Heuristic testing on task partitioning for heterogeneous cluster

Executing large task on a single machine requires CPU to have high processing power. In present environment, most machines are interconnected in a cluster and they are often underutilized. This paper explores heuristically the performance of homogeneous, heterogeneous and multi-core clusters. This w...

Ausführliche Beschreibung

Gespeichert in:

Bibliographische Detailangaben
Hauptverfasser:	Rosli, M. H., Khalid, N. E. A., Abidin, S. Z. Z., Manaf, M.
Format:	Tagungsbericht
Sprache:	eng
Schlagworte:	heterogenous cluster Heuristic testing Multi-core parallel processing task partitioning
Online-Zugang:	Volltext bestellen
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

container_end_page	390
container_issue
container_start_page	385
container_title
container_volume
creator	Rosli, M. H. Khalid, N. E. A. Abidin, S. Z. Z. Manaf, M.
description	Executing large task on a single machine requires CPU to have high processing power. In present environment, most machines are interconnected in a cluster and they are often underutilized. This paper explores heuristically the performance of homogeneous, heterogeneous and multi-core clusters. This work consists of three experiments: Equal task partitioning according to the number of nodes (homogeneous cluster), Equal task partitioning according to the number of nodes (heterogeneous cluster) and Equal task partitioning according to the number of cores (heterogeneous cluster). The task is based on edge detection method (Sobel) which is tested with an array of images. The images are processed in three different sizes; 1K x 1K, 2K × 2K and 3K × 3K. The performance evaluations are based on processing speed as parallel processing measurement. The results yield a list of critical criteria that influence the performance of processing speed in a heterogeneous cluster.
format	Conference Proceeding
fullrecord	<record><control><sourceid>ieee_6IE</sourceid><recordid>TN_cdi_ieee_primary_6530363</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><ieee_id>6530363</ieee_id><sourcerecordid>6530363</sourcerecordid><originalsourceid>FETCH-LOGICAL-i105t-5fb67063a39b6a8a40d0a94d14026a3226151898e9263f476ac3b9d2f03a3bb93</originalsourceid><addsrcrecordid>eNotjsFKxDAURSMyoI79Ajf5gcJLXvKa7JRBHWHAja6HtH2diY7tkKQL_96Krg73wL3cC1H5xjnvDZLRpC7FjTLUILjFXIkq5w8AUFrZxtK1uN_ynGIusZOFF4wHOY2yhPwpzyGVWOI0_sphSvLIhdN04JGnOcvuNOcl34rVEE6Zq3-uxfvT49tmW-9en182D7s6KrCltkNLDRAG9C0FFwz0ELzplQFNAfXy1CrnHXtNOJiGQoet7_UAS6VtPa7F3d9uZOb9OcWvkL73ZBGQEH8AuUJFIg</addsrcrecordid><sourcetype>Publisher</sourcetype><iscdi>true</iscdi><recordtype>conference_proceeding</recordtype></control><display><type>conference_proceeding</type><title>Heuristic testing on task partitioning for heterogeneous cluster</title><source>IEEE Electronic Library (IEL) Conference Proceedings</source><creator>Rosli, M. H. ; Khalid, N. E. A. ; Abidin, S. Z. Z. ; Manaf, M.</creator><creatorcontrib>Rosli, M. H. ; Khalid, N. E. A. ; Abidin, S. Z. Z. ; Manaf, M.</creatorcontrib><description>Executing large task on a single machine requires CPU to have high processing power. In present environment, most machines are interconnected in a cluster and they are often underutilized. This paper explores heuristically the performance of homogeneous, heterogeneous and multi-core clusters. This work consists of three experiments: Equal task partitioning according to the number of nodes (homogeneous cluster), Equal task partitioning according to the number of nodes (heterogeneous cluster) and Equal task partitioning according to the number of cores (heterogeneous cluster). The task is based on edge detection method (Sobel) which is tested with an array of images. The images are processed in three different sizes; 1K x 1K, 2K × 2K and 3K × 3K. The performance evaluations are based on processing speed as parallel processing measurement. The results yield a list of critical criteria that influence the performance of processing speed in a heterogeneous cluster.</description><identifier>ISBN: 1467308943</identifier><identifier>ISBN: 9781467308946</identifier><identifier>EISBN: 9788994364261</identifier><identifier>EISBN: 9788994364223</identifier><identifier>EISBN: 8994364269</identifier><identifier>EISBN: 8994364226</identifier><language>eng</language><publisher>IEEE</publisher><subject>heterogenous cluster ; Heuristic testing ; Multi-core ; parallel processing ; task partitioning</subject><ispartof>2012 7th International Conference on Computing and Convergence Technology (ICCCT), 2012, p.385-390</ispartof><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/6530363$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>309,310,780,784,789,790,2056,54919</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/6530363$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Rosli, M. H.</creatorcontrib><creatorcontrib>Khalid, N. E. A.</creatorcontrib><creatorcontrib>Abidin, S. Z. Z.</creatorcontrib><creatorcontrib>Manaf, M.