SymRCM.jl

Reverse Cuthill-McKee node-renumbering algorithm.
Author PetrKryslUCSD
Popularity
7 Stars
Updated Last
2 Years Ago
Started In
August 2020

Project Status: Active – The project has reached a stable, usable state and is being actively developed. Build Status Coverage Status

SymRCM: Reverse Cuthill-McKee node-renumbering algorithm for sparse matrices.

SymRCM is a tiny package for computing the Reverse Cuthill-McKee node permutation from a sparse matrix. The goal is to minimize the "profile" of the matrix, usually aimed at reducing the cost of LDLT or Choleski factorizations.

Get SymRCM

This package is registered, and hence one can do just

] add SymRCM

Only version 1.x and the nightly builds of Julia are supported.

Testing

] test SymRCM

Usage

For a sparse matrix A the basic usage is:

p = symrcm(A)

To solve the system of linear algebraic equations x = A * b with renumbering one can do

using SparseArrays
using LinearAlgebra
n = 7;
A = sprand(n, n, 1/n)
A = A + A' + 1.0 * I
A = sparse(A)
b = rand(n)
using SymRCM
p = symrcm(A)
ip = similar(p) # inverse permutation
ip[p] = 1:length(p)
xp = A[p, p] \ b[p] # solution to the renumbered system of equations
x = xp[ip] # solution to the original system of equations
A \ b # this is the direct solution which should be identical to the above

Lower-level functions may also be useful. The adjacency graph and the node degrees may be calculated as

ag = adjgraph(A; sortbydeg = true)
nd = nodedegrees(ag)

which can be used to compute the renumbering as

numbering1 = symrcm(ag, nd) # using the lower-level functions
numbering2 = symrcm(A) # direct use of the sparse matrix

and these two will be identical.

For significantly populated matrices the sorting of the neighbor lists may be a significant expense. In that case the sorting may be turned off.

p = symrcm(A; sortbydeg = false) # note the keyword argument

Very often the resulting permutation is as good as if the lists were sorted.

Performance

Relative numbers may be of interest:

Present package code

using SymRCM                    
using SparseArrays                                     
let   
    S = sprand(10000000, 10000000, 0.0000001);    
    S = S + transpose(S);                                                       
    @time p = symrcm(S; sortbydeg = true);  
    I, J, V = findnz(S[p, p])
    @show bw = maximum(I .- J) + 1            
    @time p = symrcm(S; sortbydeg = false);   
    I, J, V = findnz(S[p, p])
    @show bw = maximum(I .- J) + 1     
    true   
end;  

produces

  9.271877 seconds (10.07 M allocations: 1.474 GiB, 6.42% gc time)          
bw = maximum(I .- J) + 1 = 1535838              
  7.189242 seconds (10.00 M allocations: 1.471 GiB, 3.04% gc time)    
bw = maximum(I .- J) + 1 = 1535722   

Matlab 2020a:

>> S = sprand(10000000, 10000000, 0.0000001);     
S = S + S';
tic; p = symrcm(S); toc  
[i,j] = find(S(p,p));
bw = max(i-j) + 1
Elapsed time is 9.258979 seconds.
bw =
     1535477

Required Packages

No packages found.