PBLAS Routines

Intel® Math Kernel Library implements the PBLAS (Parallel Basic Algebra Subprograms) routines from the ScaLAPACK package for distributed-memory architecture. PBLAS is intended for using in vector-vector, matrix-vector, and matrix-matrix operations to simplify the parallelization of linear codes. The design of PBLAS is as consistent as possible with that of the BLAS. The routine descriptions are arranged in several sections according to the PBLAS level of operation:

PBLAS Level 1 Routines (distributed vector-vector operations)
PBLAS Level 2 Routines (distributed matrix-vector operations)
PBLAS Level 3 Routines (distributed matrix-matrix operations)

Each section presents the routine and function group descriptions in alphabetical order by the routine group name; for example, the p?asum group, the p?axpy group. The question mark in the group name corresponds to a character indicating the data type (s, d, c, and z or their combination); see Routine Naming Conventions.

Note

PBLAS routines are provided only with Intel® MKL versions for Linux* and Windows* OSs.

Generally, PBLAS runs on a network of computers using MPI as a message-passing layer and a set of prebuilt communication subprograms (BLACS), as well as a set of PBLAS optimized for the target architecture. The Intel MKL version of PBLAS is optimized for Intel® processors. For the detailed system and environment requirements see Intel® MKL Release Notes and Intel® MKL User's Guide.

For full reference on PBLAS routines and related information, see http://www.netlib.org/scalapack/html/pblas_qref.html.

Optimization Notice
The Intel® Math Kernel Library (Intel® MKL) contains functions that are more highly optimized for Intel microprocessors than for other microprocessors. While the functions in Intel® MKL offer optimizations for both Intel and Intel-compatible microprocessors, depending on your code and other factors, you will likely get extra performance on Intel microprocessors. While the paragraph above describes the basic optimization approach for Intel® MKL as a whole, the library may or may not be optimized to the same degree for non-Intel microprocessors for optimizations that are not unique to Intel microprocessors. These optimizations include Intel® Streaming SIMD Extensions 2 (Intel® SSE2), Intel® Streaming SIMD Extensions 3 (Intel® SSE3), and Supplemental Streaming SIMD Extensions 3 (Intel® SSSE3) instruction sets and other optimizations. Intel does not guarantee the availability, functionality, or effectiveness of any optimization on microprocessors not manufactured by Intel. Microprocessor-dependent optimizations in this product are intended for use with Intel microprocessors. Intel recommends that you evaluate other library products to determine which best meets your requirements.

Optimization Notice

The Intel® Math Kernel Library (Intel® MKL) contains functions that are more highly optimized for Intel microprocessors than for other microprocessors. While the functions in Intel® MKL offer optimizations for both Intel and Intel-compatible microprocessors, depending on your code and other factors, you will likely get extra performance on Intel microprocessors.

While the paragraph above describes the basic optimization approach for Intel® MKL as a whole, the library may or may not be optimized to the same degree for non-Intel microprocessors for optimizations that are not unique to Intel microprocessors. These optimizations include Intel® Streaming SIMD Extensions 2 (Intel® SSE2), Intel® Streaming SIMD Extensions 3 (Intel® SSE3), and Supplemental Streaming SIMD Extensions 3 (Intel® SSSE3) instruction sets and other optimizations. Intel does not guarantee the availability, functionality, or effectiveness of any optimization on microprocessors not manufactured by Intel. Microprocessor-dependent optimizations in this product are intended for use with Intel microprocessors.

Intel recommends that you evaluate other library products to determine which best meets your requirements.