Parallel Numerics ’05, 25-34 M. Vajterˇsic, R. Trobec, P. Zinterhof, A. Uhl (Eds.)
Chapter 2: Matrix Algebra ISBN 961-6303-67-8
Parallel Maxwell Eigensolver Using
Trilinos Software Framework ∗
Peter Arbenz 1, Martin Beˇcka 2,†, Roman Geus 3,
Ulrich Hetmaniuk 4, Tiziano Mengotti 1
1Institute of Computational Science, Swiss Federal Institute of Technology,
CH-8092 Zurich, Switzerland
2Department of Informatics, Institute of Mathematics,
Slovak Academy of Sciences,
D´ubravsk´a cesta 9, 841 04 Bratislava, Slovak Republic
3Paul Scherrer Institut,
CH-5232 Villigen PSI, Switzerland
4Sandia National Laboratories,
Albuquerque, NM 87185-1110, U.S.A. ‡
We report on a parallel implementation of the Jacobi–Davidson algo-
rithm to compute a few eigenvalues and corresponding eigenvectors of a
large real symmetric generalized matrix eigenvalue problem. The eigen-
value problem stems from the design of cavities of particle accelerators.
It is obtained by the ﬁnite element discretization of the time-harmonic
Maxwell equation in weak form by a combination of N´ed´elec (edge) and
Lagrange (node) elements. We found the Jacobi–Davidson (JD) method
to be a very eﬀective solver provided that a good preconditioner is avail-
able for the correction equations. The parallel code makes extensive use
of the Trilinos software framework. In our examples from accelerator
physics we observe satisfactory speedup and eﬃciency.
†Corresponding author. E-mail: email@example.com
‡Sandia is a multiprogram laboratory operated by Sandia Corporation, a Lockheed Martin
Company, for the United States Department of Energy’s National Nuclear Security Admini-
stration under contract DE-AC04-94AL85000.
26 P. Arbenz, M. Beˇcka, R. Geus, U. Hetmaniuk, T. Mengotti
Many applications in electromagnetics require the computation of some of the
eigenpairs of the curl-curl operator,
0εre(x) = 0,div e(x) = 0,x∈Ω,(1)
Equations (1) are obtained from the Maxwell equations after separation of
the time and space variables and after elimination of the magnetic ﬁeld in-
tensity. The discretization of (1) by ﬁnite elements leads to a real symmetric
generalized matrix eigenvalue problem
where Ais positive semideﬁnite and Mis positive deﬁnite. In order to avoid
spurious modes we approximate the electric ﬁeld eby N´ed´elec (or edge) el-
ements . The Lagrange multiplier that is a function introduced to treat
properly the divergence free condition is approximated by Lagrange (or nodal)
ﬁnite elements .
In this paper we consider a parallel eigensolver for computing a few of
the smallest eigenvalues and corresponding eigenvectors of (2) as eﬃciently as
possible with regard to execution time and memory cost. In earlier studies 
we found the Jacobi–Davidson algorithm [18, 9] a very eﬀective solver for this
task. We have parallelized this solver in the framework of the Trilinos parallel
solver environment .
In section 2 we brieﬂy review the symmetric Jacobi-Davidson eigensolver
and the preconditioner that is needed for its eﬃcient application. In section 3
we discuss data distribution and issues involving the use of Trilinos.
In section 4 we report on experiments that we conducted by means of
problems originating in the design of the RF cavity of the 590 MeV ring cy-
clotron installed at the Paul Scherrer Institute (PSI) in Villigen, Switzerland.
These experiments indicate that the implemented solution procedure is almost
optimal in that the number of iteration steps until convergence only slightly
depends on the problem size.
2 The eigensolver
The Jacobi–Davidson algorithm has been introduced by Sleijpen and van der
Vorst . There are variants for all types of eigenvalue problems . Here
we use a variant (JDSYM) adapted to the generalized symmetric eigenvalue
problem (2) as described in detail in [2, 9]. This algorithm is well-suited since
Parallel Maxwell Eigensolver Using Trilinos Software Framework 27
it does not require the factorization of the matrices Aor M. In [2, 3, 4] we
found JDSYM to be the method of choice for this problem.
In addition to the standard JDSYM algorithm, we keep solutions of the cor-
rection equation orthogonal to Capplying a projector operator (I−Y H−1CT)
in each iteration step. Note that Y=M−1Cis a very sparse basis of the null
space of Aand that H=YTCis the discretization of the Laplace operator in
the nodal element space .
Our preconditioner K≈A−σM , where σis a ﬁxed shift, is a combina-
tion of a hierarchical basis preconditioner and an algebraic multigrid (AMG)
Since our ﬁnite element spaces consist of N´ed´elec and Lagrange ﬁnite ele-
ments of degree 2 and since we are using hierarchical bases, we employ the
hierarchical basis preconditioner that we used successfully in . Numbering
the linear before the quadratic degrees of freedom, the matrices A,Mand
Kget a 2-by-2 block structure. The (1,1)-blocks correspond to the bilinear
forms involving linear basis functions. The hierarchical basis preconditioners
as discussed by Bank  are stationary iteration methods that respect the
2-by-2 block structure of Aand M. We use the symmetric block Gauss–Seidel
iteration as the underlying stationary method.
