Mondriaan is a sequential program written in C that can be used to partition a rectangular sparse matrix, an input vector, and an output vector for parallel sparse matrix-vector multiplication. The program is based on a recursive bipartitioning algorithm that cuts the matrix horizontally and vertically, in a manner resembling some of the famous Mondriaan paintings. The algorithm is multilevel, hypergraph-based, and two-dimensional. It reduces the amount of communication and it spreads both computation and communication evenly over the processors. The program can partition hypergraphs with integer vertex weights and uniform hyperedge costs, but it is primarily intended as a matrix partitioner.

This version fixes a bug when using a non-default option in version 4.2: if a 1D partitioning is chosen by Splitstrategy=onedimrow or onedimcol, and CheckUpperBound=yes this may cause the CheckUpperBound function to create a 2D partitioning instead of 1D in rare cases (when the communication volume is high). In such a case for rectangular matrices it would create a partitioning with volume (max(m,n)+1)(P-1), instead of the volume (min(m,n)+1)(P-1) that could have been achieved. Thanks to Steven Fleuren and Femke van Ieperen for discovering this bug.

Furthermore, this version removes some harmless compiler warnings and improves bad behaviour by the Makefile, which forgot to remove one file (testHelper_DisconnectedMatrix.o) when cleaning up.

- A zero-volume search based on finding connected components is performed before every bipartitioning, to perform easy splits quickly if they exist.
- Improved load balance by moving free nonzeros (in a row and column that are both cut) after every bipartitioning.
- Overall speed improvements.
- A separate MondriaanStats program which is able to quickly determine the communication volume and other information about a computed partitioning.

- The MondriaanOpt program, which offers a way to solve the bipartitioning problem to optimality. The MondriaanOpt program is available as stand-alone program, as Matlab routine and as linkable archive.
- MondriaanPlot can now also generate SVG (Scalable Vector Graphics) images of partitionings.
- Some bugfixes and fixes for some compiler warnings, as well as updates to the user's manual.

- The medium-grain partitioning method, which offers both improved quality and faster partitioning compared to the localbest (pure Mondriaan) method. Medium-grain is also the new default partitioning strategy.
- More advanced matching of hypergraph vertices in the coarsening phase of the partitioning, through the use of a better matching algorithm (PGA'), which is guaranteed to generate matchings that have at least half the weight of the heaviest possible matching.
- Support for a new hypergraph cost metric which measures the communication volume of all-neighbor communication: the lambda*(lambda - 1)-metric.
- Improved quality through iterative refinement of bipartitionings via medium-grain.

- The Extended Matrix-Market (EMM) output format enables the storage of many matrix-like objects in a single file. In the case of Mondriaan, this potentially decreases the number of output files from 5 or more for partitioning a single matrix, to a single file. (By no means is the EMM format tied to Mondriaan: the format can easily and portably be used to share more complex matrix/vector-formulated problems.) Since it remains close to the original Matrix-Market format, existing parsers are envisioned to be adapted with minimal effort.
- The hierarchy of the separator blocks is now included in the output, for all permutation strategies, i.e. Separated Block Diagonal (SBD) and (reverse) Bordered Block Diagonal (BBD). This enables building a full binary tree out of the SBD and BBD structures.
- Mondriaan can now force symmetric permutations, that is, generate PAP^T for general A. In particular, Mondriaan is now able to retain the symmetry of symmetric input matrices.
- The symmetric finegrain splitting strategy. This represents a symmetric sparse matrix A using half the number of vertices and hyperedges, compared to the regular finegrain method. This significantly improves partitioning time for symmetric input matrices, albeit at the cost of some loss in quality, when compared to finegrain.
- The Matlab interface now makes more Mondriaan output available to the users. This enables easy direct application, e.g. to reduce the fill-in in LU-type methods and to accelerate cache-sensitive computations. See also the revised User's guide for Mondriaan's Matlab interface.

