S. A. Haque, X. Li, Farnam Mansouri, M. M. Maza, Davood Mohajerani, W. Pan
{"title":"CUMODP: a CUDA library for modular polynomial computation","authors":"S. A. Haque, X. Li, Farnam Mansouri, M. M. Maza, Davood Mohajerani, W. Pan","doi":"10.1145/3177795.3177799","DOIUrl":null,"url":null,"abstract":"The CUDA Modular Polynomial (CUMODP) Library implements arithmetic operations for dense matrices and dense polynomials, primarily with modular integer coefficients. Some operations are available for integer or floating point coefficients. Similar to other software libraries, like CuBLAS 1 targeting Graphics Processing Units (GPUs), CUMODP focuses on efficiency-critical routines and provides them in the form of device functions and CUDA kernels. Hence, these routines are primarily designed to offer GPU support to polynomial system solvers. A bivariate system solver is part of the library, as a proof-of-concept. Its implementation is presented in [10] and it is integrated in Maple's Triangularize command2, since the release 18 of Maple.","PeriodicalId":7093,"journal":{"name":"ACM Commun. Comput. Algebra","volume":"27 1","pages":"89-91"},"PeriodicalIF":0.0000,"publicationDate":"2018-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"ACM Commun. Comput. Algebra","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3177795.3177799","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
Abstract
The CUDA Modular Polynomial (CUMODP) Library implements arithmetic operations for dense matrices and dense polynomials, primarily with modular integer coefficients. Some operations are available for integer or floating point coefficients. Similar to other software libraries, like CuBLAS 1 targeting Graphics Processing Units (GPUs), CUMODP focuses on efficiency-critical routines and provides them in the form of device functions and CUDA kernels. Hence, these routines are primarily designed to offer GPU support to polynomial system solvers. A bivariate system solver is part of the library, as a proof-of-concept. Its implementation is presented in [10] and it is integrated in Maple's Triangularize command2, since the release 18 of Maple.