scholarly journals The impact of high-performance computing in the solution of linear systems: trends and problems

2000 ◽  
Vol 123 (1-2) ◽  
pp. 515-530 ◽  
Author(s):  
Iain S. Duff
Keyword(s):  
High Performance Computing ◽  
Linear Systems ◽  
High Performance ◽  
The Impact ◽  
Performance Computing

scholarly journals Computational experiment in era of HPC

2019 ◽  
Vol 27 (3) ◽  
pp. 263-267
Author(s):  
Alexander S. Ayriyan
Keyword(s):  
Parallel Computing ◽  
Life Cycle ◽  
High Performance Computing ◽  
High Performance ◽  
Computational Experiment ◽  
Computing Systems ◽  
The Impact ◽  
Performance Computing

In this note we discuss the impact of development of architecture and technology of parallel computing on the typical life-cycle of the computational experiment. In particular, it is argued that development and installation of high-performance computing systems is indeed important itself regardless of specific scientific tasks, since the presence of cutting-age HPC systems within an academic infrastructure gives wide possibilities and stimulates new researches.

scholarly journals Computational experiment in era of HPC

2019 ◽  
Vol 27 (3) ◽  
pp. 263-267
Author(s):  
Alexander S. Ayriyan
Keyword(s):  
Parallel Computing ◽  
Life Cycle ◽  
High Performance Computing ◽  
High Performance ◽  
Computational Experiment ◽  
Computing Systems ◽  
The Impact ◽  
Performance Computing

In this note we discuss the impact of development of architecture and technology of parallel computing on the typical life-cycle of the computational experiment. In particular, it is argued that development and installation of high-performance computing systems is indeed important itself regardless of specific scientific tasks, since the presence of cutting-age HPC systems within an academic infrastructure gives wide possibilities and stimulates new researches.

The Impact of Virtualization on High Performance Computing Clustering in the Cloud

2015 ◽  
Vol 6 (4) ◽  
pp. 65-81 ◽  
Author(s):  
Ouidad Achahbar ◽  
Mohamed Riduan Abid
Keyword(s):  
Big Data ◽  
High Performance Computing ◽  
High Performance ◽  
Virtual Machines ◽  
Performance Trends ◽  
Tightly Coupled ◽  
Physical Clusters ◽  
User Friendly ◽  
The Impact ◽  
Performance Computing

The ongoing pervasiveness of Internet access is intensively increasing Big Data production. This, in turn, increases demand on compute power to process this massive data, and thus rendering High Performance Computing (HPC) into a high solicited service. Based on the paradigm of providing computing as a utility, the Cloud is offering user-friendly infrastructures for processing Big Data, e.g., High Performance Computing as a Service (HPCaaS). Still, HPCaaS performance is tightly coupled with the underlying virtualization technique since the latter is responsible for the creation of virtual machines that carry out data processing jobs. In this paper, the authors evaluate the impact of virtualization on HPCaaS. They track HPC performance under different Cloud virtualization platforms, namely KVM and VMware-ESXi, and compare it against physical clusters. Each tested cluster provided different performance trends. Yet, the overall analysis of the findings proved that the selection of virtualization technology can lead to significant improvements when handling HPCaaS.

Design tools for electromagnetic-driven multi-physics systems using high performance computing

2019 ◽  
Vol 39 (1) ◽  
pp. 198-205
Author(s):  
David Lowther ◽  
Vahid Ghorbanian ◽  
Mohammad Hossain Mohammadi ◽  
Issah Ibrahim
Keyword(s):  
High Performance Computing ◽  
High Performance ◽  
Complex Analysis ◽  
Job Scheduling ◽  
Computing System ◽  
Electromagnetic System ◽  
Content Type ◽  
Mechanical Coupling ◽  
The Impact ◽  
Performance Computing