- New algorithms for matrix ordering into Separated Block Diagonal (SBD) form suitable for cache-oblivious sparse matrix-vector multiplication, and also into Bordered Block Diagonal (BBD) form suitable for and sparse LU decomposition.
- Better quality of the matrix partitioning, in particular for fine-grain partitioing.
- Hypergraph partitioning by the cut-net metrics, as well as the common (lambda-1)-metric.
- Matlab (TM) interface. Sparse matrices can be partitioned within the Matlab environment.
- PaToH interface. Mondriaan can now also use PaToH as a hypergraph bipartitioner, instead of its native bipartitioner.
- Visualisation of the partitioning process, by the MondriaanPlot and MondriaanMovie programs.
- Mondriaan has now been made a separate library which can easily be intergrated into existing applications.
- The package has been tested on a variety of operating systems including Linux, Mac OS X, and a variety of architectures, including X86_64, Apple Mac, IBM BlueGene/L, and others.
- The User's guide. has been extended and includes a simple example program that shows how to use Mondriaan as a library. There is also a separate User's guide for Mondriaan's Matlab interface

- New algorithms for vector partitioning, which often achieve the best communication load balance for the given matrix partitioning.
- Much faster partitioning, by a factor of 10; this improvement was already incorporated in the minor release v1.02 from 2005.
- Better quality of the matrix partitioning. On average, communication volume is reduced by 10%, mainly due to scaling of the inner products of matrix columns in the coarsening phase.
- Inclusion of the finegrain partitioning method (proposed by Catalyurek and Aykanat 2001).
- Inclusion of a hybrid between the original Mondriaan method and the finegrain method.
- Some new hypergraph partitioning capabilities. Mondriaan 2.0 can handle hypergraphs with arbitrary integer vertex weights, but only with uniform hyperedge costs.
- Can also handle non-powers of two for the number of processors.
- Full documentation of every function. (Total 96 functions.)
- A unit test for every function.
- Package has been tested on Linux, Mac OS X, Solaris, and using Valgrind for finding memory leaks.
- User's guide.
- More liberal license: GNU Lesser General Public License

Changes the data structure used in the inner product matching of the coarsening. This makes the software about 10 times faster. Thanks to Umit Catalyurek who pointed out an inefficiency in my previous version. This concerns function FindMatchIM in file HKLFM.c. Another change is that the (non-default) option of weighted edges is removed. This option was inferior to the default option and hence should not be used anyway. Note that to use V1.02 you must discard the old file mondriaan.defaults. Mondriaan will then automatically create a new default file.

Makes comments ANSI-C compliant, delimiting them by /* ... */. Fixes the following two bugs.

- May 31, 2002. Wouter Meesen: For compiling on a Windows system with the visual c++ compiler, you have to remove all the words 'inline' from the source code. This word occurs in the files: DistributeVec.h, GainBucket.c, GainBucket.h, HKLFM.c, HKLFM.h. Same remark holds for Cray T3E compiler CC (Joris Koster, May 2002).
- Sept. 2, 2003. A remark by a sharp-eyed anonymous referee of our paper led us to reexamine the conversion from a sparse symmetric matrix to the full version in the function SparseMatrixSymmetric2Full from the file SparseMatrix.c. This function contains an error affecting the first nonzero. The error only occurs for symmetric matrices stored in symmetric input format. Thanks to the referee!

The original Mondriaan algorithm and implementation has been published in
"A Two-Dimensional Data Distribution Method
for Parallel Sparse Matrix-Vector Multiplication",
by Brendan Vastenhouw and Rob H. Bisseling,
*SIAM Review*, **47**, No. 1 (2005) pp. 67-95.

The new vector partitioning algorithms and implementations have been published in
"Communication balancing in parallel
sparse matrix-vector multiplication",
by Rob H. Bisseling and Wouter Meesen,
*Electronic Transactions on Numerical Analysis*,
**21** (2005) pp. 47-65,
special issue on Combinatorial Scientific Computing.

The new matrix ordering algorithms and implementations have been published in
Cache-oblivious sparse matrix-vector multiplication
by using sparse matrix partitioning methods by Albert-Jan N. Yzelman
and Rob H. Bisseling,
*SIAM Journal on Scientific Computing*,
** 31**, No. 4 (2009) pp. 3128-3154.

An extension to 2D is given in
Two-dimensional cache-oblivious sparse matrix-vector multiplication
by A. N. Yzelman and Rob H. Bisseling,
*Parallel Computing*, **37**, No. 12 (2011) pp. 806-819.

