Group Title: Department of Computer and Information Science and Engineering Technical Reports
Title: Hypergraph-based unsymmetric nested dissection ordering for sparse LU factorization
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Title: Hypergraph-based unsymmetric nested dissection ordering for sparse LU factorization
Alternate Title: Department of Computer and Information Science and Engineering Technical Report
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Language: English
Creator: Davis, Timothy A.
Grigori, Laura
Boman, Erik
Donfack, Simplice
Affiliation: University of Florida
Institut national de recherche en informatique et en automatique -- Sarclay
Sandia National Laboratories
Universite de Yaounda I -- Yaounde
Publisher: Department of Computer and Information Science and Engineering, University of Florida
Place of Publication: Gainesville, Fla.
Copyright Date: 2008
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Abstract. In this paper we present HUND, a hypergraph-based unsymmetric nested dissection
ordering algorithm for reducing the fill-in incurred during Gaussian elimination. HUND has several
important properties. It takes a global perspective of the entire matrix, as opposed to local heuristics.
It takes into account the assymetry of the input matrix by using a hypergraph to represent its
structure. It is suitable for performing Gaussian elimination in parallel, with partial pivoting. This
is possible because the row permutations performed due to partial pivoting do not destroy the column
separators identified by the nested dissection approach. Experimental results on 27 medium and large
size highly unsymmetric matrices compare HUND to four other well-known reordering algorithms.
The results show that HUND provides a robust reordering algorithm, in the sense that it is the best
or close to the best (often within 10%) of all the other methods.

Key words. sparse LU-factorization, reordering techniques, hypergraph partitioning, nested

AMS subject classifications. 65F50, 65F05

1. Introduction. Solving a linear system of equations Ax = b is an operation
that lies at the heart of many scientific applications. We focus on sparse, general
systems in this paper. Gaussian elimination can be used to accurately solve these
systems, and consists in decomposing the matrix A into the product of L and U,
where L is a lower triangular matrix and U is an upper triangular matrix. One of the
characteristics of Gaussian elimination is the notion of a fill element, which denotes
a zero element of the original matrix that becomes nonzero in the factors L and U
due to the operations associated with the Gaussian elimination. Hence one of the
important preprocessing steps preceding the numerical computation of the factors L
and U consists in reordering the equations and variables such that the number of fill
elements is reduced.
Although this problem is NP-complete [32], in practice there are several efficient
fill reducing heuristics. They can be grouped into two classes. The first class uses
local greedy heuristics to reduce the number of fill elements at each step of Gaussian
elimination. One of the representative heuristics is the minimum degree algorithm.
This algorithm uses the graph associated with a symmetric matrix, and chooses at each
step to eliminate the row corresponding to the vertex of minimum degree. Several
variants, such as multiple minimum degree [27] (MMD) and approximate minimum
degree [1] (AMD), improve the minimum degree algorithm, in terms of time and/or
memory usage.

*INRIA Saclay Ile de France, Laboratoire de Recherche en Informatique Universite Paris-Sud
11, France (
tScalable Algorithms Dept., Sandia National Laboratories, NM 87185-1318, USA, Sandia is a
multiprogram laboratory operated by Sandia Corporation, a Lockheed Martin Company, for the
United States Department of Energy's National Nuclear Security Administration under Contract
DE-AC04-94AL85000. This work was supported by the US DOE Office of Science through the
CSCAPES SciDAC institute (
tUniversite de Yaounde I, Computer Science Department, B.P 812 Yaounde Cameroun, the
work of this author was performed during his Master at INRIA Saclay through the INRIA Internship
program (
CISE Dept., University of Florida, supported by the National Science Foundation (0620286)

The second class is based on global heuristics and uses graph partitioning to
restrict the fill to only specific blocks of the permuted matrix. Nested dissection [15]
is the main technique used in the graph partitioning approach. This algorithm uses the
graph of a symmetric matrix and employs a top-down divide-and-conquer paradigm.
The graph partitioning approach has the advantage of reordering the matrix into a
form suitable for parallel execution. State-of-the-art nested dissection algorithms use
multilevel partitioning [20, 24]. A widely used routine is METIS_NODEND from the
METIS [23] graph partitioning package.
It has been observed in practice that minimum degree is better at reducing the fill
for smaller problems, while nested dissection works better for larger problems. This
observation has lead to the development of hybrid heuristics that consist in applying
several steps of nested dissection, followed by the usage of a variant of the minimum
degree algorithm on local blocks [21].
For unsymmetric matrices, the above algorithms use the graph associated with
the symmetrized matrix A + AT or ATA. One additional algorithm, the column
approximate minimum degree [9] (COLAMD), implements the approximate minimum
degree algorithm on ATA without explicitly forming the structure of ATA. The
approach of symmetrizing the input matrix works well in practice when the matrix is
almost symmetric. However, when the matrix is very unsymmetric, the information
related to the asymmetry of the matrix is not exploited.
There are few approaches in the literature that aim at developing fill-reducing
algorithms targeting unsymmetric matrices. For local heuristics, this is due to the
fact that the techniques for improving the runtime of minimum degree are difficult to
extend to unsymmetric matrices. In fact the minimum degree algorithm is related to
the Markowitz algorithm [29], which was developed earlier for unsymmetric matrices.
The Markowitz algorithm defines the degree of a vertex (called the Markowitz count)
as the product of the number of nonzeros in the row and the number of nonzeros in the
column corresponding to this vertex. However, this algorithm is asymptotically slower
than the minimum degree algorithm. A recent local greedy heuristic for unsymmetric
matrices is presented in [2]. To obtain reasonable runtime, the authors use local
symmetrization techniques and the degree of a vertex is given by an approximate
Markowitz count.
In this paper we present one of the first fill-reducing ordering algorithms that fully
exploits the asymmetry of the matrix and that is also suitable for parallel execution.
It relies on a variation of nested dissection, but it takes into account the asymmetry
of the input matrix by using a hypergraph to represent its structure. The algorithm
first computes a hyperedge separator of the entire hypergraph that divides it into two
disconnected parts. The matrix is reordered such that the columns corresponding to
the hyperedge separator are ordered after those in the disconnected parts. The nested
dissection is then recursively applied to the hypergraph of the two disconnected parts,
respectively. The recursion can be stopped at any depth.
An important property of our approach is that the structure of the partitioned
matrix is insensitive to row permutations. In other words, the row permutations in-
duced by pivoting during Gaussian elimination do not destroy the column separators.
Hence the fill is reduced because it can occur only in the column separators and in the
disconnected parts of the matrix. But also this property is particularly important for
a parallel execution, since the communication pattern, which depends on the column
separators, can be computed prior to the numerical factorization. In addition, the
partitioning algorithm can be used in combination with other important techniques

