## bin+lib russell_sparse

Solvers for large sparse linear systems (wraps MUMPS and UMFPACK)

### 24 releases(3 stable)

 new 1.1.1 Apr 19, 2024 Mar 30, 2024 Mar 17, 2024 Oct 22, 2023 Oct 22, 2021

#37 in Math

Used in 4 crates

2MB
33K SLoC

# Russell Sparse - Solvers for large sparse linear systems (wraps MUMPS and UMFPACK)

This crate is part of Russell - Rust Scientific Library

## Introduction

This crate implements tools for handling sparse matrices and functions to solve large sparse systems using the best libraries out there, such as UMFPACK (recommended) and MUMPS (for very large systems).

We have three storage formats for sparse matrices:

• COO: COOrdinates matrix, also known as a sparse triplet.
• CSC: Compressed Sparse Column matrix
• CSR: Compressed Sparse Row matrix

Additionally, to unify the handling of the above sparse matrix data structures, we have:

• SparseMatrix: Either a COO, CSC, or CSR matrix

The COO matrix is the best when we need to update the values of the matrix because it has easy access to the triples (i, j, aij). For instance, the repetitive access is the primary use case for codes based on the finite element method (FEM) for approximating partial differential equations. Moreover, the COO matrix allows storing duplicate entries; for example, the triple (0, 0, 123.0) can be stored as two triples (0, 0, 100.0) and (0, 0, 23.0). Again, this is the primary need for FEM codes because of the so-called assembly process where elements add to the same positions in the "global stiffness" matrix. Nonetheless, the duplicate entries must be summed up at some stage for the linear solver (e.g., MUMPS, UMFPACK). These linear solvers also use the more memory-efficient storage formats CSC and CSR. See the russell_sparse documentation for further information.

This library also provides functions to read and write Matrix Market files containing (huge) sparse matrices that can be used in performance benchmarking or other studies. The [read_matrix_market()] function reads a Matrix Market file and returns a [CooMatrix]. To write a Matrix Market file, we can use the function [write_matrix_market()], which takes a [SparseMatrix] and, thus, automatically convert COO to CSC or COO to CSR, also performing the sum of duplicates. The write_matrix_market also writes an SMAT file (almost like the Matrix Market format) without the header and with zero-based indices. The SMAT file can be given to the fantastic Vismatrix tool to visualize the sparse matrix structure and values interactively; see the example below.

See the documentation for further information:

## Installation

At this moment, Russell works on Linux (Debian/Ubuntu; and maybe Arch). It has some limited functionality on macOS too. In the future, we plan to enable Russell on Windows; however, this will take time because some essential libraries are not easily available on Windows.

### TLDR (Debian/Ubuntu/Linux)

First:

sudo apt-get install -y --no-install-recommends \
g++ \
gdb \
gfortran \
libfftw3-dev \
liblapacke-dev \
libmumps-seq-dev \
libopenblas-dev \
libsuitesparse-dev

Then:

## Details

This crate depends on russell_lab, which, in turn, depends on an efficient BLAS library such as OpenBLAS and Intel MKL. This crate also depends on UMFPACK and MUMPS.

The root README file presents the steps to install the required dependencies.

## Setting Cargo.toml

👆 Check the crate version and update your Cargo.toml accordingly:

[dependencies]
russell_sparse = "*"

Or, considering the optional features (see more about these here):

[dependencies]
russell_sparse = { version = "*", features = ["local_libs", "intel_mkl"] }

## Examples

Note: For the functions dealing with complex numbers, the following line must be added to all derived code:

use num_complex::Complex64;

This line will bring Complex64 to the scope. For convenience the (russell_lab) macro cpx! may be used to allocate complex numbers.

### Solve a tiny sparse linear system using UMFPACK

use russell_lab::{vec_approx_eq, Vector};
use russell_sparse::prelude::*;
use russell_sparse::StrError;

fn main() -> Result<(), StrError> {
// constants
let ndim = 3; // number of rows = number of columns
let nnz = 5; // number of non-zero values

// allocate solver
let mut umfpack = SolverUMFPACK::new()?;

// allocate the coefficient matrix
let mut coo = SparseMatrix::new_coo(ndim, ndim, nnz, Sym::No)?;
coo.put(0, 0, 0.2)?;
coo.put(0, 1, 0.2)?;
coo.put(1, 0, 0.5)?;
coo.put(1, 1, -0.25)?;
coo.put(2, 2, 0.25)?;

// print matrix
let a = coo.as_dense();
let correct = "┌                   ┐\n\
│   0.2   0.2     0 │\n\
│   0.5 -0.25     0 │\n\
│     0     0  0.25 │\n\
└                   ┘";
assert_eq!(format!("{}", a), correct);

// call factorize
umfpack.factorize(&mut coo, None)?;

// allocate two right-hand side vectors
let b = Vector::from(&[1.0, 1.0, 1.0]);

// calculate the solution
let mut x = Vector::new(ndim);
umfpack.solve(&mut x, &coo, &b, false)?;
let correct = vec![3.0, 2.0, 4.0];
vec_approx_eq(&x, &correct, 1e-14);
Ok(())
}

See russell_sparse documentation for more examples.

