pyblas 0.0.9

A python port of the netlib reference BLAS implementation.

🔢 PyBLAS

PyBLAS is a python port of the netlib reference BLAS implementation.

Usage

pip install numpy pyblas

import numpy as np
from pyblas.level1 import dswap

x = np.array([1.2, 2.3, 3.4], dtype=np.double) # A double-precision vector x
y = np.array([5.6, 7.8, 9.0], dtype=np.double) # A double precision vector y
N = len(x)  # The length of the vectors x and y
incx = 1  # The index spacing of the vector x
incy = 1  # The index spacing of the vector y

# Swap the values of the vectors x and y
dswap(N, x, incx, y, incy)
print(x, y)

For more details on how to use the PyBLAS library, please consult our docs

What is BLAS

The Basic Linear Algebra Subprograms (BLAS) are a collection of functions which form the basis of many modern numerical computing packages, including numpy, scipy, and matlab. They provide functions for performing basic calculations on vectors and matrices, which form the basis for more complex calculations such as solving systems of linear equations.

Who is this for?

• Maths and computer science students who are learning about linear algebra and want to see how to implement simple operations.
• Algorithm developers who want to prototype their calculations in a high level language with the same APIs they will use in C or Fortran.
• Data scientists who want to better understand what is going on under the hood of the algorithms they use.
• Educators who want an easy to use BLAS implementation when teaching numerical computing courses.

Performance

Optimal performance is not a goal of the PyBLAS project. The project aim is to have a simple and readable implementation of the BLAS standards. As such, we often forego optimization opporunities in favour of simplicity.

The project matches overall algorithmic complexity with the reference implementation for all functions.

Accuracy

The project aims to match the numerical accuracy of the reference BLAS implementation. A future goal is to have benchmarks which can be run against the system BLAS libraries to verify the numerical accuract of the functions

Level 1

single (s)	double (d)	single complex (c)	double complex (z)
scopy	dcopy	ccopy	zcopy	y := x
sswap	dswap	cswap	zswap	x, y := y
sscal	dscal	cscal (csscal)	zscal (zdscal)	x := a*x
saxpy	daxpy	caxpy	zaxpy	y := a*x + y
		scabs1	dcabs1	`=>
sasum	dasum			`=> sum(
		scasum	dzasum	`=> sum(
sdot (sdsdot, dsdot)	ddot	cdotu	zdotu	=> <x, y>
		cdotc	zdotc	=> <x^H, y>
snrm2	dnrm2			=> sqrt(<x, x>)
		scnrm2	dznrm2	=> sqrt(<x^H, x>)
srot	drot	csrot	zdrot	[x_i, y_i] := [c s; -s c] * [x_i, y_i]
srotg	drotg	crotg	zrotg	c := a/r, s:= b/r, a :=r, b := "z"
srotm	drotm			[x_i, y_i] := [h_1 h_2; h_3, h_4] * [x_i, y_i]
srotmg	drotmg

Level 2

single (s)	double (d)	single complex (c)	double complex (z)
ssyr	dsyr			A := a*x*x^T + A (sym)
		cher	zher	A := a*x*x^H + A (her)
sspr	dspr			A := a*x*x^T + A (sym-packaged)
		chpr	zhpr	A := a*x*x^H + A (her-packaged)
sger	dger	cgeru	zgeru	A := a*x*y^T + A
		cgerc	zgerc	A := a*x*y^H + A
ssyr2	dsyr2			A := a*x*y^T + a*y*x^T + A (sym)
		cher2	zher2	A := a*x*y^H + a^+*y*x^H + A (her)
sspr2	dspr2			A := a*x*y^T + a*y*x^T + A (sym-packed)
		chpr2	zhpr2	A := a*x*y^H + a*y*x^H + A (her-packed)

| | | | | strsv | dtrsv | ctrsv | ztrsv | x := A^-1*b or x := A^[TH]^-1*b (tri) | | stbsv | dtbsv | ctbsv | ztbsv | x := A^-1*b or x := A^[TH]^-1*b (band) | | stpsv | dtpsv | ctpsv | ztpsv | x := A^-1*b or x := A^[TH]^-1*b (tri-packed) | | | | | | stpmv | dtpmv | ctpmv | ztpmv | x := A^[1TH]*x (sym-packed) | | stbmv | dtbmv | ctbmv | ztbmv | x := A^[1TH]*x (tri-band) | | strmv | dtrmv | ctrmv | ztrmv | x := A^[1TH]*x (tri) | | sgemv | dgemv | cgemv | zgemv | y := a*A^[1TH]*x + b*y | | | | chemv | zhemv | y := a*A*x + b*y (herm) | | sgbmv | dgbmv | cgbmv | zgbmv | y := a*A^[1TH]*x + b*y (band) | | | | chbmv | zhbmv | y := a*A*x + b*y (band-herm) | | ssymv | dsymv | | y := a*A*x + b*y (sym) | | sspmv | dspmv | | y := a*A*x + b*y (sym-packed) | | | | chpmv | zhpmv | y := a*A*x + b*y (her-packed) | | ssbmv | dsbmv | | | y := a*A*x + b*y (sym-band) | | isamax | idamax | | | | => argmax(|x_i|) | | | | icamax |izamax | => argmax(|Re(x_i)| + |Im(x_i)|) |

Level 3