in sparse Gaussian elimination. This includes permuting large entries on the diago-
nal [12], a technique improving stability in solvers implementing Gaussian elimination
with static pivoting [25].
We note that a partitioning algorithm that takes into account the asymmetry of
the input matrix was also considered by Duff and Scott in [13, 14]. There are several
important differences with our work. The authors focus on the parallel execution
of LU factorization, and their goal is to permute the matrix to a so called singly-
bordered block diagonal form. In this form the matrix has several diagonal blocks
(which can be rectangular), and the connections between the different blocks are
assembled in the columns ordered at the end of the matrix. The advantage of this
form is that the diagonal blocks can be factorized independently, though special care
must be taken since the blocks are often non-square. The authors do not analyze this
approach for fill-reducing ordering. The core part of our method can be viewed as
a recursive application of the Duff and Scott strategy, but for ordering, where non-
square blocks are less problematic. We also gain similar advantages with respect to
parallel execution.
The remainder of the paper is organized as follows. In Section 2 we give several
basic graph theory definitions and we describe in detail the nested dissection pro-
cess. In Section 3 we present our hypergraph based unsymmetric nested dissection
algorithm and its different steps. In Section 4 we describe a possible variation of the
algorithm that aims at decreasing the size of the separators. In Section 5 we present
experimental results that study the effectiveness of the new algorithm, in terms of
fill, on a set of highly unsymmetric matrices. We also compare the results with other
fill-reducing ordering algorithms. Finally, Section 6 concludes the paper.
2. Background: Nested Dissection and Hypergraphs.
2.1. Nested Dissection. Nested dissection [15, 26] is a fill-reducing ordering
method based on the divide-and-conquer principle. The standard method only applies
to symmetric matrices; here we show a nonsymmetric variation.
The sparsity structure of a structurally symmetric matrix is often represented as
an undirected graph. The nested dissection method is based on finding a small vertex
separator, S, that partitions the graph into two disconnected subgraphs. If we order
the rows and columns corresponding to the separator vertices S last, the permuted
matrix PAPT has the form

AllAn 0 A13
0 A22 A23
AT3 AT A33/

where the diagonal blocks are square and symmetric. Now the diagonal blocks All and
A22 can be factored independently and will not cause any fill in the zero blocks. We
propose a similar approach in the nonsymmetric case, based on a column separator.
Suppose we can permute A into the form

All 0 A13
0 A22 A23
0 0 A33/

where none of the blocks are necessarily square. (This is known as singly bordered
block form.) Then we can perform Gaussian elimination and there will be no fill in
the zero blocks. Furthermore, this property holds even if we allow partial pivoting

and row interchanges. Note that if any of the diagonal blocks are square, then A is
reducible and the linear systems decouple.
The question is how to obtain singly-bordered block structure with a small column
separator. There are two common approaches: a direct approach [3, 33], and indirect
methods that first find doubly-bordered block diagonal form [14]. We choose the
direct method, and use hypergraph partitioning.
2.2. Hypergraph Partitioning and Ordering. Since graph models are lim-
ited to (structurally) symmetric matrices, either a bipartite graph or hypergraph must
be used in the unsymmetric case. We prefer the hypergraph model. A hypergraph
H(V, E) contains a set of vertices V and a set of hyperedges E (also known as nets),
where each hyperedge is a subset of V. We will use the column-net hypergraph model
of a sparse matrix [4] where each row corresponds to a vertex and each column is a
hyperedge. Hyperedge ej contains the vertices given by the nonzero entries in column
j. An example of a matrix A is given in Equation 2.1 and its hypergraph is shown in
Figure 2.1.

x x x
x x x
A xx x (2.1)
x x

X x x x
x x
x x x x

-< ^ ---(24y

n7 e
< 8 ) .- Y7 )4

FIG. 2.1. Hypergraph of matrix A in Equation 2.1. The large circles are vertices, and the small
black dots represent hyperedges (nets).

Suppose we partition the vertices (rows) into two sets, Ri and R2. This induces a
partition of the hyperedges (columns) into three sets: C1, C2, and C3. Let C1 be the set
of hyperedges (columns) where all the vertices (rows) are in Ri. 1..... in ,., let C2 be the
set of hyperedges (columns) where all the vertices (rows) are in R2. Let C3 be the set of
hyperedges (columns) that are "cut", that is, they have some vertices in Ri and some in R2.
Now let P be a permutation such that all of Ri is ordered before R2, and let Q be a similar
column permutation. Then

PAQ = A 0 A13 (2.2)
0 0 A22 A23)
It may happen that some rows in All or A22 are empty (all zero). In this case, permute
such rows to the bottom and we get
(Al 0 A13
PAQ = 0 A22 A23. (2.3)
S0 0 A33

Figure 2.2 displays the result of the first step of unsymmetric nested dissection applied on
the hypergraph in Figure 2.1. The matrix obtained after permuting matrix A in Equation 2.1
is presented in Equation 2.4.

4 5
\ 5--K .",

n3 .. n n A,,, L

V1 Vs
Vs V2

FIG. 2.2. The result of the first step of unsymmetric nested dissection applied on the hypergraph
in Figure 2.1, the hyperedge separator is Es {-ns,ns}. Note that the separator only contains
hyperedges and no vertices.

x x
x x x
x x x
/ x x \

PAQ= (2.4)
x x x
x x x

x \ x x /
x x

Hypergraph partitioning has been well studied [4]. The objective is to partition the
vertices into two parts such that there are approximately equally many vertices in each part,
and the number of cut hyperedges is minimized. Although it is an NP-hard problem, fast
multilevel algorithms give good solutions in practice. Good software is i. ,.1. available,
like PaToH, hMetis, and Zoltan. The k-way partitioning problem (k > 2) is usually solved
by recursive bisection. We note that the MONET algorithm [33] is precisely hypergraph
partitioning applied to sparse matrices.
One disadvantage of hypergraph partitioning is that it can take relatively long time.
Fortunately, methods based on graph partitioning and vertex separators are faster and can
also produce the desired singly bordered block form [14] but we do not explore this option

3. An Unsymmetric Nested Dissection Algorithm. We present an algorithm
with three stages. First, we apply hypergraph-based nested dissection to limit the fill. Sec-
ond, we perform row interchanges based on numerical values to reduce pivoting. Third, we
apply a local reordering on local blocks to again reduce fill.

