cfm-mpc / pyrsb

PyRSB is a Python interface to librsb

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 huge sparse matrices.

PyRSB is a Cython-based Python interface to librsb.

So far, PyRSB is a working prototype, so prospective users and collaborators feedback is sought; please contact me to feedback and help.

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].
• find(), find_block(), tril(), triu(), shape(), nnz().
• print'able.
• PyRSB-Specific: autotune(), do_print().

Build and Use

• If you have librsb installed: make will attempt building and testing. Make sure you have cython, scipy, numpy. installed.
• If you want the Makefile to build librsb (in this directory): make all-local will attempt downloading librsb-1.2.0-rc7 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.
• make test will test benchmark code using test.py (to compare speed to SciPy)
• make b will also produce graphs (requires gnuplot)

Example Usage

	import numpy
	import scipy
	from scipy.sparse import csr_matrix
	from rsb          import rsb_matrix
	V=[11.,12.,22.]
	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))
	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]
	x=numpy.empty([nc,nrhs],dtype=scipy.double)
	y=numpy.empty([nr,nrhs],dtype=scipy.double)
	x[:,:]=1.0
	y[:,:]=0.0
	print(a)
	print(x)
	print(y)
	import rsb # import operators
	# a.autotune() # makes only sense for large matrices
	# matrix-vector multiply
	y=y+a*x; # equivalent to y=y+c*x
	print(y)
	del a

License

GPLv3+