Arpack, Eigen and Numerical Recipes

My coworker Chris and me ran some tests last week, comparing the performance of Arpack, Eigen and Numerical Recipes (C++ version, 3rd edition) for calculating the eigenvalues of sparse real symmetric matrices. While these tests are far from comprehensive and the graphs and texts not very polished, I think it’s still worth sharing our results. This article is a slightly edited version of the original write-up (pdf, nbviewer). The code is on GitHub.

Numerical Recipes and Eigen are dense eigensolvers and always compute the full eigenvalue spectrum. In contrast, Arpack uses sparse matrices and is typically used to calculate only a few eigenvalues and not the full spectrum. It seems to be common knowledge that Numerical Recipes is not very fast. We were still surprised by how slow it really is. Conversely, Arpack gives good performance even when the matrices are not that sparse or when a substantial part (say 10%) of the eigenvalues is calculated.

Chris and me both ran similar, but slightly different test programs. Chris uses the Rice university Arpack and the Arpack++ wrapper, whereas I use Arpack-NG and the Arpaca wrapper.

In [1]:
def create_data_files():
    i_d = 0
    for r_ev in [0.01,0.1,1.0]:
        for r_zeros in [0.1,0.9,0.98]:
            i_d += 1
            fn = 'performance_nr.{}.dat'.format(i_d)
            !./build/arpaca_performance_plot_nr 100 1000 \
                40 10 $r_ev $r_zeros #> $fn
In [2]:
# Takes a very long time
# create_data_files()
In [3]:
import re
import numpy as np

def read_data_file(filename):
    f = open(filename)
    s =
    n_req = None
    r_ev = None
    r_zeros = None
    d = None
    m ='n_rep=(.+)$',s,re.MULTILINE)
    if m: 
        n_rep = int(
    m ='r_ev=(.+)$',s,re.MULTILINE)
    if m: 
        r_ev = float(
    m ='r_zeros=(.+)$',s,re.MULTILINE)
    if m: 
        r_zeros = float(
    d = np.loadtxt(filename)
    return (n_rep,r_ev,r_zeros,d)

def subplot_data_file(p, filename):
    n_rep,r_ev,r_zeros,d = read_data_file(filename)
    p.set_xlabel('matrix dimension')
    p.set_ylabel('time (s)')
    p.legend(loc='upper left')
           '$r_{{ev}} = {}$\n$r_{{zeros}} = {}$'.format(r_ev,r_zeros),
In [4]:
import matplotlib.pyplot as plt
%matplotlib inline

rows = 1
cols = 2

fig = plt.figure(figsize=(cols*5,rows*5))
i_p = 0

for i in [6,9]:
    i_p += 1
    p = fig.add_subplot(rows, cols, i_p)
    subplot_data_file(p, 'performance_nr.{}.dat'.format(i))

The graph shows the time the eigensolvers take to diagonalize a random matrix of a given size. Here, \(r_{ev}\) is the “ratio” of eigenvalues. Hence \(r_{ev} = 0.1\) means calculating 10% of the eigenvalues (for Arpack only). Eigen and Numerical Recipes always calculate all eigenvalues. \(r_{zeros}\) is the “ratio” of zeros. Hence \(r_{zeros} = 0.98\) means 98% sparse. This test was run on OS X 10.9.1, the compiler was GNU G++ 4.8.2 (installed from MacPorts). Arpack-ng is 3.1.4, installed from MacPorts. Arpaca was used as the Arpack wrapper.

In [5]:
def subplot_chris(p,xlim=None,ylim=None):
    # columns are: N  NRg++4.1.2 Arpack4.1.2 Eigen4.1.2 NR4.8.2 Eigen4.8.2
    filename = 'performance_chris.dat'
    d = np.loadtxt(filename)
    p.plot(d[:,0],d[:,2],label='ARPACK G++ 4.1')
    p.plot(d[:,0],d[:,3],label='Eigen G++ 4.1')
    p.plot(d[:,0],d[:,5],label='Eigen G++ 4.8')
    p.plot(d[:,0],d[:,1],label='NR G++ 4.1')
    p.plot(d[:,0],d[:,4],label='NR G++ 4.8')
    p.set_xlabel('matrix dimension')
    p.set_ylabel('time (s)')
    p.legend(loc='upper left')
    if xlim: p.set_xlim(xlim)
    if ylim: p.set_ylim(ylim)

rows = 1
cols = 2

fig = plt.figure(figsize=(cols*5,rows*5))
i_p = 0

i_p += 1
p = fig.add_subplot(rows, cols, i_p)

i_p += 1
p = fig.add_subplot(rows, cols, i_p)

A very similar test, this time run on WestGrid’s Jasper (Linux). Uses latest Arpack from Rice university and Arpack++ as the wrapper. The matrix is the Hamiltonian from a real physical problem, with about 1% to 2% non-zeros. Numerical Recipes and Eigen are compiled with two different versions of the GNU G++ compiler, as indicated. The full spectrum of eigenvalues is computed. This scenario corresponds to the \(r_{ev} = 1.0\) and \(r_{zeros} = 0.98\) (the right-most) plot in the last graph.

While the general trends agree between this and the last graph, there are some notable differences: Arpack is much slower here than above. Reversely, Numerical Recipes is faster in this test, compared to the last.

It should be noted that Arpack was used in a black box like fashion (via the wrappers) and there might be considerable room for improving its performance, for example by compromising on precision (we use machine precision) or just using it in a cleverer way. To compute the full spectrum we actually run Arpack twice, once to get all but one eigenvalue, starting from the smallest, and once the compute the missing largest eigenvalue. (Arpack does not seem to allow to compute the whole eigenvalue spectrum directly, at least not in the simple direct manner we are using via the wrappers.)


  1. Numerical Recipes is extremely slow.
  2. New compilers are significantly faster than the old ones, especially for sophisticated C++ libraries like Eigen.
  3. To calculate only a couple eigenvalues, use Arpack; and it’s increasingly faster for increasingly sparse matrices. No surprises here.
  4. To calculate all eigenvalues, use Eigen.