## What is the difference between BLAS and Lapack?

BLAS is a collection of low-level matrix and vector arithmetic operations (“multiply a vector by a scalar”, “multiply two matrices and add to a third matrix”, etc …). LAPACK is a collection of higher-level linear algebra operations.

## Where is Scalapack?

ScaLAPACK is a freely-available software package. It is available from netlib via anonymous ftp and the World Wide Web at http://www.netlib.org/scalapack . Thus, it can be included in commercial software packages (and has been).

**Is Lapack parallel?**

Parallel performance in LAPACK routines is often obtained through a sequence of calls to parallel BLAS and by masking sequential computations with parallel ones. The latter requires splitting thread families into groups.

### How do I compile Scalapack?

Here is the procedure to recompile the scalapack library :

- download the scalapack sources from netlib. wget http://www.netlib.org/scalapack/scalapack-1.7.4.tgz.
- create a SLmake. inc file : A sample SLmake. inc file is attached.
- build the libraries : make lib.
- build the tests :

### Does LAPACK use BLAS?

LAPACK routines are written so that as much as possible of the computation is performed by calls to the Basic Linear Algebra Subprograms (BLAS) [78,42,40]. Highly efficient machine-specific implementations of the BLAS are available for many modern high-performance computers.

**Is Eigen faster than LAPACK?**

Eigen beats LAPACK using optimization flags ( -O3 ) and a good compiler (GCC, Clang). At least for most tests I recently performed (dense linear algebra)

## What is Blacs?

BLACS (Basic Linear Algebra Communication Subprograms) provides a message passing interface to linear algebra oriented applications. The BLACS interface makes linear algebra applications more portable and easier to program in distributed memory environments.

## What is Blacs library?

The BLACS (Basic Linear Algebra Communication Subprograms) project is an ongoing investigation whose purpose is to create a linear algebra oriented message passing interface that may be implemented efficiently and uniformly across a large range of distributed memory platforms.

**Does NumPy use LAPACK?**

NumPy searches for optimized linear algebra libraries such as BLAS and LAPACK. There are specific orders for searching these libraries, as described below and in the site.

### Why is LAPACK fast?

LAPACK (“Linear Algebra Package”) is a standard software library for numerical linear algebra. LAPACK, in contrast, was designed to effectively exploit the caches on modern cache-based architectures, and thus can run orders of magnitude faster than LINPACK on such machines, given a well-tuned BLAS implementation.

### Why is Eigen so fast?

For operations involving complex expressions, Eigen is inherently faster than any BLAS implementation because it can handle and optimize a whole operation globally — while BLAS forces the programmer to split complex operations into small steps that match the BLAS fixed-function API, which incurs inefficiency due to …

**Does Eigen use Lapack?**

Eigen: Using BLAS/LAPACK from Eigen. Since Eigen version 3.3 and later, any F77 compatible BLAS or LAPACK libraries can be used as backends for dense matrix products and dense matrix decompositions. When doing so, a number of Eigen’s algorithms are silently substituted with calls to BLAS or LAPACK routines.