Welcome to Panua-Pardiso

Pardiso 8.0 Solver Project (August 2023)

The package Pardiso is a thread-safe, high-performance, robust, memory efficient and easy to use software for solving large sparse symmetric and unsymmetric linear systems of equations on shared-memory, distributed-memory multiprocessors, NVIDIA's GPUs, and ARM architectures.

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Panua-Pardiso

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:

D. Pasadakis, M. Bollhöfer, and O. Schenk, Sparse quadratic approximation for graph learning, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 45, no. 9, pp. 11256-11269, 2023.

A. Eftekhari, D. Pasadakis, M. Bollhöfer, S. Scheidegger, and O. Schenk,, Block-enhanced precision matrix estimation for large-scale datasets, Journal of Computational Science, Volume 53, 2021.

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.

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.

Features

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 - Pardiso 8.0 has been licensed to various leading industrial software vendors such as Dassault, NXP, Siemens, or Formula One racing teams.

NVIDIA GPU support for unsymmetric matrices, very effective for sparse matrices.

Easily integrate Panua-Pardiso as a dynamic or static library.

Benchmarks of Pardiso 8.0

Pardiso vs Pardiso 8.0 performance comparison, NXP semiconductors

MKL Pardiso

Pardiso 8.0

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.

Pardiso vs Pardiso 8.0 performance comparison, Silvaco circuit simulator

MKL Pardiso

Pardiso 8.0

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

1 core; y=0.33k

8 cores; y=0.21k

Full factorization

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.

Usage

Pardiso user guide 8.0 Reference sheet

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

Fortran

Map of Pardiso users from academic institutions
(Year 2023)