## Tools

This crate includes a tool named solve_matrix_market to study the performance of the available sparse solvers (currently MUMPS and UMFPACK).

solve_matrix_market reads a Matrix Market file and solves the linear system:

A ⋅ x = b

where the right-hand side (b) is a vector containing only ones.

The data directory contains an example of a Matrix Market file named bfwb62.mtx, and you may download more matrices from https://sparse.tamu.edu/

For example, run the command:

cargo run --release --bin solve_matrix_market -- ~/Downloads/matrix-market/bfwb62.mtx

Or

cargo run --release --bin solve_matrix_market -- --help

to see the options.

The default solver of solve_matrix_market is UMFPACK. To run with MUMPS, use the --genie (-g) flag:

cargo run --release --bin solve_matrix_market -- -g mumps ~/Downloads/matrix-market/bfwb62.mtx

The output looks like this:

{
"main": {
"platform": "Russell",
"blas_lib": "OpenBLAS",
"solver": "MUMPS-local"
},
"matrix": {
"name": "bfwb62",
"nrow": 62,
"ncol": 62,
"nnz": 202,
"complex": false,
"symmetric": "YesLower"
},
"requests": {
"ordering": "Auto",
"scaling": "Auto",
},
"output": {
"effective_ordering": "Amf",
"effective_scaling": "RowColIter",
"umfpack_strategy": "Unknown",
"umfpack_rcond_estimate": 0.0
},
"determinant": {
"mantissa_real": 0.0,
"mantissa_imag": 0.0,
"base": 2.0,
"exponent": 0.0
},
"verify": {
"max_abs_a": 0.0001,
"max_abs_ax": 1.0000000000000004,
"max_abs_diff": 5.551115123125783e-16,
"relative_error": 5.550560067119071e-16
},
"time_human": {
"initialize": "266.59µs",
"factorize": "196.81µs",
"solve": "166.87µs",
"total_ifs": "630.27µs",
"verify": "2.234µs"
},
"time_nanoseconds": {
"initialize": 266590,
"factorize": 196810,
"solve": 166870,
"total_ifs": 630270,
"verify": 2234
},
"mumps_stats": {
"inf_norm_a": 0.0,
"inf_norm_x": 0.0,
"scaled_residual": 0.0,
"backward_error_omega1": 0.0,
"backward_error_omega2": 0.0,
"normalized_delta_x": 0.0,
"condition_number1": 0.0,
"condition_number2": 0.0
}
}

## MUMPS + OpenBLAS issue

We found that MUMPS + OpenBLAS becomes very, very slow when the number of OpenMP threads is left automatic, i.e., using the available number of threads. Thus, with OpenBLAS, it is recommended to set LinSolParams.mumps_num_threads = 1 (this is automatically set when using OpenBLAS).

This issue has also been discovered by 1, who states (page 72) "We have observed that multi-threading of OpenBLAS library in MUMPS leads to multiple thread conflicts which sometimes result in significant slow-down of the solver."

Therefore, we have to take one of the two approaches:

• If fixing the number of OpenMP threads for MUMPS, set the number of OpenMP threads for OpenBLAS to 1
• If fixing the number of OpenMP threads for OpenBLAS, set the number of OpenMP threads for MUMPS to 1

This issue has not been noticed with MUMPS + Intel MKL.

Command to reproduce the issue:

Also, to reproduce the issue, we need:

• Git hash = e020d9c8486502bd898d93a1998a0cf23c4d5057
• Remove Debian OpenBLAS, MUMPS, and etc.
• Install the compiled MUMPS solver with 02-ubuntu-openblas-compile.bash

### References

1. Dorozhinskii R (2019) Configuration of a linear solver for linearly implicit time integration and efficient data transfer in parallel thermo-hydraulic computations. Master's Thesis in Computational Science and Engineering. Department of Informatics Technical University of Munich.

## For developers

• The c_code directory contains a thin wrapper to the sparse solvers (MUMPS, UMFPACK)
• The build.rs file uses the crate cc to build the C-wrappers
• The zscripts directory also contains following:
• memcheck.bash: Checks for memory leaks on the C-code using Valgrind
• run-examples: Runs all examples in the examples directory
• run-solve-matrix-market.bash: Runs the solve-matrix-market tool from the bin directory

~9–22MB
~281K SLoC