BLACS

Blacs refers to a set of libraries for performing basic linear algebra operations. Specifically, it is a platform for the implementation of a standard interface for linear algebra routines, called the Basic Linear Algebra Subprograms (BLAS). The BLAS is a specification that defines a set of routines for common vector and matrix operations such as vector scaling, vector addition, dot products, and matrix-vector multiplication. Implementations of BLAS are often highly optimized for specific hardware architectures, leading to significant performance improvements for numerical computations.

The BLACS library, standing for Blocked and Cyclic distribution of matrices, extends the BLAS by providing routines for linear algebra operations on distributed memory parallel computers. It handles the distribution of matrices across multiple processors and provides communication primitives necessary for performing these operations efficiently. BLACS is designed to work in conjunction with parallel message-passing libraries like MPI (Message Passing Interface). It allows users to perform operations on matrices that are too large to fit into the memory of a single machine, distributing them across a cluster of computers.

Key features of BLACS include its support for blocked matrix algorithms, which improve cache utilization, and its ability to handle both cyclically and block-cyclically distributed matrices. This allows for flexible data distribution strategies to optimize performance for different problem sizes and hardware configurations. BLACS is a foundational component in many high-performance scientific computing applications, particularly those involving large-scale linear algebra problems encountered in fields such as computational fluid dynamics, structural analysis, and machine learning. It is often used as a building block for more complex parallel linear algebra libraries like ScaLAPACK (Scalable LAPACK).