]
id="x3-2000">Contents
- Pasqua D’Ambra, IAC-CNR, IT;
+
+
+ [1] A. Aprovitola, P. D’Ambra, F. Denaro, D. di Serafino, S. Filippone,
+ Scalable algebraic multilevel preconditioners with application to CFD, in Proc.
+ of CFD 2008, LNCSE, 74, (2010), 15–27.
+
+ [2] P. R. Amestoy, C. Ashcraft, O. Boiteau, A. Buttari, J. L’Excellent,
+ C. Weisbecker, Improving multifrontal methods by means of block low-rank
+ representations, SIAM Journal on Scientific Computing, volume 37 (3), 2015,
+ A1452–A1474. See also http://mumps.enseeiht.fr.
+
+ [3] D. Bertaccini and S. Filippone, Sparse approximate inverse
+ preconditioners on high performance GPU platforms, Comput. Math. Appl.,
+ 71, (2016), no. 3, 693–711.
+
+ [4] M. Brezina, P. Vaněk, A Black-Box Iterative Solver Based on a
+ Two-Level Schwarz Method, Computing, 63, 1999, 233–263.
+
+ [5] W. L. Briggs, V. E. Henson, S. F. McCormick, A Multigrid Tutorial,
+ Second Edition, SIAM, 2000.
+
+ [6] A. Buttari, P. D’Ambra, D. di Serafino, S. Filippone, Extending
+ PSBLAS to Build Parallel Schwarz Preconditioners, in J. Dongarra,
+
+
+
+ K. Madsen, J. Wasniewski, editors, Proceedings of PARA 04 Workshop on
+ State of the Art in Scientific Computing, Lecture Notes in Computer Science,
+ Springer, 2005, 593–602.
+
+ [7] A. Buttari, P. D’Ambra, D. di Serafino, S. Filippone, 2LEV-D2P4: a
+ package of high-performance preconditioners for scientific and engineering
+ applications, Applicable Algebra in Engineering, Communications and
+ Computing, 18 (3) 2007, 223–239.
+
+ [8] X. C. Cai, M. Sarkis, A Restricted Additive Schwarz Preconditioner for
+ General Sparse Linear Systems, SIAM Journal on Scientific Computing, 21
+ (2), 1999, 792–797.
+
+ [9] U.. V. Catalyurek, F. Dobrian, A. Gebremedhin, M. Halappanavar,
+ and A. Pothen, Distributed-memory parallel algorithms for matching and
+ coloring, in PCO’11 New Trends in Parallel Computing and Optimization,
+ IEEE International Symposium on Parallel and Distributed Processing
+ Workshops, IEEE CS, 2011.
+
+ [10] P. D’Ambra, S. Filippone,
+ D. di Serafino, On the Development of PSBLAS-based Parallel Two-level
+ Schwarz Preconditioners, Applied Numerical Mathematics, Elsevier Science,
+ 57 (11-12), 2007, 1181-1196.
+
+ [11] P. D’Ambra, D. di Serafino, S. Filippone, MLD2P4: a Package of
+ Parallel Multilevel Algebraic Domain Decomposition Preconditioners in
+ Fortran 95, ACM Trans. Math. Softw., 37(3), 2010, art. 30.
+
+ [12] P. D’Ambra and P. S. Vassilevski, Adaptive AMG with coarsening based
+ on compatible weighted matching, Computing and Visualization in Science,
+ 16, (2013) 59–76.
+
+
+
+
+ [13] P. D’Ambra, S. Filippone and P. S. Vassilevski, BootCMatch: a software
+ package for bootstrap AMG based on graph weighted matching, ACM
+ Transactions on Mathematical Software, 44, (2018) 39:1–39:25.
+
+ [14] P. D’Ambra, F. Durastante, S. Filippone, AMG preconditioners for
+ Linear Solvers towards Extreme Scale, SIAM Journal on Scientific Computing
+ 43, no. 5 (2021): S679-S703.
+
+ [15] P. D’Ambra, F. Durastante, S. Filippone, S. Massei, S. Thomas
+ Optimal Polynomial Smoothers for Parallel AMG, 2024, arXiv:2407.09848.
+
+ [16] T. A. Davis, Algorithm 832: UMFPACK
+ - an Unsymmetric-pattern Multifrontal Method with a Column Pre-ordering
+ Strategy, ACM Transactions on Mathematical Software, 30, 2004, 196–199.
