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BLAS

As explained in Classic Computer Science Problems in Swift: Essential techniques for practicing programmers

BLAS is a standard interface and family of routines for basic vector and matrix operations in numerical computing.

BLAS, or Basic Linear Algebra Subprograms, is an older specification for optimized vector and matrix arithmetic. Neural-network code uses it to process arrays of numbers efficiently. section 7.3

On Apple platforms, the Accelerate framework supplies a BLAS implementation and can select suitable CPU SIMD instructions. GPUs offer another path for accelerating the same vector and matrix workload. section 7.3

Vector operations in Swift

A dot product uses vDSP_dotprD, while subtraction, elementwise multiplication, and summation use catlas_daxpby, vDSP_vmulD, and vDSP_sveD. These functions operate on Swift arrays of Double values. section 7.3

import Foundation
import Accelerate

Open the minimal Swift import needed before calling Accelerate's vector and matrix routines. section 7.3

How Classic Computer Science Problems in Swift: Essential techniques for practicing programmers covers it

How deep it goes
Defines BLAS and demonstrates several Accelerate-backed vector operations, but defers detailed routine semantics and matrix examples.
Examples
Uses Swift functions for dot products, subtraction, elementwise multiplication, and vector summation.
What it assumes
Assumes familiarity with arrays and basic vector arithmetic while illustrating how those operations map to library calls.
What you can do after
Apply it via Accelerate to implement faster vector operations in Swift neural-network code.
How it connects
Connects BLAS with vector and matrix math, Accelerate, SIMD instructions, CPUs, GPUs, and neural networks.
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