LU factorization using multithreaded system
The problem of solving large systems of linear equations of the form (Ax = b) arises in various applications such as finite element analysis, computational fluid dynamics, and power systems analysis, which is of high algorithms complexities, that takes a lot of execution time. The high computational...
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creator | Badawy, M. O. Hanafy, Y. Y. Eltarras, R. |
description | The problem of solving large systems of linear equations of the form (Ax = b) arises in various applications such as finite element analysis, computational fluid dynamics, and power systems analysis, which is of high algorithms complexities, that takes a lot of execution time. The high computational power required for fast solution of such problem is beyond the reach of present day conventional uniprocessor. Furthermore, the performance of using a system of uniprocessor tends to display an early saturation in relation to their costs. This implies that even modest gains in performance of a uniprocessor come at an exorbitant increase in its cost, that made the use of new technologies mandatory to minimize the execution time. This paper presents a parallel implementation of the classical solution of system of linear equations at high and reasonable speed up. The speed up achievement is obtained through the fine granularity in data and tasks, and asynchronicity to hide latency of memory access. |
doi_str_mv | 10.1109/ICCTA.2012.6523540 |
format | Conference Proceeding |
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The speed up achievement is obtained through the fine granularity in data and tasks, and asynchronicity to hide latency of memory access.</description><identifier>ISBN: 1467328235</identifier><identifier>ISBN: 9781467328234</identifier><identifier>EISBN: 1467328243</identifier><identifier>EISBN: 9781467328227</identifier><identifier>EISBN: 9781467328241</identifier><identifier>EISBN: 1467328227</identifier><identifier>DOI: 10.1109/ICCTA.2012.6523540</identifier><language>eng</language><publisher>IEEE</publisher><subject>Dense linear algebra ; LU factorization ; Many-core ; Multicore ; Multithreading ; Task scheduling</subject><ispartof>2012 22nd International Conference on Computer Theory and Applications (ICCTA), 2012, p.9-14</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/6523540$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>309,310,780,784,789,790,2058,27925,54920</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/6523540$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Badawy, M. O.</creatorcontrib><creatorcontrib>Hanafy, Y. Y.</creatorcontrib><creatorcontrib>Eltarras, R.</creatorcontrib><title>LU factorization using multithreaded system</title><title>2012 22nd International Conference on Computer Theory and Applications (ICCTA)</title><addtitle>ICCTA</addtitle><description>The problem of solving large systems of linear equations of the form (Ax = b) arises in various applications such as finite element analysis, computational fluid dynamics, and power systems analysis, which is of high algorithms complexities, that takes a lot of execution time. The high computational power required for fast solution of such problem is beyond the reach of present day conventional uniprocessor. Furthermore, the performance of using a system of uniprocessor tends to display an early saturation in relation to their costs. This implies that even modest gains in performance of a uniprocessor come at an exorbitant increase in its cost, that made the use of new technologies mandatory to minimize the execution time. This paper presents a parallel implementation of the classical solution of system of linear equations at high and reasonable speed up. The speed up achievement is obtained through the fine granularity in data and tasks, and asynchronicity to hide latency of memory access.</description><subject>Dense linear algebra</subject><subject>LU factorization</subject><subject>Many-core</subject><subject>Multicore</subject><subject>Multithreading</subject><subject>Task scheduling</subject><isbn>1467328235</isbn><isbn>9781467328234</isbn><isbn>1467328243</isbn><isbn>9781467328227</isbn><isbn>9781467328241</isbn><isbn>1467328227</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2012</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNpFj81KAzEURiNSUGtfQDezlxlvcvPXZRm0FgbctOuSSW5qpNPKJF3Up1ew4OrjLM6Bj7EHDg3nMH9ete160QjgotFKoJJwxe641AaFFRKv_wHVDZvl_AkAv6Y21tyyp25TRefLcUzfrqTjoTrldNhVw2lfUvkYyQUKVT7nQsM9m0S3zzS77JRtXl_W7VvdvS9X7aKrEzeq1DL0xLWcS-g9AELQkROBkhyjiBTRefBGI5JD8OAExN4LksFGK6wBnLLHv24iou3XmAY3nreXc_gDxUpCyQ</recordid><startdate>201210</startdate><enddate>201210</enddate><creator>Badawy, M. O.</creator><creator>Hanafy, Y. Y.</creator><creator>Eltarras, R.</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>201210</creationdate><title>LU factorization using multithreaded system</title><author>Badawy, M. O. ; Hanafy, Y. Y. ; Eltarras, R.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i175t-4dbe164940bc0030d6f1ee05413f2fef3ac0c7633ea30c0a20fbc2e4d8f828703</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2012</creationdate><topic>Dense linear algebra</topic><topic>LU factorization</topic><topic>Many-core</topic><topic>Multicore</topic><topic>Multithreading</topic><topic>Task scheduling</topic><toplevel>online_resources</toplevel><creatorcontrib>Badawy, M. O.</creatorcontrib><creatorcontrib>Hanafy, Y. Y.</creatorcontrib><creatorcontrib>Eltarras, R.</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>Badawy, M. O.</au><au>Hanafy, Y. Y.</au><au>Eltarras, R.</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>LU factorization using multithreaded system</atitle><btitle>2012 22nd International Conference on Computer Theory and Applications (ICCTA)</btitle><stitle>ICCTA</stitle><date>2012-10</date><risdate>2012</risdate><spage>9</spage><epage>14</epage><pages>9-14</pages><isbn>1467328235</isbn><isbn>9781467328234</isbn><eisbn>1467328243</eisbn><eisbn>9781467328227</eisbn><eisbn>9781467328241</eisbn><eisbn>1467328227</eisbn><abstract>The problem of solving large systems of linear equations of the form (Ax = b) arises in various applications such as finite element analysis, computational fluid dynamics, and power systems analysis, which is of high algorithms complexities, that takes a lot of execution time. The high computational power required for fast solution of such problem is beyond the reach of present day conventional uniprocessor. Furthermore, the performance of using a system of uniprocessor tends to display an early saturation in relation to their costs. This implies that even modest gains in performance of a uniprocessor come at an exorbitant increase in its cost, that made the use of new technologies mandatory to minimize the execution time. This paper presents a parallel implementation of the classical solution of system of linear equations at high and reasonable speed up. The speed up achievement is obtained through the fine granularity in data and tasks, and asynchronicity to hide latency of memory access.</abstract><pub>IEEE</pub><doi>10.1109/ICCTA.2012.6523540</doi><tpages>6</tpages></addata></record> |
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subjects | Dense linear algebra LU factorization Many-core Multicore Multithreading Task scheduling |
title | LU factorization using multithreaded system |
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