</creatorcontrib><title>Heuristic testing on task partitioning for heterogeneous cluster</title><title>2012 7th International Conference on Computing and Convergence Technology (ICCCT)</title><addtitle>IConCCT</addtitle><description>Executing large task on a single machine requires CPU to have high processing power. In present environment, most machines are interconnected in a cluster and they are often underutilized. This paper explores heuristically the performance of homogeneous, heterogeneous and multi-core clusters. This work consists of three experiments: Equal task partitioning according to the number of nodes (homogeneous cluster), Equal task partitioning according to the number of nodes (heterogeneous cluster) and Equal task partitioning according to the number of cores (heterogeneous cluster). The task is based on edge detection method (Sobel) which is tested with an array of images. The images are processed in three different sizes; 1K x 1K, 2K × 2K and 3K × 3K. The performance evaluations are based on processing speed as parallel processing measurement. The results yield a list of critical criteria that influence the performance of processing speed in a heterogeneous cluster.</description><subject>heterogenous cluster</subject><subject>Heuristic testing</subject><subject>Multi-core</subject><subject>parallel processing</subject><subject>task partitioning</subject><isbn>1467308943</isbn><isbn>9781467308946</isbn><isbn>9788994364261</isbn><isbn>9788994364223</isbn><isbn>8994364269</isbn><isbn>8994364226</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2012</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNotjsFKxDAURSMyoI79Ajf5gcJLXvKa7JRBHWHAja6HtH2diY7tkKQL_96Krg73wL3cC1H5xjnvDZLRpC7FjTLUILjFXIkq5w8AUFrZxtK1uN_ynGIusZOFF4wHOY2yhPwpzyGVWOI0_sphSvLIhdN04JGnOcvuNOcl34rVEE6Zq3-uxfvT49tmW-9en182D7s6KrCltkNLDRAG9C0FFwz0ELzplQFNAfXy1CrnHXtNOJiGQoet7_UAS6VtPa7F3d9uZOb9OcWvkL73ZBGQEH8AuUJFIg</recordid><startdate>201212</startdate><enddate>201212</enddate><creator>Rosli, M. H.</creator><creator>Khalid, N. E. A.</creator><creator>Abidin, S. Z. Z.</creator><creator>Manaf, M.</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>201212</creationdate><title>Heuristic testing on task partitioning for heterogeneous cluster</title><author>Rosli, M. H. ; Khalid, N. E. A. ; Abidin, S. Z. Z. ; Manaf, M.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i105t-5fb67063a39b6a8a40d0a94d14026a3226151898e9263f476ac3b9d2f03a3bb93</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2012</creationdate><topic>heterogenous cluster</topic><topic>Heuristic testing</topic><topic>Multi-core</topic><topic>parallel processing</topic><topic>task partitioning</topic><toplevel>online_resources</toplevel><creatorcontrib>Rosli, M. H.</creatorcontrib><creatorcontrib>Khalid, N. E. A.</creatorcontrib><creatorcontrib>Abidin, S. Z. Z.</creatorcontrib><creatorcontrib>Manaf, M.</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan All Online (POP All Online) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE Electronic Library (IEL)</collection><collection>IEEE Proceedings Order Plans (POP All) 1998-Present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Rosli, M. H.</au><au>Khalid, N. E. A.</au><au>Abidin, S. Z. Z.</au><au>Manaf, M.</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Heuristic testing on task partitioning for heterogeneous cluster</atitle><btitle>2012 7th International Conference on Computing and Convergence Technology (ICCCT)</btitle><stitle>IConCCT</stitle><date>2012-12</date><risdate>2012</risdate><spage>385</spage><epage>390</epage><pages>385-390</pages><isbn>1467308943</isbn><isbn>9781467308946</isbn><eisbn>9788994364261</eisbn><eisbn>9788994364223</eisbn><eisbn>8994364269</eisbn><eisbn>8994364226</eisbn><abstract>Executing large task on a single machine requires CPU to have high processing power. In present environment, most machines are interconnected in a cluster and they are often underutilized. This paper explores heuristically the performance of homogeneous, heterogeneous and multi-core clusters. This work consists of three experiments: Equal task partitioning according to the number of nodes (homogeneous cluster), Equal task partitioning according to the number of nodes (heterogeneous cluster) and Equal task partitioning according to the number of cores (heterogeneous cluster). The task is based on edge detection method (Sobel) which is tested with an array of images. The images are processed in three different sizes; 1K x 1K, 2K × 2K and 3K × 3K. The performance evaluations are based on processing speed as parallel processing measurement. The results yield a list of critical criteria that influence the performance of processing speed in a heterogeneous cluster.</abstract><pub>IEEE</pub><tpages>6</tpages></addata></record>
fulltext	fulltext_linktorsrc
identifier	ISBN: 1467308943
ispartof	2012 7th International Conference on Computing and Convergence Technology (ICCCT), 2012, p.385-390
issn
language	eng
recordid	cdi_ieee_primary_6530363
source	IEEE Electronic Library (IEL) Conference Proceedings
subjects	heterogenous cluster Heuristic testing Multi-core parallel processing task partitioning
title	Heuristic testing on task partitioning for heterogeneous cluster
url	https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-11T03%3A41%3A54IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-ieee_6IE&rft_val_fmt=info:ofi/fmt:kev:mtx:book&rft.genre=proceeding&rft.atitle=Heuristic%20testing%20on%20task%20partitioning%20for%20heterogeneous%20cluster&rft.btitle=2012%207th%20International%20Conference%20on%20Computing%20and%20Convergence%20Technology%20(ICCCT)&rft.au=Rosli,%20M.%20H.&rft.date=2012-12&rft.spage=385&rft.epage=390&rft.pages=385-390&rft.isbn=1467308943&rft.isbn_list=9781467308946&rft_id=info:doi/&rft_dat=%3Cieee_6IE%3E6530363%3C/ieee_6IE%3E%3Curl%3E%3C/url%3E&rft.eisbn=9788994364261&rft.eisbn_list=9788994364223&rft.eisbn_list=8994364269&rft.eisbn_list=8994364226&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_id=info:pmid/&rft_ieee_id=6530363&rfr_iscdi=true