The hybrid between the original Mondriaan method and
the finegrain method has been published as a book chapter
Two-Dimensional Approaches to Sparse Matrix Partitioning
by Rob H. Bisseling, Bas O. Fagginger Auer, A. N. Yzelman, Tristan van Leeuwen and Ümit V. Çatalyürek,
Chapter 12 of *Combinatorial Scientific Computing*, Chapman and Hall/CRC, pp. 321-349 (2012).

The lambda*(lambda - 1)-metric is introduced in
A new metric enabling an exact hypergraph model for the communication volume in distributed-memory parallel applications
by O. Fortmeier, H. M. Bücker, B. O. Fagginger Auer, R. H. Bisseling, *Parallel Computing*, **39**, No. 8 (2013) pp. 319-335.

The medium-grain method is described in A medium-grain method for fast 2D bipartitioning of sparse matrices by D. M. Pelt and R. H. Bisseling, Proceedings IEEE International Parallel & Distributed Processing Symposium 2014, IEEE Press, pp. 529-539.

An exact algorithm for obtaining an optimal partitioning of small matrices for 2 processors is given in
An
exact algorithm for sparse matrix bipartitioning,
by Daniel M. Pelt and Rob H. Bisseling,
*Journal of Parallel and Distributed Computing*, **85** (2015) pp. 79-90.
Postprint version.
The corresponding program MondriaanOpt has been included in Mondriaan version 4.1.

Extensive background reading on the parallel sparse matrix-vector multiplication problem, including a detailed discussion of the Mondriaan algorithm, can be found in Chapter 4 (pp. 163-250) of Parallel Scientific Computation: A Structured Approach using BSP and MPI, by Rob H. Bisseling, Oxford University Press, March 2004. 324 pages. ISBN 978-0-19-852939-2. OUP home page of the book.

- CECAM Workshop Open Source Software for Microscopic Calculations, June 19-21, 2002, Lyon, France: Mondriaan, partitioning software for sparse matrix computations (24 transparancies, 719kB, PDF format, generated using Prosper)
- A hybrid 2D method for sparse matrix partitioning by Rob Bisseling, Tristan van Leeuwen, and Umit Catalyurek. Lecture at SIAM Conference on Parallel Processing for Scientific Computing San Francisco, February 22-24, 2006. (29 transparancies, 612kB, PDF format).
- Sparse matrix partitioning, ordering, and visualisation by Rob Bisseling, Albert-Jan Yzelman, and Bas Fagginger Auer. Lecture at Parallel Matrix Algorithms and Applications, Basel, Switzerland, June 29 - July 2, 2010.

This software is copyrighted (2002, 2008, 2010, 2013, 2016, 2017) by Rob Bisseling, Bas Fagginger Auer, Tristan van Leeuwen, Wouter Meesen, Marco van Oort, Daan Pelt, Brendan Vastenhouw, Albert-Jan Yzelman. You can use and modify it under the GNU Lesser General Public License, see GNU Licenses. Also see the files, README, COPYING, COPYING.LESSER. Anything free, as usual, comes with no guarantee!

- Umit Catalyurek: interface to finegrain partitioning, PaToH
- Ken Stanley: visualisation

Most matrices we use in our tests can be obtained from
Tim Davis'
University of Florida Sparse Matrix Collection.
The term-by-document matrices used in the SIAM Review paper are:
`tbdmatlab.mtx`
(430171 nonzeros, 3.2 Mbyte) and
`tbdlinux.mtx`
(2157675 nonzeros, 18.9 Mbyte), both in gzip-compressed Matrix Market format.
The RSA matrices used in the paper
"Mondriaan sparse matrix partitioning for attacking cryptosystems
by a parallel block Lanczos algorithm - a case study"
by Rob H. Bisseling and Ildiko Flesch,
*Parallel Computing*
**32** Nr. 7/8 (2006) pp. 551-567,
Final preprint (Sept 2006),
are:
`rsa_c82.mtx`
(16338 rows, 16307 columns, 507716 nonzeros) and
`rsa_c98.mtx`
56274 rows, 56243 columns, 2075889 nonzeros).
Note that the paper uses the transposed of the matrices given here.
The matrices are courtesy of Richard Brent.

Last updated August 19, 2019.

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