3.1. Hypergraph Recursive Ordering. Recall our goal is to find permutations
P1 and Qi such that (PiAQi)(Qf) = Plb is easier to solve than the original system Ax = b.
Our idea is to apply the block decomposition (2.2) recursively. This is quite different from
most recursive approaches for solving linear systems, because our blocks are usually not
square. Our approach works because we only produce orderings recursively, and do not
factor or solve recursively (using the matrix blocks).
Figure 3.1 (left) shows the sparsity structure of PIAQ1 after two levels of bisection. We
continue the recursive bisection until each block is smaller than a chosen threshold. As in

symmetric nested dissection, we expect it is beneficial to switch to a local ordering algorithm
on small blocks but in principle one could continue the recursion until each block has only one
row or column. We sketch the recursive ordering heuristic in Algorithm 1. In this variant,
the recursion stops at a constant block size, tmin.

1 2

4 5 6

1 2 3 4 5 6

FIG. 3.1. The matrix after hypergraph ordering ( i 1 and after row permutation from matching

Algorithm 1 Hypergraph Unsymmetric Nested Dissection
1: Function [p, q] HUND(A)
2: [m, n] size(A)
3: if min(m, n) < tmi then
4: p= l:m
5: q l:n
6: else
7: Create column-net hypergraph H for A
8: Partition H into two parts using hypergraph partitioning
9: Let p and q be the row and column permutations, respectively, to permute A
into the block structure in Eq. 2.2
10: [mi,ni] = size(Al1)
11: [p1, qi] HUND(A1n)
12: [p, q2] = HUND(A22)
13: p p(pi,p2 + mi)
14: q q(ql, q2 + n)
15: end if

3.2. Stabilization. The ordering procedure above only takes the structure into ac-
count, and not the numerical values. To stabilize the factorization and minimize pivoting,
we wish to permute large entries to the diagonal. A standard approach is to model this
as matching in the bipartite graph [12], and we can use the HSL [22] routine MC64. We
use the matching permutation to permute the rows as shown in Figure 3.1 (right). Observe
that after row permutation, the diagonal blocks are now square. The remaining rows in the
originally rectangular blocks have been "pushed down". All the permutations applied on the
matrix after this step should be symmetric.
This permutation step for obtaining a strong diagonal is helpful for dynamic (partial)
pivoting methods, since the number of row swaps is -i_ 1i1i .I i... reduced, thereby speeding
up the factorization process [12]. It is essential for static pivoting methods [25], because it
decreases the probability of encountering small pivots during the factorization.

3.3. Local Reordering. The goal of the third preprocessing step is to use local
strategies to further decrease the fill in the blocks of the permuted matrix. Algorithms as
CAMD [6] (constrained AMD) or CCOLAMD [6] (constrained COLAMD) can be used for
this step. These algorithms are based on COLAMD, respectively AMD, and have the prop-
erty of preserving the partitioning obtained by the unsymmetric nested dissection algorithm.
This is because in a constrained ordering method, each node belongs to one of up to n con-
straint sets. In our case, a constraint set corresponds to a separator or a partition. After
the ordering it is ensured that all the nodes in set zero are ordered first, followed by all the
nodes in set one, and so on.
A preprocessing step useful for the efficiency of direct methods consists of reordering the
matrix according to a postorder traversal of its elimination tree. This reordering tends to
group together columns with the same non zero structure, so they can be treated as a dense
matrix during the numeric factorization. This allows for the use of dense matrix kernels
during numerical factorization, improves the memory hierarchy usage, and hence leads to a
more efficient numeric factorization.
In order to preserve the structure obtained in the previous steps, we compute the elimi-
nation tree corresponding to the diagonal blocks of the input matrix. Note that in practice,
postordering a matrix preserves its structure but can change the fill in the factors L and U.
We remark that the local reordering should be applied symmetrically, so that the diagonal
is preserved.
3.4. Algorithm Summary. In summary, LU factorization with partial pivoting
based on unsymmetric nested dissection contains several distinct steps in the solution process:
1. Reorder the equations and variables by using the HUND heuristic that chooses
permutation matrices P1 and Q1 so that the number of fill-in elements in the factors
L and U of P1AQ1 is reduced.
2. Choose a permutation matrix P2 so that the matrix P2P1AQ1 has large entries on
the diagonal. The above permutation helps ensure the accuracy of the computed
solution. In our tests this is achieved using the HSL routine MC64 [12].
3. Find a permutation matrix P3 using a local heuristic and a postorder of the elimina-
tion tree associated with the diagonal blocks such that the fill-in is further reduced
in the matrix P3P2P1AQIP3f. In our tests this is achieved using constrained CO-
LAMD and postordering based on the row merge tree [17] of the diagonal blocks.
4. Compute the numerical values of L and U.
The execution of the above algorithm on a real matrix (FD18) is displayed in Figure 3.2.
The structure of the original matrix is presented at top left. The structure obtained after
the unsymmetric nested dissection HUND is presented at top right. The structure obtained
after permuting to place large entries on the diagonal using MC64 is displayed at bottom
left. And finally the structure obtained after the local ordering is displayed at bottom right.
4. Variations. We have presented an ordering method that uses both row and column
permutations to reorder a matrix to reduce fill. We used a column separator approach based
on the column-net hypergraph model, where rows are vertices. Another option is to use the
row-net hypergraph model, where columns are vertices. The method will work as before,
except now we find row separators instead of column separators. One step of hypergraph
bisection gives the matrix block structure

All 0 0o\
PAQ = A22 0 (4.1)
\A31 A32 A33

The row separator approach is advantageous when the row separator is smaller than
the column separator. However, row permutations can now destroy the sparsity structure.
This variation is thus not suitable for partial pivoting with row interchanges (though partial





(a) fdl 8

5000 10000 1500(
nz = 63406
(c) After HUND and MC64




0 5000 10000 15000
nz =63406

(b) After HUND



0 5000 10000 15000
nz =63406
(d) After HUND, MC64 and CCOLAMD

5000 "-


0 5000 10000 15000
nz =63406

FIG. 3.2. Example of application of preprocessing steps on a real matrix FD18. Displayed are
(a) the structure of the original matrix FD18, (b) the structure obtained after HUND, (c) after
MC64, and (d) after CCOLAMD

pivoting with column interchanges would be fine). For static pivoting, we can again use a
permutation based on matching (MC64), but now we should permute columns not rows.

