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Description:

pyspbla 1.0.1

pyspbla

pyspbla is a python wrapper for spbla library.
spbla is a linear Boolean algebra library primitives and operations for
work with sparse matrices written for CPU, Cuda and OpenCL platforms. The primary
goal of the library is implementation, testing and profiling algorithms for
solving formal-language-constrained problems, such as context-free
and regular path queries with various semantics for graph databases.
The library provides C-compatible API, written in the GraphBLAS style.
The library is shipped with python package pyspbla - wrapper for
spbla library C API. This package exports library features and primitives
in high-level format with automated resources management and fancy syntax sugar.
The primary library primitive is a sparse boolean matrix. The library provides
the most popular operations for matrix manipulation, such as construction from
values, transpose, sub-matrix extraction, matrix-to-vector reduce, matrix-matrix
element-wise addition, matrix-matrix multiplication and Kronecker product.
As a fallback library provides sequential backend for mentioned above operations
for computations on CPU side only. This backend is selected automatically
if Cuda/OpenCL compatible device is not presented in the system. This can be quite handy for
prototyping algorithms on a local computer for later running on a powerful server.
Features

Cuda backend for computations
OpenCL backend for computations
Cpu backend for computations
Matrix creation (empty, from data, with random data)
Matrix-matrix operations (multiplication, element-wise addition, kronecker product)
Matrix operations (equality, transpose, reduce to vector, extract sub-matrix)
Matrix data extraction (as lists, as list of pairs)
Matrix syntax sugar (pretty string printing, slicing, iterating through non-zero values)
IO (import/export matrix from/to .mtx file format)
GraphViz (export single matrix or set of matrices as a graph with custom color and label settings)
Debug (matrix string debug markers, logging)

Performance
Sparse Boolean matrix-matrix multiplication evaluation results are listed bellow.
Machine configuration: PC with Ubuntu 20.04, Intel Core i7-6700 3.40GHz CPU, DDR4 64Gb RAM, GeForce GTX 1070 GPU with 8Gb VRAM.

The matrix data is selected from the SuiteSparse Matrix Collection link.

Matrix name
# Rows
Nnz M
Nnz/row
Max Nnz/row
Nnz M^2

SNAP/amazon0312
400,727
3,200,440
7.9
10
14,390,544

LAW/amazon-2008
735,323
5,158,388
7.0
10
25,366,745

SNAP/web-Google
916,428
5,105,039
5.5
456
29,710,164

SNAP/roadNet-PA
1,090,920
3,083,796
2.8
9
7,238,920

SNAP/roadNet-TX
1,393,383
3,843,320
2.7
12
8,903,897

SNAP/roadNet-CA
1,971,281
5,533,214
2.8
12
12,908,450

DIMACS10/netherlands_osm
2,216,688
4,882,476
2.2
7
8,755,758

Detailed comparison is available in the full paper text at
link.
Simple example
Create sparse matrices, compute matrix-matrix product and print the result to the output:
import pyspbla as sp

a = sp.Matrix.empty(shape=(2, 3))
a[0, 0] = True
a[1, 2] = True

b = sp.Matrix.empty(shape=(3, 4))
b[0, 1] = True
b[0, 2] = True
b[1, 3] = True
b[2, 1] = True

print(a, b, a.mxm(b), sep="\n")

Transitive closure example
Compute the transitive closure problem for the directed graph and print the result:
import pyspbla as sp

a = sp.Matrix.empty(shape=(4, 4))
a[0, 1] = True
a[1, 2] = True
a[2, 0] = True
a[2, 3] = True
a[3, 2] = True

t = a.dup() # Duplicate matrix where to store result
total = 0 # Current number of values

while total != t.nvals:
total = t.nvals
t.mxm(t, out=t, accumulate=True) # t += t * t

print(a, t, sep="\n")

GraphViz example
Generate GraphViz graph script for a graph stored as a set of adjacency matrices:
import pyspbla as sp

name = "Test" # Displayed graph name
shape = (4, 4) # Adjacency matrices shape
colors = {"a": "red", "b": "green"} # Colors per label

a = sp.Matrix.empty(shape=shape) # Edges labeled as 'a'
a[0, 1] = True
a[1, 2] = True
a[2, 0] = True

b = sp.Matrix.empty(shape=shape) # Edges labeled as 'b'
b[2, 3] = True
b[3, 2] = True

print(sp.matrices_to_gviz(matrices={"a": a, "b": b}, graph_name=name, edge_colors=colors))

Script can be rendered by any gviz tool online and the result can be following:

Contributors

Egor Orachyov (Github: EgorOrachyov)
Maria Karpenko (Github: mkarpenkospb)
Pavel Alimov (Github : Krekep)
Semyon Grigorev (Github: gsvgit)

Citation
@online{spbla,
author = {Orachyov, Egor and Karpenko, Maria and Alimov, Pavel and Grigorev, Semyon},
title = {spbla: sparse Boolean linear algebra for CPU, Cuda and OpenCL computations},
year = 2021,
url = {https://github.com/JetBrains-Research/spbla},
note = {Version 1.0.0}
}

License
This project is licensed under MIT License. License text can be found in the
license file.
Acknowledgments
This is a research project of the Programming Languages and Tools Laboratory
at JetBrains-Research. Laboratory website link.