Features of PARDISO 8.0
-
Solves unsymmetric, structurally symmetric or symmetric systems, real or complex, positive definite or indefinite, hermitian.
-
LU decomposition with complete pivoting and extremely fast selected inversion strategies.
-
PARDISO 8.0 enables you to rapidly and accurately reduce computing time by taking advantage of SMP and MPP parallel processing technology.
-
Industrial strength implementation - the new release PARDISO 8.0 has been licensed to various leading industrial software vendors such as Dassault, NXP, Siemens, or Formula One Racing Teams.
-
As of version PARDISO 8.0: NVIDIA GPU support for unsymmetric matrices, very effective for sparse matrices.
PANUA-PARDISO 8.0 was released in February 2023. It contains full support of multi-threaded Schur-complement computations, parallel selected inversion, incremental updates, and many others accelerations. Some of the new features have been used, e.g., in the following papers:
- C. Alappat, G. Hager, O. Schenk, J. Thies, A. Basermann, A. Bischop, H. Fehske, G. Wellein, A Recursive Algebraic Coloring Technique for Hardware-Efficient Symmetric Sparse Matrix-Vector Multiplication, ACM Transactions on Parallel Computing, Vol. 7, No. 3(19), 2020.
- M. Bollhöfer, O. Schenk , R. Janalik, S. Hamm, K. Gullapalli, State-of-The-Art Sparse Direct Solvers, Parallel Algorithms in Computational Science and Engineering. Modeling and Simulation in Science, Engineering and Technology, 1-32, Birkhäuser, 2020.
- M. Bollhöfer, A. Eftekhari, S. Scheidegger, and O. Schenk, Large-Scale Sparse Inverse Covariance Matrix Estimation, SIAM J. Sci. Comput., 41(1), A380–A401, 2019.
Additional publications related to PARDISO are available on the webpage of the research group of Prof. Olaf Schenk, Advanced Computing Laboratory, Institute of Computing, USI Lugano, Switzerland.
Important: Please note that the Intel MKL version of PARDISO is based on our version from 2006 and that a lot of new features and improvements of PARDISO are not available in the Intel MKL library.
Benchmarks of PARDISO 8.0
MKL PARDISO vs PARDISO 8.0 Performance Comparison, NXP Semiconductors
PARDISO 8.0 is benchmarked against Intel MKL PARDISO version 2020 on up to 16 cores on an Intel Xeon E7-4880. The matrices are from NXP's industrial circuit simulator Mica. The plots shows the performance acceleration for the factorization and solution phase against MKL PARDISO.
MKL PARDISO vs PARDISO 8.0 Performance Comparison, Silvaco Circuit Simulator
PARDISO 8.0 is benchmarked against Intel MKL PARDISO version 2020 on up to 32 cores on an Intel Xeon Intel E5-2650 with 2.30GHz architecture. The matrices are from Silvaco industrial circuit simulator. The plots shows the performance acceleration for the factorization and solution phase against MKL PARDISO.
PARDISO 8.0 k-rank Update Analysis, NXP Semiconductor
Regression analysis on the k-rank update LU factorization in PARDISO. The scatter plot shows the number of k-rank updates and the corresponding factorization time in milliseconds. The regression analysis cleary demonstates a linear trend both for the single and the multiple core version. The dashed line shows the time for the full factorization.
How To Use PARDISO
Examples
-
C
- symmetric linear systems
- unsymmetric linear systems
- complex unsymmetric linear systems
- symmetric linear systems showing an example based on a discretization of the 2D Laplacian equation (laplace.c, laplace.h)
- scalable MPI C example for symmetric linear systems showing a parallel MPI example based on a discretization of the 2D Laplacian equation (laplace_mpi.tgz)
-
C++
-
C++, Schur Complement
-
Matlab
MATLAB interface for all types of linear systems in PARDISO
(by Peter Carbonetto, Dept. of Human Genetics, Univ. Chicago)
pardiso-matlab.tgz -
Julia
Julia interface for all types of linear systems in PARDISO
(by Kristoffer Carlsson , Chalmers University of Technology, Göteborg).
-
Fortran
Download & License
Academic Licenses / Commercial Licensing Program
Here you can dowload a cost-free academic license or a commercial license for the current release of PANUA-PARDISO. Currently, only Linux versions are available. We are actively working on Windows and Mac versions of the software and they will be available as soon as released. The option commercial is only available for selected commercial or commercial entities. Please choose the desired license type:
News
-
February 2023
Release of Version 8.0 including faster LU updates, initial GPU version for unsymmetric matrices, new licensing options. (release notes) -
December 2020
Release of Version 7.2 including faster LU updates, full support of AMD orderings, several important bug fixes, improved pivoting for symmetric indefinite matrices. (release notes) -
April 2019
Release of Version 6.2 including fast supernodal incremental LU updates, improved factorization routines, and approximate minimum degree orderings (release notes) -
May 2018
Release of Version 6.0 for the R-INLA project including supernodal data compression and selected inversion software (release notes) -
November 2015
Release of new webpage, including user map and list of successful recent consulting projects. -
January 2014
Release of Version 5.0.0, including new Schur-complement solver and selected inversion software (release notes). -
April 2011
Release of Version 4.1.2, including the distributed memory solver (release notes). -
January 2011
Release of Version 4.1.0 (release notes). -
March 2010
Map of pardiso users -
October 2009
Release of Version 4.0.0 (release notes). -
November 2007
The solver is now available for both academic and commercial use. -
July 2007
The solver PARDISO is now available under www.pardiso-project.org.
References
In case that you are using the PARDISO 8.0 please cite:
- C. Alappat, G. Hager, O. Schenk, J. Thies, A. Basermann, A. Bischop, H. Fehske, G. Wellein, A Recursive Algebraic Coloring Technique for Hardware-Efficient Symmetric Sparse Matrix-Vector Multiplication, ACM Transactions on Parallel Computing, Vol. 7, No. 3(19), 2020.
- M. Bollhöfer, O. Schenk , R. Janalik, S. Hamm, K. Gullapalli, State-of-The-Art Sparse Direct Solvers, Parallel Algorithms in Computational Science and Engineering. Modeling and Simulation in Science, Engineering and Technology, 1-32, Birkhäuser, 2020.
- M. Bollhöfer, A. Eftekhari, S. Scheidegger, and O. Schenk Large-Scale Sparse Inverse Covariance Matrix Estimation, SIAM J. Sci. Comput., 41(1), A380–A401, 2019.
Map of PARDISO Users from Academic Institutions
(Year 2023)
Contact
Email: contact@panua.ch