4.1. Row-or-column (Mondriaan) Approach. Since the best variation (row or
column) depends on the matrix structure, an intriguing idea is to combine these two methods.
The idea is to try both partitioning methods for every bisection, and pick the best. This
gives a recursive decomposition that uses a combination of row and column separators. This
is illustrated in Figure 4.1. We call this row-or-column hybrid method Mondriaan ordering,
since it is similar to the Mondriaan method for sparse matrix partitioning [31], which also
uses recursive hypergraph bisection with varying directions.
Obtaining a strong diagonal is a bit more difficult with the Mondriaan method. As
usual, we compute a matching in the bipartite graph, but it is not obvious how to apply
this as a permutation. A pure row or column permutation of the entire matrix will ruin the
sparsity structure. Instead, parts of the matrix should be permuted by columns and other
parts by rows. We omit the details here since it is difficult to describe.
We have not implemented the Mondriaan hybrid ordering, and leave it as future work
to evaluate the potential improvement over the column-based method.

5. Experimental Results. In this section we present experimental results for HUND
algorithm applied to real world matrices. The goal of the experiments is two-fold. First,
we want to compare the performance of the new ordering algorithm with other widely used
ordering algorithms as MMD, AMD, COLAMD and METIS (nested dissection). Second, we
want to study the quality of the partitioning, in terms of size of the separators. As stated
in the introduction, our goal is to reorder the matrix into a form that is suitable for parallel
computation, while reducing or at least maintaining comparable the number of fill-in elements
in the factors L and U to other state-of-art reordering algorithms. We show here that this

FIG. 4.1. Example of Mondriaan variation. The top level separator is a column separator
(blue), while one of the subproblems has a row separator (red) while the other has a column separator

goal is indeed achieved.
We use a set of highly unsymmetric matrices that represent a variety of application
domains. We present in Table 5.1 their characteristics which include the matrix order,
the number of nonzeros in the input matrix A, the numerical symmetry and the application
domain. The matrices are grouped depending on their number of nonzeros, with no particular
order within a group. For example, the first eleven matrices have less than 2 105 nonzeros,
the following three matrices have less than 3 105 nonzeros, and so on. The matrices are
available from University of Florida Sparse Matrix collection [7].

5.1. HUND versus other Reordering Algorithms: Results with SuperLU.
We compare the ordering produced by HUND with four widely used fill-reducing ordering
algorithms, that is MMD (applied on the structure of A + AT or on the structure of AAT),
AMD, COLAMD, and METIS nested dissection (applied on the structure of A + AT or on the
structure of AAT). The ,i i;li. of each algorithm can be evaluated using several criteria, as
the number of nonzero entries (nnz) in the factors L and U, the number of floating point
operations performed during the numerical factorization, and the factorization time. We
restrict our attention to the first criterion, the number of nonzeros in the factors L and U,
since floating point operations are very fast on current computers while memory is often the
bottleneck. (We also computed the number of operations and the results were quite similar
to the nonzero counts.)
In our first set of tests we use LU factorization with partial pivoting implemented in the
SuperLU solver [10]. SuperLU uses partial pivoting with threshold and chooses in priority
the diagonal element. In our experiments we use a threshold of 1, that is at each step of
factorization the element of maximum magnitude in the current column of L is used as pivot.
To evaluate HUND, the different preprocessing steps presented in section 3.4 are performed
before the LU factorization with partial pivoting. That is, first the matrix is reordered using
HUND heuristic. Second, the MC64 routine [12] is called to move large entries onto the
diagonal. Third, the matrix is reordered using constrained COLAMD algorithm, as presented
in [6], and based on a postorder traversal of the row merge tree [17] of the diagonal blocks.
After these three preprocessing steps, the LU factorization with partial pivoting of SuperLU
is called.
For the other reordering algorithms we use only two preprocessing steps. The matrix is
scaled and permuted using MC64 in order to place large entries on the diagonal, and then a
fill-reducing ordering is applied.
The HUND reordering heuristic presented in Algorithm 1 starts with the hypergraph of

# Matrix Order n nnz(A) Sym. Application Domain
1 LNS_3937 3937 25407 0.14% Linearized n.-s. compressible
2 FD18 16428 63406 11 i Crack problem
3 POLILARGE 15575 33074 1iv. Chemical process simulation
4 BAYER04 20545 159082 11 ''. Chemical process simulation
5 SWANG1 3169 20841 i i'. Semiconductor device sim
6 MARK3JAC020 9129 56175 1.00% Economic model
7 LHR04 4101 82682 11 r. Light hydrocarbon recovery
8 RAEFSKY6 3402 137845 11 '. Incompressible flow
9 ZHAO2 33861 166453 I I'. Electromagnetism
10 MULTDCOP_03 25187 193216 1.00% Circuit simulation
11 SHERMANACB 18510 145149 :111 Circuit simulation
12 JAN99JAC120SC 41374 260202 ii i. Economic model
13 BAYERO1 57735 277774 i i. Chemical process simulation
14 SINC12 7500 294986 i i'. Single material crack problem
15 MARK3JAC140SC 64089 399735 1.00% Economic model
16 ONETONE1 36057 341088 4.00% Circuit simulation
17 AF23560 23560 484256 11 r Airfoil eigenvalue calculation
18 SINC15 11532 568526 1 ,'. Single material crack problem
19 E40R0100 17281 553562 i Fluid dynamics
20 ZD_JAC2_DB 22835 676439 1 ,'. Chemical process simulation
21 LHR34C 35152 764014 i, '. Light hydrocarbon recovery
22 SINC18 16428 973826 r i Single material crack problem
23 TWOTONE 120750 1224224 11.00% circuit simulation
24 LHR71C 70304 1528092 1 i Light hydrocarbon recovery
25 TORSO2 115967 1033473 I I Bioengineering
26 AV41092 41092 1683902 11 Unstructured finite element
27 BBMAT 38744 1771722 i ,.. Computational fluid dynamics
Benchmark matrices.

