Performance issues in emerging homogeneous multi-core architectures

Kayi, Abdullah; El‐Ghazawi, Tarek; Newby, Gregory B.

doi:10.1016/j.simpat.2009.06.014

Cited by 17 publications

(11 citation statements)

References 22 publications

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“…GPU programming can be used in all areas of graphics and intensive applications that include science, engineering, bioinformatics, art and gaming [9,10].…”

Section: Gpu Programmingmentioning

confidence: 99%

A Learning Approach to Introducing GPU Computing in Undergraduate Engineering Program

Amrani¹

2014

IJCA

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The graphics processing unit (GPU) learning initiative is developed within a project awarded by the Moroccan Fulbright Alumni Association (MFAA), entitled "GPU Acceleration of Human Genome Sequencing". This project involves undergraduate students at Abdelmalek Essaadi University, and is conducted in collaboration with the High Performance Computing Lab (HPCL) at the George Washington University in U.S.The study brings together two of the most important topics and challenges for the medical field, Genomics, and information technology, parallel computing specially with Graphical Processing Units. The potential outcomes from the project will make very valuable contributions to medical and information technology research and will enrich the academic experience of the students.

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“…GPU programming can be used in all areas of graphics and intensive applications that include science, engineering, bioinformatics, art and gaming [9,10].…”

Section: Gpu Programmingmentioning

confidence: 99%

A Learning Approach to Introducing GPU Computing in Undergraduate Engineering Program

Amrani¹

2014

IJCA

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“…Some studies provide performance evaluation of MPI communication on clusters with ccNUMA nodes (Hamid and Coddington, 2010;Kayi et al, 2008) and multi-core architecture such as dual-core and quad-core nodes (Gu et al, 2013;Cheng and Gu, 2012;Kayi et al, 2009).…”

Section: Related Workmentioning

confidence: 99%

Performance Analysis of Message Passing Interface Collective Communication on Intel Xeon Quad-Core Gigabit Ethernet and Infiniband Clusters

Ismail¹,

Hamid²,

Othman³

et al. 2013

Journal of Computer Science

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The performance of MPI implementation operations still presents critical issues for high performance computing systems, particularly for more advanced processor technology. Consequently, this study concentrates on benchmarking MPI implementation on multi-core architecture by measuring the performance of Open MPI collective communication on Intel Xeon dual quad-core Gigabit Ethernet and InfiniBand clusters using SKaMPI. It focuses on well known collective communication routines such as MPI-Bcast, MPI-AlltoAll, MPI-Scatter and MPI-Gather. From the collection of results, MPI collective communication on InfiniBand clusters had distinctly better performance in terms of latency and throughput. The analysis indicates that the algorithm used for collective communication performed very well for all message sizes except for MPI-Bcast and MPI-Alltoall operation of inter-node communication. However, InfiniBand provides the lowest latency for all operations since it provides applications with an easy to use messaging service, compared to Gigabit Ethernet, which still requests the operating system for access to one of the server communication resources with the complex dance between an application and a network.

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“…GPU programming has immediate application in all areas of graphics and intensive applications including science, engineering, bioinformatics, art, animation and gaming [18,19].…”

Section: Use Of Graphics Processing Units Gpumentioning

confidence: 99%

Implementation of the WRF-Chem model in Grid Computing and GPU for regional air quality forecasting

Amrani

Hedgecock

2012

2012 IEEE International Geoscience and Remote Sensing Symposium

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WRF-Chem is the WRF coupled with Chemistry. The model simulates the coupling between atmospheric dynamics, radiation and chemistry, and is used for investigation of regional-scale air quality, field program analysis, and cloud-scale interactions between clouds and chemistry. WRF-Chem can predict transport of atmospheric constituents and radiation of O3 and UV [1].The WRF-Chem processing takes long execution time, and needs high storage space. The porting to Grid Computing and GPU can significantly reduce the overall run-time, since the model supports distributed and shared memory computations.Grid Computing and GPU are two innovative technologies in High Performance Computing, aiming to accelerate compute-intensive applications.

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Performance issues in emerging homogeneous multi-core architectures

Cited by 17 publications

References 22 publications

A Learning Approach to Introducing GPU Computing in Undergraduate Engineering Program

A Learning Approach to Introducing GPU Computing in Undergraduate Engineering Program

Performance Analysis of Message Passing Interface Collective Communication on Intel Xeon Quad-Core Gigabit Ethernet and Infiniband Clusters

Implementation of the WRF-Chem model in Grid Computing and GPU for regional air quality forecasting

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