Purpose The design of electromagnetic systems for a variety of applications such as induction heating, electrical machines, actuators and transformers requires the solution of a multi-physics problem often involving thermal, structural and mechanical coupling to the electromagnetic system. This results in a complex analysis system embedded within an optimization process. The appearance of high-performance computing systems over the past few years has made coupled simulations feasible for the design engineer. When coupled with surrogate modelling techniques, it is possible to significantly reduce the wall clock time for generating a complete design while including the impact of the multi-physics performance on the device. Design/methodology/approach An architecture is proposed for linking multiple singe physics analysis tools through the material models and a controller which schedules the execution of the various software tools. The combination of tools is implemented on a series of computational nodes operating in parallel and creating a “super node” cluster within a collection of interconnected processors. Findings The proposed architecture and job scheduling system can allow a parallel exploration of the design space for a device. Originality/value The originality of the work derives from the organization of the parallel computing system into a series of “super nodes” and the creation of a materials database suitable for multi-physics interactions.

scholarly journals The Impact of High-Performance Computing Best Practice Applied to Next-Generation Sequencing Workflows

2015 ◽  
Author(s):  
Pierre Carrier ◽  
Bill Long ◽  
Richard Walsh ◽  
Jef Dawson ◽  
Carlos P. Sosa ◽  
...  
Keyword(s):  
Next Generation Sequencing ◽  
High Performance Computing ◽  
High Performance ◽  
Best Practice ◽  
Distributed Memory ◽  
Rna Seq ◽  
Next Generation ◽  
The Impact ◽  
Performance Computing ◽  
Generation Sequencing

High Performance Computing (HPC) Best Practice offers opportunities to implement lessons learned in areas such as computational chemistry and physics in genomics workflows, specifically Next-Generation Sequencing (NGS) workflows. In this study we will briefly describe how distributed-memory parallelism can be an important enhancement to the performance and resource utilization of NGS workflows. We will illustrate this point by showing results on the parallelization of the Inchworm module of the Trinity RNA-Seq pipeline for de novo transcriptome assembly. We show that these types of applications can scale to thousands of cores. Time scaling as well as memory scaling will be discussed at length using two RNA-Seq datasets, targeting the Mus musculus (mouse) and the Axolotl (Mexican salamander). Details about the efficient MPI communication and the impact on performance will also be shown. We hope to demonstrate that this type of parallelization approach can be extended to most types of bioinformatics workflows, with substantial benefits. The efficient, distributed-memory parallel implementation eliminates memory bottlenecks and dramatically accelerates NGS analysis. We further include a summary of programming paradigms available to the bioinformatics community, such as C++/MPI.

scholarly journals Computational experiment in era of HPC

2019 ◽  
Vol 27 (3) ◽  
pp. 263-267
Author(s):  
Alexander S. Ayriyan
Keyword(s):  
Parallel Computing ◽  
Life Cycle ◽  
High Performance Computing ◽  
High Performance ◽  
Computational Experiment ◽  
Computing Systems ◽  
The Impact ◽  
Performance Computing

In this note we discuss the impact of development of architecture and technology of parallel computing on the typical life-cycle of the computational experiment. In particular, it is argued that development and installation of high-performance computing systems is indeed important itself regardless of specific scientific tasks, since the presence of cutting-age HPC systems within an academic infrastructure gives wide possibilities and stimulates new researches.

The Impact of Virtualization on High Performance Computing Clustering in the Cloud

Big Data ◽  
2016 ◽  
pp. 1687-1704
Author(s):  
Ouidad Achahbar ◽  
Mohamed Riduan Abid
Keyword(s):  
Big Data ◽  
High Performance Computing ◽  
High Performance ◽  
Virtual Machines ◽  
Performance Trends ◽  
Tightly Coupled ◽  
Physical Clusters ◽  
User Friendly ◽  
The Impact ◽  
Performance Computing

The ongoing pervasiveness of Internet access is intensively increasing Big Data production. This, in turn, increases demand on compute power to process this massive data, and thus rendering High Performance Computing (HPC) into a high solicited service. Based on the paradigm of providing computing as a utility, the Cloud is offering user-friendly infrastructures for processing Big Data, e.g., High Performance Computing as a Service (HPCaaS). Still, HPCaaS performance is tightly coupled with the underlying virtualization technique since the latter is responsible for the creation of virtual machines that carry out data processing jobs. In this paper, the authors evaluate the impact of virtualization on HPCaaS. They track HPC performance under different Cloud virtualization platforms, namely KVM and VMware-ESXi, and compare it against physical clusters. Each tested cluster provided different performance trends. Yet, the overall analysis of the findings proved that the selection of virtualization technology can lead to significant improvements when handling HPCaaS.

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