the input matrix and partitions it recursively into two parts. The recursion can be stopped
either when a predefined number of parts is reached, or when the size of a part is smaller
than a predefined threshold. In our tests we use PaToH [5] (with a fixed seed of 42 for
the random number generator) to partition a hypergraph in two parts at each iteration of
Algorithm 1. To study the performance of HUND we vary the number of parts in which the
matrix is partitioned. When HUND partitions into a fixed number of parts, we present the
results obtained for the number of parts (denoted as parts) equal to 16 and 128. When
HUND partitions until the size of each part is smaller than a given threshold, we present
results for values of threshold (denoted as tmin) equal to 1 and 100.
Figure 5.1 compares HUND to the best result obtained for each matrix by one of the
other reordering algorithms tested. It displays the ratio of the smallest number of nonzeros
in the factors L and U obtained by one of the other reordering algorithms relative to the
number of nonzeros in the factors L and U obtained by four versions of HUND partss 16,
parts 128, tmin = 1, tmin = 100). When this ratio is bigger than 1, HUND is the
best reordering strategy. Figure 5.1 also shows the best reordering algorithm among MMD,
COLAMD, AMD and METIS in term of fill-in. For completeness, we report in Table 6.1 of the
Appendix, the results obtained for all the ordering strategies tested. The represented values
are nnz(L + U I)/nnz(A). The cases represented in the table by "-" mean that SuperLU
failed due to too much fill-in generated, and hence a memory requirement that exceeded the
limits of our computer.

We observe that for half of the matrices in our test set, one variant of HUND induced the
least fill-in compared to the other state-of-art reordering algorithms. For 3 other matrices,
each of AMD, MMD and COLAMD produced the best results, while Metis produced the best
result for 2 matrices. For 15 matrices, COLAMD produces results comparable to the best

I IHUND(kparts=128)
3 I HUND(tmin=1)
o HUND(tmin=100)
SO mmd(AT+A)
2.5 mmd(ATA)
S"* Metis(AT+A)
H Metis(ATA)
+ 1.5


0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27
Matrix number

FIG. 5.1. Ratio of number of nonzeros in the factors L and U produced by best state-of-art
algorithm relative to four variants of HUND algorithm

For most of the matrices, the four variants of HUND produce comparable results.
As displayed in Table 6.1, the fill-in has a large value between 30 and 50 for the matrices
MARK3JAC020, ZHAO2, SINC12, MARK3JAC140sc, SINC15, and SINC18 (numbers 6, 9, 14, 15, 18
and 22). However for these matrices HUND produced the best, or very close to the best,
results. The other reordering strategies lead generally to a larger number of fill-in elements.
COLAMD leads to a fill-in factor between 42 and 116, and METIS (ATA) leads to a fill-in factor
between 32 and 66. The algorithms (MMD(A+AT) and AMD) fail for half of these matrices.
Note that matrices SINC12, SINC15, and SINC18 come from the same application domain
(single-material crack problem), as well as matrices MARK3JACO20 and MARK3JAC140SC
(economic model).
In Figure 5.2 we restrict our attention to two variants of HUND partss 128 and
tmin 1) and compare them with one of the best algorithms that uses a local strategy
(COLAMD) and one of the best algorithms that uses a global approach (METIS applied to
the structure of ATA). The figure displays for each reordering algorithm the fill-in, that is
the ratio of the number of nonzeros of L and U to the number of nonzeros of A. There are
several cases for which HUND i 1 ill ..1i17 outperforms COLAMD.
Figure 5.3 presents more in detail the fill in the factors L and U obtained by the two
global strategies in our tests, HUND (with parts = 128) and METIS (applied on the structure
of ATA, which was better than A + AT). We see that for most (about two thirds) of the
matrices in our test set HUND outperforms METIS. The best result is obtained for matrix
SHERMANACB, for which HUND leads to 3 times less fill than METIS.

5.2. Results with UMFPACK. UMFPACK is a right-looking multifrontal method
which factorizes a sparse matrix using a sequence of frontal matrices. A frontal matrix is a

100 HI HUND(Kparts=128)


40 -

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 1516 17 18 19 20 21 22 23 24 25 26 27
Matrix number

FIG. 5.2. Comparison of two variants of HUND with COLAMD and METIS in terms of fill in
the factors L and U, computed as nnz(L + U I)/nnz(A)

small dense matrix F that holds k > 1 pivot rows and columns in their entirety. The lower
right portion of the frontal matrix is a contribution block C (or Schur complement) that
remains after the leading part is factorized into the corresponding k rows of U and columns
of L:

F All A12 L1 0 U[ n U 2 i 0 1
S A21 A22 L21 I 0 I 0 C22
The ordering strategy in UMFPACK combines a fill-reducing symbolic preordering with
adjustments made during numeric factorization.
As the first step of fill-reducing ordering, all pivots with zero Markowitz cost (referred
to as singletons) are removed from the matrix. Suppose a column j exists with one nonzero
entry aij. Then row i is removed from A and becomes the first row of U; column j is
also removed and becomes the first column of L. This process repeats until all remaining
columns have two or more nonzero entries. Next, a similar process is used for rows with one
nonzero entry. These entries would be the leading and trailing 1-by-1 blocks of a block upper
triangular form (Dulmage-Mendelsohn decomposition) if UMFPACK were to compute such
a form. This singleton removal process takes O(IAI) time, where |A| is the number of entries
in A; a complete permutation to block triangular form can take much more time. Most
matrices seen in practice that are reducible to block triangular form have many such leading
and trailing singleton blocks, and finding these singletons is often sufficient for obtaining the
benefits of the more-costly block triangular form.
If both the rows and columns lower or upper triangular n-by-n matrix are i, .11 I i .
permuted, the singleton removal process will find n column singletons and will permute A
into upper triangular form. No numerical factorization is then needed. One matrix in the
test collection (RAEFSKY6) falls into this category.
In the current version of UMFPACK (v5.2), singleton removal is always performed. For
these experiments (and in a future release of UMFPACK) we have added a parameter that
allows the removal of singletons to be disabled. By default, singletons are removed. We
added this option for two reasons:

+ METIS(ATA)/HUND(kparts=128)
o + Matrix 10. Failed with METIS
Z Matrix 11: Ratio 3.4

S+ + +

1.1 + +
1- + + +
+ + + +

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27
Matnx number

FIG. 5.3. Ratio of the number of nonzeros in the factors of L and U obtained by METIS relative
to HUND.

