OpenBLAS

As explained in Fast Python: High performance techniques for large datasets

OpenBLAS is an open-source optimized implementation of the BLAS and LAPACK numerical linear algebra interfaces.

OpenBLAS is a threaded, optimized implementation of the BLAS and LAPACK interfaces used beneath NumPy. BLAS supplies operations such as vector addition and matrix multiplication; LAPACK builds higher-level algorithms such as singular value decomposition on top of them. section 4.3.1

OpenBLAS matters when tuning numerical Python workloads because its execution model affects resource allocation:

• Threading can substantially change CPU usage compared with nonthreaded Netlib implementations. section 4.3.1
• Combining multithreaded BLAS or LAPACK with process-level parallelism can overcommit CPU resources. section 4.3.1
• Checking NumPy’s linked libraries is necessary before choosing later optimization or parallelization steps. section 4.3.1

Check the linked numerical libraries

Run np.show_config() and inspect the reported BLAS and LAPACK entries. When the output is unclear, inspect shared-library links and package metadata to determine whether NumPy uses OpenBLAS, MKL, or another implementation. section 4.3.1

Install or select an implementation

With Anaconda, conda install numpy will commonly provide OpenBLAS. Standard pip installations may require an operating-system BLAS and LAPACK installation; avoid the slow, nonthreaded Netlib implementation when performance matters. Benchmark alternatives such as OpenBLAS and MKL when maximum performance is required. section 4.3.2

Figure 4.11 The NumPy stack, including library dependencies

Use the NumPy dependency diagram to trace how Python-facing numerical operations connect to BLAS, LAPACK, and their implementations. section 4.3.1

How Fast Python: High performance techniques for large datasets covers it

How deep it goes

Explains interfaces, threading, linkage inspection, and installation choices but stops short of kernel-level implementation details.

Examples

Uses np.show_config(), shared-library inspection, Anaconda installation, and BLAS implementation benchmarking.

What it assumes

Assumes familiarity with NumPy and Python distributions while defining BLAS and LAPACK roles from their basic operations upward.

What you can do after

Operate/configure NumPy with an appropriate BLAS implementation and avoid process-level CPU overcommitment.

How it connects

Connects NumPy and SciPy to BLAS, LAPACK, Netlib, and Intel MKL.

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Related concepts

• BLASalternative to · BLAS is a standard interface and collection of low-level routines for efficient vector and matrix operations in numerical computing.
• Intel MKLalternative to · Intel MKL, now Intel oneMKL, is a library of optimized mathematical and scientific computing routines, including implementations of BLAS and LAPACK.

This explainer was generated by Manning's knowledge graph from the text of Fast Python: High performance techniques for large datasets. The section links point to the parts of the book it draws on.