Chap8 slides

Dense Matrix Algorithms
Ananth Grama, Anshul Gupta,
George Karypis, and Vipin Kumar
To accompany the text “Introduction to Parallel Computing”,
Addison Wesley, 2003.

Topic Overview
• Matrix-Vector Multiplication
• Matrix-Matrix Multiplication
• Solving a System of Linear Equations

Matix Algorithms: Introduction
• Due to their regular structure, parallel computations
involving matrices and vectors readily lend themselves to
data-decomposition.
• Typical algorithms rely on input, output, or intermediate
data decomposition.
• Most algorithms use one- and two-dimensional block,
cyclic, and block-cyclic partitionings.

Matrix-Vector Multiplication
• We aim to multiply a dense n x n matrix A with an n x 1
vector x to yield the n x 1 result vector y.
• The serial algorithm requires n2 multiplications and
additions.

Matrix-Vector Multiplication:
Rowwise 1-D Partitioning
• The n x n matrix is partitioned among n processors, with
each processor storing complete row of the matrix.
• The n x 1 vector x is distributed such that each process
owns one of its elements.

Multiplication of an n x n matrix with an n x 1 vector using
rowwise block 1-D partitioning. For the one-row-per-process
case, p = n.

• Since each process starts with only one element of x ,
an all-to-all broadcast is required to distribute all the
elements to all the processes.
• Process Pi now computes .
• The all-to-all broadcast and the computation of y[i] both
take time Θ(n) . Therefore, the parallel time is Θ(n) .

• Consider now the case when p < n and we use block 1D
partitioning.
• Each process initially stores n=p complete rows of the
matrix and a portion of the vector of size n=p.
• The all-to-all broadcast takes place among p processes
and involves messages of size n=p.
• This is followed by n=p local dot products.
• Thus, the parallel run time of this procedure is
This is cost-optimal.

Scalability Analysis:
• We know that T0 = pTP - W, therefore, we have,
• For isoefficiency, we have W = KT0, where K = E/(1 – E)
for desired efficiency E.
• From this, we have W = O(p2) (from the tw term).
• There is also a bound on isoefficiency because of
concurrency. In this case, p < n, therefore, W = n2 =
Ω(p2).
• Overall isoefficiency is W = O(p2).

2-D Partitioning
• The n x n matrix is partitioned among n2 processors such
that each processor owns a single element.
• The n x 1 vector x is distributed only in the last column of
n processors.

Matrix-Vector Multiplication: 2-D Partitioning
Matrix-vector multiplication with block 2-D partitioning. For the
one-element-per-process case, p = n2 if the matrix size is n x n .

2-D Partitioning
• We must first align the vector with the matrix
appropriately.
• The first communication step for the 2-D partitioning
aligns the vector x along the principal diagonal of the
matrix.
• The second step copies the vector elements from each
diagonal process to all the processes in the
corresponding column using n simultaneous broadcasts
among all processors in the column.
• Finally, the result vector is computed by performing an
all-to-one reduction along the columns.

2-D Partitioning
• Three basic communication operations are used in this
algorithm: one-to-one communication to align the vector
along the main diagonal, one-to-all broadcast of each
vector element among the n processes of each column,
and all-to-one reduction in each row.
• Each of these operations takes Θ(log n) time and the
parallel time is Θ(log n) .
• The cost (process-time product) is Θ(n2 log n) ; hence,
the algorithm is not cost-optimal.

2-D Partitioning
• When using fewer than n2 processors, each process
owns an block of the matrix.
• The vector is distributed in portions of elements in
the last process-column only.
• In this case, the message sizes for the alignment,
broadcast, and reduction are all .
• The computation is a product of an
submatrix with a vector of length .

2-D Partitioning
• The first alignment step takes time
• The broadcast and reductions take time
• Local matrix-vector products take time
• Total time is

2-D Partitioning
• Scalability Analysis:
•
• Equating T0 with W, term by term, for isoefficiency, we
have, as the dominant term.
• The isoefficiency due to concurrency is O(p).
• The overall isoefficiency is (due to the
network bandwidth).
• For cost optimality, we have, . For this,
we have,

Matrix-Matrix Multiplication
• Consider the problem of multiplying two n x n dense,
square matrices A and B to yield the product matrix C =A
x B.
• The serial complexity is O(n3).
• We do not consider better serial algorithms (Strassen's
method), although, these can be used as serial kernels
in the parallel algorithms.
• A useful concept in this case is called block operations.
In this view, an n x n matrix A can be regarded as a q x q
array of blocks Ai,j (0 ≤ i, j < q) such that each block is an
(n/q) x (n/q) submatrix.
• In this view, we perform q3 matrix multiplications, each
involving (n/q) x (n/q) matrices.

• Consider two n x n matrices A and B partitioned into p
blocks Ai,j and Bi,j (0 ≤ i, j < ) of size
each.
• Process Pi,j initially stores Ai,j and Bi,j and computes block
Ci,j of the result matrix.
• Computing submatrix Ci,j requires all submatrices Ai,k
and Bk,j for 0 ≤ k < .
• All-to-all broadcast blocks of A along rows and B along
columns.
• Perform local submatrix multiplication.

• The two broadcasts take time
• The computation requires multiplications of
sized submatrices.
• The parallel run time is approximately
• The algorithm is cost optimal and the isoefficiency is
O(p1.5) due to bandwidth term tw and concurrency.
• Major drawback of the algorithm is that it is not memory
optimal.

