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librsb is ahigh performance sparse matrixlibrary implementing the Recursive Sparse Blocks format,which is especially well suited formultiplications initerative methods onvery 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 tocontact me.

The following functionality is implemented:

• Initialization withrsb.rsb_matrix() styled asscipy.sparse.csr_matrix().
• Conversion fromscipy.sparse.csr_matrix().
• Multiplication by vector/multivector.
• Rows/columns throughnr=a.shape()[0]/nr=a.shape()[1], ornr()/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'))

• If you want theMakefile to build librsb (in this directory):make all-local will attempt downloading librsb-1.3.0.0 from theweb 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 (ormake lp) will build pyrsbusing 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 havecython,scipy,numpy. installed.
• If you don't have librsb installed you may want to try viapippip 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 checklibrsb's web site.
• make test will test benchmark code usingtest.py (to compare speed to SciPy)
• make b will also produce graphs (requiresgnuplot)

# Example: demo1.py"""pyrsb demo"""importnumpyimportscipyfromscipy.sparseimportcsr_matrixfrompyrsbimport*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 exampleprint(a)a=rsb_matrix((4,4))a=rsb_matrix(c)nrhs=1# set to nrhs>1 to multiply by multiple vectors at oncenr=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[:, :]=1.0y[:, :]=0.0print(a)print(x)print(y)# import rsb # import operators# a.autotune() # makes only sense for large matricesy=y+a*x# equivalent to y=y+c*xprint(y)dela

# Example: demo2.py"""pyrsb demo"""importnumpyimportscipyfromscipy.sparseimportcsr_matrixfrompyrsbimport*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 oncenr=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[:, :]=1.0y[:, :]=0.0print(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 yy=y+a*x# Inefficient: reallocate yy+=a*x# Equivalent but more efficient: don't reallocate ya._spmm(x,y)print(y)dela

GPLv3+