1. Removing singletons can restrict the fill-reducing ordering applied to the matrix.
If removing of singletons causes an unsymmetric permutation to the matrix, then
the symmetric and 2-by-2 strategies are disabled (even if explicitly requested by the
user). For most matrices, such unsymmetric permutations are clear indicators that
the unsymmetric strategy should be used to obtain the best fill-in. For a very few
matrices this can be a poor choice of ordering heuristics, however. More pertinent
to this paper, however, it also confounds our experimental results, since requesting
the symmetric ordering (with AMD or METIS) would otherwise lead to COLAMD
being used instead.
2. Removing singletons does not diminish the numerical accuracy of the LU factor-
ization, but it can result in a matrix L that is not well-conditioned. With normal
partial pivoting, L is always well-conditioned and any ill-conditioning of A is con-
tained in U. Some applications (linear programming solvers, for example [30])
require a well-conditioned L.
After singletons are removed, the columns (and perhaps the rows) of the remain ma-
trix are permuted to reduce fill-in in the LU factorization, using one of the four following
1. The auto strategy is the default: UMFPACK examines the nonzero pattern of the
matrix A and the entries on the diagonal of A, and selects one of the other three
strategies automatically. This usually leads to the best choice, but the user can
select one of the other strategies manually.
2. The unsymmetric strategy is best suited for matrices with an unsymmetric nonzero
pattern. A column ordering Q is chosen that minimizes the fill-in in the C'..I. 1. -.
factorization of QTATAQ, or identically the R factor in the QR factorization of
AQ. Normally, a slightly modified version of COLAMD is used [9]; this algorithm
is the same as COLAMD except that additional information about each frontal
matrix is collected for the subsequent numerical factorization. This unsymmetric
strategy is best suited for use with the HUND ordering. All results that we present

with HUND use this strategy. With the unsymmetric strategy, UMFPACK can
use COLAMD or HUND (applied to A) or either AMD or METIS (applied to the
pattern of ATA).
3. The symmetric strategy is selected for matrices with a nonzero-free diagonal (or
mostly so) symmetric (or nearly symmetric) nonzero pattern. A symmetric ordering
P is found that minimizes the fill-in in the C'l...I. -.. factorization of the matrix
P(A+AT)PT. With the symmetric strategy, UMFPACK can use AMD or METIS
(both applied to the pattern of A + AT).
4. The 2-by-2 strategy is a pre-processing step followed by the symmetric strategy.
In the pre-processing step, rows are exchanged to increase the number of nonzeros
on the diagonal. None of our results in this paper use this strategy.
The next step is the numerical factorization, which can revise the ordering computed
by the symbolic preanalysis.
In the symbolic preanalysis, the size of each frontal matrix F is bounded by the frontal
matrix that would arise in a sparse multifrontal QR factorization. Since this can be much
larger what is needed by an LU factorization, columns within each frontal matrix (the
columns of A1.) are reordered during numerical factorization to further reduce fill-in. This
column reordering is only performed for the unsymmetric strategy; it is not performed by
Numerical threshold partial pivoting is used to select pivots within the All and A21
part of F. If the symmetric or 2-by-2 strategy is used, a strong preference is given to the
diagonal entry. SuperLU also has these options.
Since UMFPACK is a right-looking method, it can consider the sparsity of a candidate
pivot row when deciding whether or not to select it. This is a key advantage over left-looking
methods such as Gilbert-Peierl's LU [16], SuperLU [10], and the implementation of Gilbert-
Peierl's left-looking sparse LU in CSparse [8]. Left-looking methods cannot consider the
sparsity of candidate pivot rows, since the matrix to the right of the pivot column has not
yet been updated when the pivot row is selected.
Removing singletons prior to the fill-reducing ordering and factorization can have a
dramatic impact on fill-in. This is another reason why UMFPACK can have a lower fill-in
than SuperLU, but it is not intrinsic to the algorithm used by UMFPACK. That is, singleton
removal is independent of the method used to factorize the remaining matrix, and any LU
factorization method (including left-looking methods such as SuperLU) could use it as well.
There are thus four primary differences between UMFPACK and SuperLU which affect
the results presented in this paper. The first three are detail of the implementation and not
intrinsic to the methods used in UMFPACK and SuperLU.
1. UMFPACK removes singletons prior to factorization; SuperLU does not.
2. UMFPACK can selects ordering strategy automatically (unsymmetric, symmetric,
or 2-by-2); SuperLU does not.
3. UMFPACK revises its column orderings within each frontal matrix to reduce fill-in;
SuperLU does not revise the column orderings with each supercolumn.
4. UMFPACK can select a sparse pivot row; SuperLU cannot.
In our results, we disable the automatic strategy selection. Instead, we use the unsym-
metric strategy for COLAMD, METIS (applied to ATA) and HUND. We use the symmetric
strategy with singletons disabled for AMD and METIS (applied to A+AT). Complete results
are shown in Table 6.2. Figure 5.4 displays a performance profile of just four of the unsym-
metric orderings (COLAMD, HUND with parts 16 and t 100, and METIS applied to
ATA). Overall, HUND provides a robust ordering with a performance profile superior to
both COLAMD and METIS. We can notice that for 70% of the matrices, the performance
of HUND partss = 16) is within 10% of the best performance. A similar profile for the
same four methods is given for the SuperLU results in Figure 5.5.

Performance profile of ordering methods for UMFPACK

90% -

80% ,,,..,

70% -


I 50%

4 40%

L % HUND (kparts=16)
-- HUND (tmin=100)
10% ."''COLAMD
1 1.1 1.2 1.3 1.4 1.5 1.6 1.7 1.6 1.9 2
Fill-in relative to best fill-in

FIG. 5.4. Performance I of ordering methods with UMFPACK. Closer to 1, better the
performance is.

Performance profile of ordering methods for SuperLU
100% _

90% --
I -

I 70%

60% t

I 50%


u20- HUND (kparts=16)
--- HUND (tmin=100)
10% .'." COLAMD
1 11 1.2 13 14 15 16 17 16 1 9 2
Fill-in relative to best fill-in

FIG. 5.5. Performance I of ordering methods with SuperLU. Closer to 1, better the per-
formance is.

