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Introduction

StarNEig library aims to provide a complete task-based software stack for solving dense nonsymmetric (generalized) eigenvalue problems. The library is built on top of the StarPU runtime system and targets both shared memory and distributed memory machines. Some components of the library support GPUs.

The four main components of the library are:

  • Hessenberg(-triangular) reduction: A dense matrix (or a dense matrix pair) is reduced to upper Hessenberg (or Hessenberg-triangular) form.
  • Schur reduction (QR/QZ algorithm): A upper Hessenberg matrix (or a Hessenberg-triangular matrix pair) is reduced to (generalized) Schur form. The (generalized) eigenvalues can be determined from the diagonal blocks.
  • Eigenvalue reordering and deflating subspaces: Reorders a user-selected set of (generalized) eigenvalues to the upper left corner of an updated (generalized) Schur form.
  • Computation of eigenvectors: Computes (generalized) eigenvectors for a user-selected set of (generalized) eigenvalues.

The library has been developed as a part of the NLAFET project. The project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 671633. Support has also been received from eSSENCE, a collaborative e-Science programme funded by the Swedish Government via the Swedish Research Council (VR), and VR Grant E0485301. The development and performance evaluations were performed on resources provided by the Swedish National Infrastructure for Computing (SNIC) at HPC2N partially funded by VR through grant agreement No. 2016-07213. The library is published under open-source BSD 3-Clause license.

Please cite the following article when referring to StarNEig:

Mirko Myllykoski, Carl Christian Kjelgaard Mikkelsen: Task-based, GPU-accelerated and Robust Library for Solving Dense Nonsymmetric Eigenvalue Problems, Concurrency and Computation: Practice and Experience, 33 (11), 2021 (online since 2020; e5915), doi: 10.1002/cpe.5915

Please see publications and authors.

Performance

Performance comparison against LAPACK (with parallel BLAS) using 25 CPU cores and a Nvidia V100 GPU:

Performance comparisons against MAGMA (Nvidia V100 GPU) and ScaLAPACK (distributed memory), and strong scalability on shared and distributed memory machines:

Also, see following publications:

  • Mirko Myllykoski: Algorithm 1019: A Task-based Multi-shift QR/QZ Algorithm with Aggressive Early Deflation, ACM Transactions on Mathematical Software, 48 (1), Article 11, pp. 1-36, 2022 (online since 2021), doi: 10.1145/3495005
  • Mirko Myllykoski, Carl Christian Kjelgaard Mikkelsen: Task-based, GPU-accelerated and Robust Library for Solving Dense Nonsymmetric Eigenvalue Problems, Concurrency and Computation: Practice and Experience, 33 (11), 2021 (online since 2020; e5915), doi: 10.1002/cpe.5915
  • Mirko Myllykoski, Carl Christian Kjelgaard Mikkelsen, Angelika Schwarz, Bo Kågström: D2.7 Eigenvalue solvers for nonsymmetric problems, public NLAFET deliverable, 2019 (download)

Current status (development series)

The development series is under continuous development and considered unstable. See stable 0.1-series.

The library currently supports only real arithmetic (real input and output matrices but real and/or complex eigenvalues and eigenvectors). In addition, some interface functions are implemented as LAPACK and ScaLAPACK wrapper functions.

Standard eigenvalue problems:

Component Shared memory Distributed memory CUDA
Hessenberg reduction Complete Incomplete Incomplete
Schur reduction Complete Complete Experimental
Eigenvalue reordering Complete Complete Experimental
Eigenvectors Complete --- ---

Generalized eigenvalue problems:

Component Shared memory Distributed memory CUDA
HT reduction LAPACK 3rd party ---
Schur reduction Complete Complete Experimental
Eigenvalue reordering Complete Complete Experimental
Eigenvectors Complete --- ---

Please see changelog and known problems.

Documentation

The StarNEig User's Guide is available in both HTML and PDF formats at https://nlafet.github.io/StarNEig. The PDF version is also available under releases.

Quickstart guide

Installation

Prebuild binary packages are available under releases and can be installed with the following command:

$ sudo apt install ./StarNEig-v0.xx.yy-ubuntu-vv.uu.deb

The binary packages rely on mainstream StarPU packages and do not necessary provide full functionality.

For full functionality, it is recommended that StarNEig (and StarPU) are compiled from the source code, see below and/or the StarNEig User's Guide. Please consider using the stable 0.1 series; preferably one of the tested release versions.

Dependencies

Library dependencies:

  • Linux
  • CMake 3.3 or newer
  • Portable Hardware Locality (hwloc)
  • Starpu 1.2 or 1.3
    • Newer versions require the user set the STARPU_LIBRARIES, STARPU_MPI_LIBRARIES and STARPU_INCLUDE_PATH environmental variables.
  • OpenBLAS, MKL, GotoBLAS or single-threaded BLAS library
  • LAPACK
  • MPI (optional)
  • CUDA + cuBLAS (optional)
  • ScaLAPACK + BLACS (optional)

Test program and example code dependencies:

  • pkg-config
  • GNU Scientific Library (optional)
  • MAGMA (optional)

Configure, build and install

To compile, validate and install StarNEig, execute in the same directory as this README.md file:

$ mkdir build
$ cd build/
$ cmake ../
$ make
$ make test
$ sudo make install

Example

The following example demonstrates how a dense matrix A is reduced to real Schur form:

// my_program.c
#include <starneig/starneig.h>
#include <stdlib.h>
#include <time.h>

int main()
{
    int n = 3000;
    srand((unsigned) time(NULL));

    // generate a random matrix A
    int ldA = ((n/8)+1)*8;
    double *A = malloc(n*ldA*sizeof(double));
    for (int j = 0; j < n; j++)
        for (int i = 0; i < n; i++)
            A[j*ldA+i] = 2.0*rand()/RAND_MAX - 1.0;

    // generate an identity matrix Q
    int ldQ = ((n/8)+1)*8;
    double *Q = malloc(n*ldA*sizeof(double));
    for (int j = 0; j < n; j++)
        for (int i = 0; i < n; i++)
            Q[j*ldQ+i] = i == j ? 1.0 : 0.0;

    // allocate space for the eigenvalues
    double *real = malloc(n*sizeof(double));
    double *imag = malloc(n*sizeof(double));

    // initialize the StarNEig library
    starneig_node_init(STARNEIG_USE_ALL, STARNEIG_USE_ALL, STARNEIG_HINT_SM);

    // reduce matrix A to real Schur form S = Q^T A Q
    starneig_SEP_SM_Reduce(
        n, A, ldA, Q, ldQ, real, imag, NULL, NULL, NULL, NULL);

    // de-initialize the StarNEig library
    starneig_node_finalize();

    free(A); free(Q); free(real); free(imag);

    return 0;
}

Compile:

$ gcc -o my_program my_program.c -lstarneig

or:

$ gcc -o my_program my_program.c $(pkg-config --cflags starneig) $(pkg-config --libs starneig)