Math libraries in SunStudio

• •  Math libraries in SunStudio  

1 Libraries Included With Sun Performance Library

Sun Performance Library contains enhanced versions of the following standard libraries:

• LAPACK version 3.0 - For solving linear algebra problems.
• BLAS1 (Basic Linear Algebra Subprograms) - For performing vector-vector operations.
• BLAS2 - For performing matrix-vector operations.
• BLAS3 - For performing matrix-matrix operations.

The BLAS1, BLAS2, and BLAS3 libraries do not have version numbers. There has been only one version of the BLAS routines on Netlib.

2 Sun Performance Library Features

Sun Performance Library routines can increase application performance on both serial and multiprocessor (MP) platforms, because the serial speed of many Sun Performance Library routines has been increased, and many routines have been parallelized. Sun Performance Library routines also have SPARC and AMD specific optimizations that are not present in the base Netlib libraries.

Sun Performance Library provides the following optimizations and extensions to the base Netlib libraries:

• Extensions that support Fortran 95 and C language interfaces
• Fortran 95 language features, including type independence, compile time checking, and optional arguments.
• Consistent API across the different libraries in Sun Performance Library
• Compatibility with LAPACK 1, LAPACK 2.0, and LAPACK 3.0 libraries
• Increased performance, and in some cases, greater accuracy
• Optimizations for specific SPARC and x86/x64 instruction set architectures
• Support for 64-bit enabled Solaris and Linux operating environments
• Support for parallel processing compiler options for SPARC and x86/x64 platforms
• Support for multiple processor hardware options

3 Mathematical Routines

The Sun Performance Library routines are used to solve the following types of linear algebra and numerical problems:

• Elementary vector and matrix operations - Vector and matrix products; plane rotations; 1, 2-, and infinity-norms; rank-1, 2, k, and 2k updates
• Linear systems - Solve full-rank systems, compute error bounds, solve Sylvester equations, refine a computed solution, equilibrate a coefficient matrix
• Least squares - Full-rank, generalized linear regression, rank-deficient, linear equality constrained
• Eigenproblems - Eigenvalues, generalized eigenvalues, eigenvectors, generalized eigenvectors, Schur vectors, generalized Schur vectors
• Matrix factorizations or decompositions - SVD, generalized SVD, QL and LQ, QR and RQ, Cholesky, LU, Schur, LDLT and UDUT
• Support operations - Condition number, in-place or out-of-place transpose, inverse, determinant, inertia
• Sparse matrices - Solve symmetric, structurally symmetric, and unsymmetric coefficient matrices using direct methods and a choice of fill-reducing ordering algorithms, and user-specified orderings
• Convolution and correlation in one and two dimensions
• Fast Fourier transforms, Fourier synthesis, cosine and quarter-wave cosine transforms, cosine and quarter-wave sine transforms
• Complex vector FFTs and FFTs in two and three dimensions
• Interval BLAS routines
• Sorting operations

Full description - http://docs.sun.com/source/819-5268/index.html