+ (See also http://www.cise.ufl.edu/~davis/)
+
+ [17] J. W. Demmel, S. C. Eisenstat, J. R. Gilbert,
+ X. S. Li, J. W. H. Liu, A supernodal approach to sparse partial pivoting,
+ SIAM Journal on Matrix Analysis and Applications, 20 (3), 1999, 720–755.
+
+ [18] J. J. Dongarra, J. Du Croz, I. S. Duff, S. Hammarling, A set of Level
+ 3 Basic Linear Algebra Subprograms, ACM Transactions on Mathematical
+ Software, 16 (1) 1990, 1–17.
+
+ [19] J. J. Dongarra, J. Du Croz, S. Hammarling, R. J. Hanson, An
+ extended set of FORTRAN Basic Linear Algebra Subprograms, ACM
+ Transactions on Mathematical Software, 14 (1) 1988, 1–17.
+
+
+
+
+ [20] S. Filippone, P. D’Ambra, M. Colajanni, Using a Parallel Library of
+ Sparse Linear Algebra in a Fluid Dynamics Application Code on Linux
+ Clusters, in Proc. of ParCo 2001, Parallel Computing, Advances and Current
+ Issues, 2002.
+
+ [21] S. Filippone, A. Buttari, PSBLAS 3.5.0 User’s Guide. A Reference
+ Guide for the Parallel Sparse BLAS Library, 2012, available from
+ https://github.com/sfilippone/psblas3/tree/master/docs.
+
+ [22] S. Filippone, A. Buttari, Object-Oriented Techniques for Sparse Matrix
+ Computations in Fortran 2003. ACM Transactions on on Mathematical
+ Software, 38 (4), 2012, art. 23.
+
+ [23] S. Filippone, M. Colajanni, PSBLAS: A
+ Library for Parallel Linear Algebra Computation on Sparse Matrices, ACM
+ Transactions on Mathematical Software, 26 (4), 2000, 527–550.
+
+ [24] S. Gratton, P. Henon, P. Jiranek and X. Vasseur, Reducing complexity of
+ algebraic multigrid by aggregation, Numerical Lin. Algebra with Applications,
+ 2016, 23:501-518
+
+ [25] W. Gropp, S. Huss-Lederman, A. Lumsdaine, E. Lusk, B. Nitzberg,
+ W. Saphir, M. Snir, MPI: The Complete Reference. Volume 2 - The MPI-2
+ Extensions, MIT Press, 1998.
+
+ [26] C. L. Lawson, R. J. Hanson, D. Kincaid, F. T. Krogh, Basic Linear
+ Algebra Subprograms for FORTRAN usage, ACM Transactions on
+ Mathematical Software, 5 (3), 1979, 308–323.
-
- Fabio Durastante, University of Pisa and IAC-CNR, IT;
- Salvatore Filippone, University of Rome Tor-Vergata and IAC-CNR, IT;
+ [27] J. Lottes, Optimal polynomial smoothers for multigrid V-cycles,
+ Numerical Linear Algebra with Applications 30.6 (2023): e2518.
+
+ [28] X. S. Li, J. W. Demmel, SuperLU_DIST: A Scalable
+ Distributed-memory Sparse Direct Solver for Unsymmetric Linear Systems,
+ ACM Transactions on Mathematical Software, 29 (2), 2003, 110–140.
+
+ [29] Y. Notay, P. S. Vassilevski, Recursive Krylov-based multigrid cycles,
+ Numerical Linear Algebra with Applications, 15 (5), 2008, 473–487.
+
+ [30] Y. Saad, Iterative methods for sparse linear systems, 2nd edition, SIAM,
+ 2003.
+
+ [31] B. Smith, P. Bjorstad, W. Gropp, Domain Decomposition: Parallel
+ Multilevel Methods for Elliptic Partial Differential Equations, Cambridge
+ University Press, 1996.
+
+ [32] M. Snir, S. Otto, S. Huss-Lederman, D. Walker, J. Dongarra, MPI:
+ The Complete Reference. Volume 1 - The MPI Core, second edition, MIT
+ Press, 1998.
+
+ [33] K. Stüben, An Introduction to Algebraic Multigrid, in A. Schüller,
+ U. Trottenberg, C. Oosterlee, Multigrid, Academic Press, 2001.