For very large problems (order 105and more), we solve with K11 by a
single V-cycle of an AMG preconditioner. This makes our preconditioner a
true multilevel preconditioner. We found ML  the AMG solver of choice as
it can handle unstructured systems that originate from the Maxwell equation
discretized by linear N´ed´elec ﬁnite elements. ML implements a smoothed
aggregation AMG method  that extends the straightforward aggregation
approach of Reitzinger and Sch¨oberl . ML is part of Trilinos which is
discussed in the next section.
K22 of K22 again represents a stationary iteration
method of which we execute a single iteration step.
3 Parallelization issues
For very large problems, the data must be distributed over a series of proces-
sors. To make the solution of these large problems feasible, an eﬃcient parallel
implementation of the algorithm is necessary. Such a parallelization of the al-
gorithm requires proper data structures and data layout, some parallel direct
and iterative solvers, and some parallel preconditioners. For our project, we
found the Trilinos Project  to be an eﬃcient environment to develop such
a complex parallel application.
28 P. Arbenz, M. Beˇcka, R. Geus, U. Hetmaniuk, T. Mengotti
The Trilinos Project is an ongoing eﬀort to design, develop, and integrate
parallel algorithms and libraries within an object-oriented software framework
for the solution of large-scale, complex multi-physics engineering and scientiﬁc
applications [19, 10, 15]. Trilinos is a collection of compatible software pack-
ages. Their capabilities include parallel linear algebra computations, parallel
algebraic preconditioners, the solution of linear and non-linear equations, the
solution of eigenvalue problems, and related capabilities. Trilinos is primarily
written in C++ and provides interfaces to essential Fortran and C libraries.
For our project, we use the following packages
•Epetra, the fundamental package for basic parallel algebraic operations.
It provides a common infrastructure to the higher level packages,
•Amesos, the Trilinos wrapper for linear direct solvers (SuperLU, UMF-
PACK, KLU, etc.),
•AztecOO, an object-oriented descendant of the Aztec library of parallel
iterative solvers and preconditioners,
•ML, the multilevel preconditioner package, that implements a smoothed
aggregation AMG preconditioner capable of handling Maxwell equa-
tions [7, 16].
For a detailed overview of Trilinos and its packages, we refer the reader to .
3.2 Data structures
Real valued double precision distributed vectors, multivectors (collections of
one or more vectors) and (sparse) matrices are fundamental data structures,
which are implemented in Epetra. The distribution of the data is done by
specifying a communicator and a map, both Epetra objects.
The notion of a communicator is known from MPI . A communicator
deﬁnes a context of communication, a group of processes and their topology,
and it provides the scope for all communication operations. Epetra imple-
ments communicators for serial and MPI use. Moreover, communicator classes
provide methods similar to other MPI functions.
Vectors, multivectors and matrices are distributed row wise. The distribu-
tion is deﬁned by means of a map. A map can be deﬁned as the distribution
of a set of integers across the processes, it relates the global and local row in-
dices. To create a map object, a communicator, the global and local numbers
of elements (rows), and the global numbering of all local elements have to be
Parallel Maxwell Eigensolver Using Trilinos Software Framework 29
provided. So, a map completely describes the distribution of vector elements
or matrix rows. Note that rows can be stored on several processors redun-
dantly. To create a distributed vector object, in addition to a map, one must
assign values to the vector elements. The Epetra vector class oﬀers standard
functions for doing this and other common vector manipulations.
Trilinos supports dense and sparse matrices. Sparse matrices are stored
locally in the compressed row storage (CRS) format . Construction of a
matrix is row by row or element by element. Afterwards, a transformation
of the matrix is required in order to perform matrix-(multi)vector product
Y=A×Xeﬃciently, specifying maps of the vectors Xand Y.
Some algorithms require only the application of a linear operator, such that
the underlying matrix need not be available as an object. Epetra handles this
by means of a virtual operator class. Epetra also admits to work with block
sparse matrices. Unfortunately, there is no particular support for symmetric
To redistribute data, one deﬁnes a new, so-called target map and creates
an empty data object according to this new map as well as an Epetra’s im-
port/export object from the original and the new map. The new data object
can be ﬁlled with the values of the original data object using the import/export
object, which describes the communication plan.
3.3 Data distribution
A suitable data distribution can reduce communication costs and balance the
computational load. The gain from such a redistribution can, in general,
overcome the cost of this preprocessing step.
Zoltan [21, 8] is a library that contains tools for load balancing and parallel
data management. It provides a common interface to graph partitioners like
METIS and ParMetis [12, 11]. Zoltan is not a Trilinos package. But the
Trilinos package EpetraExt provides an interface between Epetra and Zoltan.