5.3. Quality of the Partitioning. In this section we study the separators obtained
during HUND's unsymmetric nested dissection, when parts = 128. These separators are
important because they tend to have an impact on the fill in the factors L and U as well as
on the suitability of the reordered matrix for parallel execution. Fill in the factors can occur
only in the separators, hence smaller the separator size, fewer nonzeros should be obtained
in the factors. In a parallel execution, the communication will incur during the factorization
of the separators. Hence the communication will be reduced for separators of a smaller size.
Table 5.2 present the number of columns and the number of nonzeros in the separators
obtained at each step of the unsymmetric nested dissection. In these tables, level i denotes

Column Counts Nonzero counts
# Lvl 1 Lvl 2 Lvl 3 Lvl 1 Lvl 2 Lvl 3
1 3.33 / 3.33 3.33 / 3.33 2.05 / 2.39 3.82 / 3.82 3.82 / 3.82 2.39 / 2.77
2 0.85 / 0.85 0.91 / 0.91 0.62 / 0.66 1.18 / 1.18 1.06 / 1.07 0.64 / 0.69
3 0.65 / 0.65 0.18 / 0.28 0.13 / 0.37 1.09 / 1.09 0.27 / 0.42 0.27 / 0.82
4 0.40 / 0.40 0.24 / 0.25 0.23 / 0.31 1.03 / 1.03 0.68 / 0.81 0.70 / 0.97
5 2.97 / 2.97 1.47 / 1.51 1.32 / 1.45 3.13 / 3.13 1.54 / 1.59 1.39 / 1.53
6 4.44 / 4.44 5.13 / 5.14 1.18 / 1.30 7.12 / 7.12 12.14 / 12.16 1.80 / 2.03
7 2.66/ 2.66 1.89/ 2.27 1.38/ 1.51 4.14/ 4.14 2.88 / 3.22 1.95/ 2.54
8 13.43 / 13.43 8.14 / 8.76 3.29 / 4.61 16.36 / 16.36 10.69 / 10.86 4.13 / 5.93
9 1.83 / 1.83 1.37 / 1.40 0.90 / 0.93 1.83 / 1.83 1.45 / 1.48 0.90 / 0.92
10 43.71 / 43.71 11.51 / 23.02 2.90 / 11.59 63.23 / 63.23 8.73 / 17.46 2.12 / 8.49
11 29.39 / 29.39 10.00 / 19.99 3.13 / 12.52 45.78 / 45.78 10.07 / 20.13 3.08 / 12.32
12 1.07/ 1.07 1.07/ 1.07 0.84/ 0.97 7.94/ 7.94 7.84/ 7.92 4.13/ 5.53
13 0.14/ 0.14 0.08/ 0.11 0.06/ 0.09 0.26/ 0.26 0.14/ 0.15 0.11/ 0.17
14 15.33 / 15.33 15.69 / 15.87 2.48 / 4.41 20.47 / 20.47 18.37 / 18.40 3.01 / 5.32
15 0.63/ 0.63 0.65/ 0.65 0.64/ 0.65 1.00 / 1.00 1.02 / 1.03 1.00/ 1.03
16 2.27/ 2.27 0.91/ 0.95 0.51/ 0.73 16.12 / 16.12 4.59/ 4.77 1.86 / 4.18
17 2.58/ 2.58 2.58/ 2.58 2.29/ 2.41 2.59 / 2.59 2.59/ 2.59 2.33/ 2.44
18 31.22 / 31.22 7.80 / 7.80 2.52 / 3.10 36.85 / 36.85 10.15 / 10.17 2.92 / 3.63
19 3.61/ 3.61 1.83/ 2.05 1.70/ 1.96 4.14/ 4.14 2.09/ 2.35 1.94/ 2.25
20 0.00/ 0.00 0.60/ 1.20 0.50/ 1.02 0.00/ 0.00 1.56/ 3.11 1.33/ 2.71
21 0.18/ 0.18 0.42/ 0.46 0.34/ 0.47 0.16/ 0.16 0.64/ 0.71 0.47/ 0.61
22 31.56 / 31.56 7.88/ 7.88 2.43/ 2.68 37.35 / 37.35 10.12 / 10.12 2.82/ 3.12
23 1.71/ 1.71 0.38/ 0.74 0.25/ 0.95 3.71/ 3.71 1.32/ 2.60 0.14/ 0.45
24 0.15/ 0.15 0.11/ 0.13 0.20/ 0.23 0.21/ 0.21 0.11/ 0.15 0.29/ 0.35
25 0.45/ 0.45 0.17/ 0.22 0.20/ 0.26 0.45/ 0.45 0.17/ 0.22 0.20/ 0.26
26 4.23/ 4.23 1.66/ 2.65 0.65/ 1.23 29.43 / 29.43 12.34 / 23.91 2.74/ 6.81
27 2.75/ 2.75 2.40/ 2.40 2.37/ 2.40 1.28 / 1.28 2.94 / 2.97 2.63/ 2.77
Percentage of columns and of nonzeros in the separators corresponding to the first three levels of
unsymmetric nested dissection HUND with parts = 128. The values are as AVG/MAX,
and compute separatorsize/n 100, where n is the order of the matrix.

the step i of unsymmetric nested dissection. For each level we determine the maximum and
the average separator size. The values displayed are separatorsize/n 100. We display the
results only for the first three levels, since the results for the other levels were in general less
than 1%. Note that a value of 0.0 denotes a small value rounded to zero.
For several matrices, the number of columns and the number of nonzeros in the separa-
tors of the first and the second level are very large. For example, for matrix MULTDCOP_03
(number 10), 43.4% of the columns are in the first level separator, and an average of 11.5%
of the columns are in the second level separator. Matrices SHERMANACB, SINC12, SINC15,
and SINC18 (numbers 11, 14,18,22) have more than 15% of the columns in the first level
separator. As already previously observed and as reported in Table 6.1, for matrices in SINC
f I.... ., HUND leads to a high amount of fill in the factors L and U, between 32 and 62. This
observation shows that the size of the separator has an important impact on the quality of
the reordering, that is the number of nonzeros in the factors L and U.
However this is not always true. For example the matrices ZHAo2 and MARK3JAC140SC
(numbers 9, 15) have separators of small size. But the fill in the factors L and U is high, 61
for ZHAO2 and 46 for MARK3JAC140SC.

