs from the actor model in that its principal entities are tasks, i.e. independent stateless computations, rather than isolated stateful actors. Thanks to statelessness, fault recovery can be performed automatically and entirely hidden in the HdpH-RS runtime system. Statelessness is also key for proving a crucial property of the semantics of HdpH-RS: fault recovery does not change the result of the program, akin to deterministic parallelism. HdpH-RS provides a simple distributed fork/join-style programming model, with minimal exposure of fault tolerance at the language level, and a library of higher level abstractions such as algorithmic skeletons. In fact, the HdpH-RS DSL is exactly the same as the HdpH DSL, hence users can opt in or out of fault tolerant execution without any refactoring. Computations in HdpH-RS are always as reliable as the root node, no matter how many nodes and cores are actually used. We benchmark HdpH-RS on conventional clusters and an High Performance Computing platform: all benchmarks survive Chaos Monkey random fault injection; the system scales well e.g. up to 1,400 cores on the High Performance Computing; reliability and recovery overheads are consistently low even at scale.

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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