PyRSB

librsb is a high performance sparse matrix library implementing the Recursive Sparse Blocks format, which is especially well suited for multiplications in iterative methods on very large sparse matrices.

PyRSB is a Cython-based Python interface to librsb.

On multicore machines, PyRSB can be several times faster than e.g. scipy.sparse.csr_matrix(). For an example how to invoke it with minimal overhead, see the advanced example.

So far, PyRSB is a prototype tested on Linux only. The librsb library instead is mature and well tested. Prospective PyRSB users and collaborators are welcome to contact me.

Features

The following functionality is implemented:

• Initialization with rsb.rsb_matrix() styled as scipy.sparse.csr_matrix().
• Conversion from scipy.sparse.csr_matrix().
• Multiplication by vector/multivector.
• Rows/columns through nr=a.shape()[0]/nr=a.shape()[1], or nr()/nc().
• find(), find_block(), tril(), triu(), shape(), nnz.
• print'able.
• PyRSB-Specific: autotune(), do_print().
• load from a Matrix Market file, e.g. rsb_matrix(bytes(filename,encoding='utf-8'))

Build and Use

• If you want the Makefile to build librsb (in this directory): make all-local will attempt downloading librsb-1.3.0.0 from the web and building it here before building pyrsb. If the file is in place, it won't download it a second time. After that, make local-librsb-pyrsb (or make lp) will build pyrsb using local librsb, then run it. This method shall use the best compilation flags.
• If you have librsb already installed: make shall build and test.
• Make sure you have cython, scipy, numpy. installed.
• If you don't have librsb installed you may want to try via pip pip install pyrsb
• If you want to install librsb on Ubuntu or Debian: sudo apt-get install librsb-dev shall suffice. Other operating systems have librsb, too. Please check yours. Or check librsb's web site.
• make test will test benchmark code using test.py (to compare speed to SciPy)
• make b will also produce graphs (requires gnuplot)

Example Usage

# Example: demo1.py
"""
pyrsb demo
"""

import numpy
import scipy
from scipy.sparse import csr_matrix
from pyrsb import *

V = [11.0, 12.0, 22.0]
I = [0, 0, 1]
J = [0, 1, 1]
c = csr_matrix((V, (I, J)))
print(c)
# several constructor forms, as with csr_matrix:
a = rsb_matrix((V, (I, J)))
a = rsb_matrix((V, (I, J)), [3, 3])
a = rsb_matrix((V, (I, J)), sym="S")  # symmetric example
print(a)
a = rsb_matrix((4, 4))
a = rsb_matrix(c)
nrhs = 1  # set to nrhs>1 to multiply by multiple vectors at once
nr = a.shape[0]
nc = a.shape[1]
order = "F"
x = numpy.empty([nc, nrhs], dtype=rsb_dtype, order=order)
y = numpy.empty([nr, nrhs], dtype=rsb_dtype, order=order)
x[:, :] = 1.0
y[:, :] = 0.0
print(a)
print(x)
print(y)
# import rsb # import operators
# a.autotune() # makes only sense for large matrices
y = y + a * x
# equivalent to y=y+c*x
print(y)
del a

Example Advanced Usage

# Example: demo2.py
"""
pyrsb demo
"""

import numpy
import scipy
from scipy.sparse import csr_matrix
from pyrsb import *

V = [11.0, 12.0, 22.0]
I = [0, 0, 1]
J = [0, 1, 1]
a = rsb_matrix((V, (I, J)))

nrhs = 4  # set to nrhs>1 to multiply by multiple vectors at once
nr = a.shape[0]
nc = a.shape[1]

# Choose Fortran or "by columns" order here.
order = "F"
x = numpy.empty([nc, nrhs], dtype=rsb_dtype, order=order)
y = numpy.empty([nr, nrhs], dtype=rsb_dtype, order=order)
x[:, :] = 1.0
y[:, :] = 0.0
print(a)
print(x)
print(y)

# Autotuning example: use it if you need many multiplication iterations on huge matrices (>>1e6 nonzeroes).
# Here general (nrhs=1) case:
a.autotune()
# Here with all the autotuning parameters specified:
a.autotune(1.0,1,2.0,'N',1.0,nrhs,'F',1.0,False)

# Inefficient: reallocate y
y = y + a * x
# Inefficient: reallocate y
y += a * x

# Equivalent but more efficient: don't reallocate y
a._spmm(x,y)

print(y)

del a

License

GPLv3+