6. Conclusions. We have presented a new ordering algorithm (HUND) for unsym-
metric sparse matrix factorization, based on hypergraph partitioning and unsymmetric nested
dissection. To enhance performance, we proposed a hybrid method that combines the nested
dissection with local reordering. Our method allows partial pivoting, without destroying
sparsity. We have tested the method using SuperLU and UMFPACK, two well-known par-
tial pivoting LU codes. Empirical experiments show that our method is highly competitive
with existing ordering methods. In particular, it is robust in the sense that it in most cases
(23 out of 27 in our study) it performs close to the best of all the other existing methods
(often within 11 '. Thus, it is a good choice as an all-purpose ordering method.
The HUND method was designed for parallel computing, though we only evaluated it
in serial here. The recursive top-down design allows coarse-grain parallelism, as opposed to
local search methods like AMD and COLAMD. For symmetric systems, nested dissection
ordering is considered superior for large systems and it is reasonable to expect the same holds
for unsymmetric systems. The most expensive part of HUND is hypergraph partitioning,
which can be done efficiently in parallel using the Zoltan toolkit [11]. The matching for strong
diagonal can also be performed in parallel [28], though no parallel MC64 is yet available.
Local reordering can be done locally in serial. Thus, our approach is well suited for fully
parallel solvers that aim at being time and memory scalable [18, 25].
There are several directions for future work. First, we only used the default settings
for the hypergraph partitioner. By default, Patoh tries to achieve a balance of 3%. This
is quite strict, and perhaps a looser tolerance would work better in HUND. We can likely
reduce fill and operation count by allowing more imbalance in the partitioning (and thus
the elimination tree). A second future optimization is the "Mondriaan" version with varying
directions outlined in section 4. A third direction is to study hybridization with other local
(greedy) ordering methods, in particular, the recent unsymmetric method by Amestoy, Li,
and Ng [2].


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Appendix. In Table 6.1 we present the detailed experimental data that was used to
create Figures 5.1, 5.2 and 5.3 in section 5, using SuperLU.
Table 6.2 presents the results using UMFPACK, for Figure 5.4.

(A + A) (ATA) (A + A) (ATA) k=16 k=128 t=1 t=100
1 46.6 13.5 13.6 46.7 22.8 15.3 15.0 15.2 15.2 15.2
2 337.9 19.2 18.3 112.3 47.1 16.8 19.6 16.8 17.2 16.9
3 1.0 1.1 1.1 1.0 1.0 1.1 1.1 1.1 1.1 1.1
4 18.3 3.5 3.4 10.4 10.1 3.9 3.4 3.6 3.7 3.6
5 5.7 7.7 7.5 5.6 6.0 8.3 8.3 8.4 8.2 8.3
6 105.7 42.8 42.0 86.8 60.9 32.9 30.6 30.0 30.0 30.0
7 22.4 4.0 3.9 9.4 6.7 4.6 4.5 4.5 4.5 4.5
8 28.8 7.6 7.9 23.5 15.5 7.0 6.8 6.7 6.7 6.7
9 94.7 111.9 225.2 66.0 71.5 62.7 61.9 62.3
10 19.8 93.4 8.1 218.0 83.9 39.9 4.9 3.2 3.2
11 3.9 37.0 23.1 14.5 40.2 39.0 40.8 11.6 11.5 11.6
12 93.3 23.0 18.7 85.9 76.8 19.4 16.8 16.5 16.8 16.7
13 10.1 5.3 4.8 5.2 17.2 5.3 5.1 4.9 4.7 4.7
14 68.6 47.2 54.4 55.1 48.2 32.3 33.9 32.8 32.7 32.8
15 144.8 116.0 102.8 54.7 49.1 46.5 46.6 46.5
16 17.2 12.9 13.4 18.1 27.8 14.2 11.1 11.1 11.4 11.2
17 80.5 26.1 24.9 33.0 28.3 27.6 29.3 29.8 29.6 29.6
18 92.5 61.0 68.1 72.1 66.5 42.4 53.5 52.2 52.4 52.2
19 13.1 14.7 28.5 13.0 13.6 12.4 12.3 12.4
20 14.7 5.5 5.5 15.2 8.5 7.1 6.0 6.1 6.1 6.1
21 27.6 4.5 4.5 13.2 9.6 4.8 4.8 5.0 4.9 4.9
22 76.3 81.4 85.7 55.9 64.7 62.5 62.6 62.6
23 29.5 15.8 13.1 45.8 20.8 23.9 24.1 23.5 23.2
24 36.0 4.5 4.6 13.1 9.9 5.0 4.7 4.9 4.9 4.9
25 8.3 14.0 16.7 9.9 8.9 13.4 15.8 15.2 14.3 14.5
26 23.2 25.8 41.5 16.4 16.8 15.5 15.1 15.2
27 27.8 28.1 -61.2 28.2 29.5 28.8 28.8 28.8
Fill in the factors L and U, computed as nnz(L + U I)/nnz(A) obtained by fill-
reducing strategies with SuperLU

(AT +A) (ATA) k=16 k=128 t=1 t=100
1 11.5 40.5 23.8 14.5 14.3 14.1 14.2 14.3
2 14.5 134.4 125.0 14.0 14.2 13.8 13.9 13.8
3 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0
4 2.5 4.0 11.7 2.8 2.7 2.9 2.8 2.9
5 6.9 5.7 6.0 7.7 7.2 7.3 7.2 7.3
6 32.9 45.0 36.9 29.3 25.0 24.8 24.8 24.9
7 3.0 13.0 9.5 3.8 3.6 3.6 3.6 3.6
8 1.0 2.0 2.1 1.0 1.0 1.0 1.0 1.0
9 78.7 47.2 28.4 55.7 57.6 51.0 50.3 50.7
10 1.9 2.3 2.8 2.3 1.9 2.0 1.9 2.0
11 4.4 4.1 4.9 4.1 4.1 4.1 4.0 4.1
12 8.8 7.6 9.5 10.7 9.2 9.2 9.4 9.3
13 3.0 4.7 13.0 3.6 3.3 3.5 3.4 3.4
14 38.3 36.5 35.4 20.6 22.7 21.9 19.9 22.3
15 79.6 63.9 42.0 44.7 41.9 39.7 39.2 40.1
16 10.2 37.8 11.6 9.5 9.6 10.2 9.6
17 22.0 16.9 17.5 24.9 25.6 25.6 25.6 25.5
18 49.9 29.9 41.4 39.7 40.0 41.5
19 6.8 14.4 11.6 8.5 6.2 7.8 8.3 7.8
20 4.6 9.8 6.1 5.6 5.0 4.6 4.7 4.7
21 3.8 26.8 20.9 4.2 3.8 4.0 4.0 4.0
22 38.9
23 5.9 14.4 8.3 6.1 6.1 6.1 6.3
24 3.8 4.2 3.8 4.0 4.0 4.0
25 14.3 9.4 8.9 11.6 13.1 12.5 11.5 12.1
26 20.2 11.2 11.8 11.0 10.6 10.7
27 23.7 25.8 25.6 25.4 24.6 24.8 24.8
Fill in the factors L and U, computed as nnz(L + U I)/nnz(A), using UMFPACK

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Last updated October 10, 2010 - - mvs