In our experiments, we use ParMetis to distribute the data. This parti-
tioner tries to distribute a graph such that (I) the number of graph vertices
per processor is balanced and (II) the number of edge cuts is minimized. The
former balances the work load. The latter minimizes the communication over-
head by concentrating elements in diagonal blocks and minimizing the number
of non-zero oﬀ-diagonal blocks. In our experiments, we deﬁne a graph G, which
contains connectivity informations for each node, edge, and face of the ﬁnite
element mesh. Gis constructed from portions of the sparse matrices M,H,
and C. To reduce overhead of the redistribution we also work with a smaller
than our artiﬁcial graph G. We determine a good parallel distribution just for
the vertices and then adjust the edges and faces accordingly.
30 P. Arbenz, M. Beˇcka, R. Geus, U. Hetmaniuk, T. Mengotti
4 Numerical experiments
In this section, we discuss the numerical experiments used to assess the parallel
implementation. Results have been presented in .
The experiments have been executed on a 32 dual-node PC cluster in
dedicated mode. Each node has 2 AMD Athlon 1.4 GHz processors, 2 GB
main memory, and 160 GB local disk. The nodes are connected by a Myrinet
providing a communication bandwidth of 2000 Mbit/s. The system operates
For these experiments, we use the developer version of Trilinos on top of
MPICH. We computed the 5 smallest positive eigenvalues and corresponding
eigenvectors using JDSYM with the multilevel preconditioner.
Test problems originate in the design of the RF cavity of the 590 MeV ring
cyclotron installed at the Paul Scherrer Institute (PSI) in Villigen, Switzer-
land. We deal with two problem sizes. They are labelled cop40k and cop300k.
Their characteristics are given in Table 1, where we list the order nand the
Table 1: Matrix characteristics
grid nA−σM nnzA−σM nHnnzH
cop40k 231,668 4,811,786 46,288 1,163,834
cop300k 1,822,854 39,298,588 373,990 10,098,456
number of non-zeros nnz for the shifted operator A−σM and for the discrete
Laplacian H. Here the eigenvalues to be computed are
λ1≈1.13, λ2≈4.05, λ3≈9.89, λ4≈11.3, λ5≈14.2.
We set σ= 1.5.
In Table 2, we report the execution times t=t(p) for solving the eigenvalue
problem with various numbers pof processors. These times do not include
preparatory work, such as the assembly of matrices or the data redistribution.
E(p) describes the parallel eﬃciency with respect to the simulation run with
the smallest number of processors. tprec and tpro j indicate the percentage
of the time the solver spent applying the preconditioner and the projector,
inner is the average number of inner (QMRS) iterations per
outer iteration. The total number of applications for the preconditioner K
is approximately nouter ·navg
inner. Here we use an AMG preconditioner for the
block K11, Jacobi steps for K22 and an AMG preconditioner for the whole H.
In Table 3, we use the AMG preconditioner for the block K11, Jacobi
steps for K22, and a similar strategy for H(AMG preconditioner for H11 and
Jacobi steps for H22). We investigate the eﬀect of redistributing the matrices.
Parallel Maxwell Eigensolver Using Trilinos Software Framework 31
Table 2: Results for matrix cop40k
p t [sec] E(p)tprec [%] tproj [%] nouter navg
1 2092 1.00 37 18 53 19.02
2 1219 0.86 38 17 54 18.96
4 642 0.81 37 17 54 19.43
8 321 0.81 38 18 53 19.23
12 227 0.77 40 19 53 19.47
16 174 0.75 43 20 53 18.96
Results in Table 3 show that the quality of data distribution is important.
For the largest number of processors (p= 16), the execution time with the
redistributed matrices is half the time obtained with the original matrices.
These were straightforward block distributions of the matrices.
Table 3: cop40k: Comparison of results with (left) and without (right) redis-
p t [sec] E(p)nouter navg
1 1957 2005 1.00 1.00 53 53 19.02 19.02
2 1159 1297 0.84 0.77 54 53 19.06 19.66
4 622 845 0.79 0.59 54 55 19.43 19.18
8 318 549 0.77 0.45 53 54 19.23 19.67
12 231 451 0.71 0.37 53 54 20.47 19.78
16 184 366 0.66 0.34 53 54 19.00 19.04
Finally, in Table 4, we report results for our larger problem size cop300k.
We use the 2-level preconditioner for Kand H: an appropriate AMG pre-
conditioner for the blocks K11 and H11 and one step of Jacobi for the blocks
K22 and H22. Table 4 shows that, for these experiments, the iteration counts
behave nicely and that eﬃciencies stay high.
Table 4: Results for matrix cop300k
p t [sec] E(p)nouter navg
8 4346 1.00 62 28.42
12 3160 0.91 62 28.23
16 2370 0.92 61 28.52
32 P. Arbenz, M. Beˇcka, R. Geus, U. Hetmaniuk, T. Mengotti
In conclusion, the parallel algorithm shows a very satisfactory behavior. The
eﬃciency of the parallelized code does not get below 65 percent for 16 proces-
sors. We usually have a big eﬃciency loss initially. Then eﬃciency decreases
slowly as the number of processors increases. This is natural due to the grow-
ing communication-to-computation ratio.
The accuracy of the results are satisfactory. The computed eigenvectors
were M-orthogonal and orthogonal to Cto machine precision.
We plan to compare the Jacobi-Davidson approach with another eigen-
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