{"title":"海报:MPACK 0.7.0: BLAS和LAPACK的多精度版本","authors":"Maho Nakata","doi":"10.1109/SC.Companion.2012.183","DOIUrl":null,"url":null,"abstract":"We are interested in the accuracy of linear algebra operations; accuracy of the solution of linear equation, eigenvalue and eigenvectors of some matrices, etc. This is a reason for we have been developing the MPACK. The MPACK consists of MBLAS and MLAPACK, multiple precision version of BLAS and LAPACK, respectively. Features of MPACK are: (i) based on LAPACK 3.x, (ii) to provide a reference implementation and or API (iii) written in C++, rewrite from FORTRAN77 (iv) supports GMP, MPFR, DD/QD and binary128 as multiple precision arithmetic library and (v) portable. Current version of MPACK is 0.7.0 and it supports 76 MBLAS routines and 100 MLAPACK routines. Matrix-matrix multiplication routine has been accelerated using NVIDIA C2050 GPU.","PeriodicalId":6346,"journal":{"name":"2012 SC Companion: High Performance Computing, Networking Storage and Analysis","volume":"26 1","pages":"1353-1353"},"PeriodicalIF":0.0000,"publicationDate":"2012-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":"{\"title\":\"Poster: MPACK 0.7.0: Multiple Precision Version of BLAS and LAPACK\",\"authors\":\"Maho Nakata\",\"doi\":\"10.1109/SC.Companion.2012.183\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We are interested in the accuracy of linear algebra operations; accuracy of the solution of linear equation, eigenvalue and eigenvectors of some matrices, etc. This is a reason for we have been developing the MPACK. The MPACK consists of MBLAS and MLAPACK, multiple precision version of BLAS and LAPACK, respectively. Features of MPACK are: (i) based on LAPACK 3.x, (ii) to provide a reference implementation and or API (iii) written in C++, rewrite from FORTRAN77 (iv) supports GMP, MPFR, DD/QD and binary128 as multiple precision arithmetic library and (v) portable. Current version of MPACK is 0.7.0 and it supports 76 MBLAS routines and 100 MLAPACK routines. Matrix-matrix multiplication routine has been accelerated using NVIDIA C2050 GPU.\",\"PeriodicalId\":6346,\"journal\":{\"name\":\"2012 SC Companion: High Performance Computing, Networking Storage and Analysis\",\"volume\":\"26 1\",\"pages\":\"1353-1353\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2012-11-10\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2012 SC Companion: High Performance Computing, Networking Storage and Analysis\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/SC.Companion.2012.183\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2012 SC Companion: High Performance Computing, Networking Storage and Analysis","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SC.Companion.2012.183","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Poster: MPACK 0.7.0: Multiple Precision Version of BLAS and LAPACK
We are interested in the accuracy of linear algebra operations; accuracy of the solution of linear equation, eigenvalue and eigenvectors of some matrices, etc. This is a reason for we have been developing the MPACK. The MPACK consists of MBLAS and MLAPACK, multiple precision version of BLAS and LAPACK, respectively. Features of MPACK are: (i) based on LAPACK 3.x, (ii) to provide a reference implementation and or API (iii) written in C++, rewrite from FORTRAN77 (iv) supports GMP, MPFR, DD/QD and binary128 as multiple precision arithmetic library and (v) portable. Current version of MPACK is 0.7.0 and it supports 76 MBLAS routines and 100 MLAPACK routines. Matrix-matrix multiplication routine has been accelerated using NVIDIA C2050 GPU.
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