GAMS Index for the NAG Parallel Library

ثبت نشده
چکیده

C Elementary and special functions (search also class L5 ) C1 Integer-valued functions (e.g., factorial, binomial coefficient, permutations, combinations, floor, ceiling) C06GXFP Factorizes a positive integer n as n = n1 × n2. This routine may be used in conjunction with C06MCFP D Linear Algebra D1 Elementary vector and matrix operations D1a Elementary vector operations D1a1 Set to constant D1a11 Other vector operations F01XEFP Scatter real vector distributed conformally to sparse matrix, used with routines from Chapter F11 F01XFFP Gather real vector distributed conformally to sparse matrix, used with routines from Chapter F11 F01XGFP Scatter integer vector distributed conformally to sparse matrix, used with routines from Chapter F11 F01XHFP Gather integer vector distributed conformally to sparse matrix, used with routines from Chapter F11 F01XUFP Gather complex vector distributed conformally to sparse matrix, used with routines from Chapter F11 F01YEFP In-place generation of real dense vector distributed conformally to sparse matrix F01YTFP In-place generation of complex dense vector distributed conformally to sparse matrix, used with routines from Chapter F11 F01ZYFP In-place generation of complex vector in column block fashion, used with routines from Chapter F07 F01ZZFP In-place generation of real vector in column block fashion, used with routines from Chapter F07 F11YBFP Permutation of real vector from distribution based order to local indexing based order F11YCFP Permutation of real vector from local indexing based order to distribution based order F11YPFP Permutation of complex vector from distribution based order to local indexing based order F11YQFP Permutation of complex vector from local indexing based order to distribution based order X04YPFP Outputs complex vector, distributed conformally to sparse matrix to a sequential file D1b Elementary matrix operations F01CPFP Element-wise maximum or minimum in absolute value of integer matrices F11YAFP Permutation of non-zero entries of real sparse matrix with repeated sparsity pattern F11YNFP Permutation of non-zero entries of complex sparse matrix with repeated sparsity pattern F11ZGFP Generates multi-colour ordering for real sparse matrix with symmetric sparsity pattern, distributed in row block form F11ZUFP Generates multi-colour ordering for complex sparse matrix with symmetric sparsity pattern, distributed in row block form. X04BZFP Outputs complex matrix stored in row block fashion D1b1 Initialize (e.g., to zero or identity) F01YAFP In-place generation of real sparse matrix using cyclic row block distribution F01YBFP In-place generation of real sparse matrix using cyclic row block distribution (suitable for repeated sparsity pattern), used with routines from Chapter F11 F01YPFP In-place generation of complex sparse matrix according to cyclic row block distribution, used with routines from Chapter F11 F01YQFP In-place generation of complex sparse matrix according to cyclic row block distribution (suitable for repeated sparsity pattern) F01YWFP In-place generation of complex Hermitian banded matrix in column block fashion, used with routines from Chapter F07 F01YXFP In-place generation of real symmetric banded matrix in column block fashion, used with routines from Chapter F07 F01YYFP In-place generation of real matrix in row block fashion on a one-dimensional grid of processors, used with routines from Chapter F07 F01YZFP In-place generation of complex matrix in row block fashion on a one-dimensional grid of processors, used with routines from Chapter F07 F01ZHFP Generates an l by m by n three-dimensional array A(i, j, k) on a grid of processors in i-block form

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Solving PDE Problems on Parallel and Distributed Computer Systems Using the NAG Parallel Library

The NAG Parallel Library enables users to take advantage of the increased computing power and memory capacity ooered by multiple processors. It provides parallel subroutines in some of the areas covered by traditional numerical libraries, such as dense and sparse linear algebra, optimization, quadrature and random number generation, as well as utility routines for data distribution, input/outpu...

متن کامل

Digital Software and Data Repositories for Support of Scientiic Computing

This paper discusses the special characteristics and needs of software repositories and describes how these needs have been met by some existing repositories. These repositories include Netlib, the National HPCC Software Exchange, and the GAMS Virtual Repository. We also describe some systems that provide on-line access to various types of scienti c data. Finally, we outline a proposal for inte...

متن کامل

Parallel Computing for Computational Finance Applications: A Case Study Parallelizing NAG with Zircon Software

Analysts, scientist, engineers, and multimedia professionals require massive processing power to analyze financial trends, create test simulations, model climate, compile code, render video, decode genomes and other complex tasks. Although these groups could use specialized super computers, the custom development time and the hardware costs are prohibitive. This paper describes how we applied t...

متن کامل

Case studies on the development of ScaLAPACK and the NAG Numerical PVM Library

In this paper we look at the development of ScaLAPACK, a software library for dense and banded numerical linear algebra, and the NAG Numerical PVM Library, which includes software for dense and sparse linear algebra, quadrature, optimization and random number generation. Both libraries are aimed at distributed memory machines, including networks of workstations. The paper concentrates on the un...

متن کامل

Parleda: a Library for Parallel Processing in Computational Geometry Applications

ParLeda is a software library that provides the basic primitives needed for parallel implementation of computational geometry applications. It can also be used in implementing a parallel application that uses geometric data structures. The parallel model that we use is based on a new heterogeneous parallel model named HBSP, which is based on BSP and is introduced here. ParLeda uses two main lib...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

عنوان ژورنال:

دوره   شماره 

صفحات  -

تاریخ انتشار 2000