+
+ [34] R. S. Tuminaro, C. Tong, Parallel Smoothed Aggregation Multigrid:
+ Aggregation Strategies on Massively Parallel Machines, in J. Donnelley,
+ editor, Proceedings of SuperComputing 2000, Dallas, 2000.
-
+
+ [35] P. Vaněk, J. Mandel, M. Brezina, Algebraic Multigrid by Smoothed
+ Aggregation for Second and Fourth Order Elliptic Problems, Computing, 56
+ (3) 1996, 179–196.
diff --git a/docs/html/userhtmlse1.html b/docs/html/userhtmlse1.html
index d33979d4..ae661559 100644
--- a/docs/html/userhtmlse1.html
+++ b/docs/html/userhtmlse1.html
@@ -83,11 +83,11 @@ class="cmr-12">) provides parallel Algebraic MultiGrid (AMG) preconditioners (se
e.g., [5, 33]), to be used in the iterative solution of linear systems,
@@ -116,11 +116,11 @@ class="cmr-12">multigrid cycles include the V-, W-, and a version of a Krylov-ty
class="cmr-12">(K-cycle) [5, 29]; they can be combined with Jacobi, hybrid forward/backward
@@ -136,7 +136,7 @@ class="cmr-8">1
class="cmr-12">version [14].
@@ -154,22 +154,22 @@ class="cmr-12">strategies, based on aggregation, are available:
class="cmr-12">a decoupled version of the smoothed aggregation procedure proposed in [4,
35], and already included in the previous versions of the package [7, 11];
@@ -184,17 +184,17 @@ class="cmr-12">a coupled, parallel implementation of the Coarsening based on Com
class="cmr-12">Weighted Matching introduced in [12, 13] and described in detail in [14];
@@ -233,11 +233,11 @@ class="cmr-12">multilevel preconditioners in the context of the PSBLAS (Parallel
class="cmr-12">computational framework [23, 22]. PSBLAS provides basic linear algebra operators
@@ -301,7 +301,7 @@ class="cmr-12">smoothers and solvers for building new versions of the preconditi
Section 6).
, while details on the configuration and installation
of the package are given in Section 3. The basics for building and applying the
preconditioners with the Krylov solvers implemented in PSBLAS are
class="cmr-12">in Section 4, where the Fortran codes of a few sample programs are also shown.
A reference guide for the user interface routines is provided in Section 5.
Information on the extension of the package through the addition
smoothers and solvers is reported in Section 6. The error handling mechanism
used by the package is briefly described in Section 7. The copyright terms
concerning the distribution and modification of AMG4PSBLAS are re
Appendix A.
diff --git a/docs/html/userhtmlse2.html b/docs/html/userhtmlse2.html
index 554b6fa9..225a3eab 100644
--- a/docs/html/userhtmlse2.html
+++ b/docs/html/userhtmlse2.html
@@ -34,7 +34,7 @@ class="cmr-12">Code Distribution
AMG4PSBLAS is available from the web site
where contact points for further information can be also found. The software is available under a modified BSD license, as specified in Appendix A;
The library defines a version string with the constant
src="userhtml1x.png" alt="amg_version_string_
" class="math-display" > whose current value is
- Pasqua D’Ambra, IAC-CNR, IT;
+ Fabio Durastante, University of Pisa and IAC-CNR, IT;
+ Salvatore Filippone, University of Rome Tor-Vergata and IAC-CNR, IT;
+ When use the library, please cite the following:
+
+
+
+
+
+
+
+
+
+
+
+
up] Contributors
-
-
References
+
-
1.0whose current value is 1.0.
Contributors
+
+
+
+
+
+Citing AMG4PSBLAS
+
+@article{DDF2021,
+ author = {D’Ambra, Pasqua and Durastante, Fabio and Filippone, Salvatore},
+ title = {{{AMG Preconditioners for Linear Solvers towards Extreme Scale}},
+ journal = {arXiv e-preprints},
+ eprint = {2006.16147v3},
+ archivePrefix = {arXiv},
+ year={2021}
+ }
+
+@Misc{psctoolkit-web-page,
+ author = {D’Ambra, Pasqua and Durastante, Fabio and Filippone, Salvatore},
+ title = {{PSCToolkit} {W}eb page},
+ url = {https://psctoolkit.github.io/},
+ howpublished = {\url{https://psctoolkit.github.io/}},
+ year = {2021}
+ }
+
+