Matrix-Matrix Multiplication:
Cannon's Algorithm
• In this algorithm, we schedule the computations of the
processes of the ith row such that, at any given time,
each process is using a different block Ai,k.
• These blocks can be systematically rotated among the
processes after every submatrix multiplication so that
every process gets a fresh Ai,k after each rotation.

Cannon's Algorithm
Communication steps in Cannon's algorithm on 16 processes.

Cannon's Algorithm
• Align the blocks of A and B in such a way that each
process multiplies its local submatrices. This is done by
shifting all submatrices Ai,j to the left (with wraparound)
by i steps and all submatrices Bi,j up (with wraparound)
by j steps.
• Perform local block multiplication.
• Each block of A moves one step left and each block of B
moves one step up (again with wraparound).
• Perform next block multiplication, add to partial result,
repeat until all blocks have been multiplied.

Cannon's Algorithm
• In the alignment step, since the maximum distance over
which a block shifts is , the two shift operations
require a total of time.
• Each of the single-step shifts in the compute-and-
shift phase of the algorithm takes time.
• The computation time for multiplying matrices of size
is .
• The parallel time is approximately:
• The cost-efficiency and isoefficiency of the algorithm are
identical to the first algorithm, except, this is memory
optimal.

DNS Algorithm
• Uses a 3-D partitioning.
• Visualize the matrix multiplication algorithm as a cube .
matrices A and B come in two orthogonal faces and
result C comes out the other orthogonal face.
• Each internal node in the cube represents a single add-
multiply operation (and thus the complexity).
• DNS algorithm partitions this cube using a 3-D block
scheme.

DNS Algorithm
• Assume an n x n x n mesh of processors.
• Move the columns of A and rows of B and perform
broadcast.
• Each processor computes a single add-multiply.
• This is followed by an accumulation along the C
dimension.
• Since each add-multiply takes constant time and
accumulation and broadcast takes log n time, the total
runtime is log n.
• This is not cost optimal. It can be made cost optimal by
using n / log n processors along the direction of
accumulation.

DNS Algorithm
The communication steps in the DNS algorithm while
multiplying 4 x 4 matrices A and B on 64 processes.

DNS Algorithm
Using fewer than n3 processors.
• Assume that the number of processes p is equal to q3 for
some q < n.
• The two matrices are partitioned into blocks of size (n/q)
x(n/q).
• Each matrix can thus be regarded as a q x q two-
dimensional square array of blocks.
• The algorithm follows from the previous one, except, in
this case, we operate on blocks rather than on individual
elements.

DNS Algorithm
Using fewer than n3 processors.
• The first one-to-one communication step is performed for
both A and B, and takes time for each matrix.
• The two one-to-all broadcasts take
time for each matrix.
• The reduction takes time .
• Multiplication of submatrices takes
time.
• The parallel time is approximated by:
• The isoefficiency function is .

Solving a System of Linear Equations
• Consider the problem of solving linear equations of the
kind:
• This is written as Ax = b, where A is an n x n matrix with
A[i, j] = ai,j, b is an n x 1 vector [ b0, b1, … , bn ]T, and x is
the solution.

Solving a System of Linear Equations
Two steps in solution are: reduction to triangular form, and
back-substitution. The triangular form is as:
We write this as: Ux = y .
A commonly used method for transforming a given matrix
into an upper-triangular matrix is Gaussian Elimination.

Gaussian Elimination
Serial Gaussian Elimination

• The computation has three nested loops - in the kth
iteration of the outer loop, the algorithm performs (n-k)2
computations. Summing from k = 1..n, we have roughly
(n3/3) multiplications-subtractions.
A typical computation in Gaussian elimination.

Parallel Gaussian Elimination
• Assume p = n with each row assigned to a processor.
• The first step of the algorithm normalizes the row. This is
a serial operation and takes time (n-k) in the kth
iteration.
• In the second step, the normalized row is broadcast to all
the processors. This takes time .
• Each processor can independently eliminate this row
from its own. This requires (n-k-1) multiplications and
subtractions.
• The total parallel time can be computed by summing
from k = 1 … n-1 as
• The formulation is not cost optimal because of the tw
term.

Parallel Gaussian Elimination
Gaussian elimination steps during the iteration corresponding k = 3
for an 8 x 8 matrix partitioned rowwise among eight processes.

Parallel Gaussian Elimination:
Pipelined Execution
• In the previous formulation, the (k+1)st iteration starts
only after all the computation and communication for the
kth iteration is complete.
• In the pipelined version, there are three steps -
normalization of a row, communication, and elimination.
These steps are performed in an asynchronous fashion.
• A processor Pk waits to receive and eliminate all rows
prior to k.
• Once it has done this, it forwards its own row to
processor Pk+1.

Pipelined Execution
Pipelined Gaussian elimination on a 5 x 5 matrix partitioned
withone row per process.

Pipelined Execution
• The total number of steps in the entire pipelined
procedure is Θ(n).
• In any step, either O(n) elements are communicated
between directly-connected processes, or a division step
is performed on O(n) elements of a row, or an elimination
step is performed on O(n) elements of a row.
• The parallel time is therefore O(n2) .
• This is cost optimal.

Pipelined Execution
The communication in the Gaussian elimination iteration
corresponding to k = 3 for an 8 x 8 matrix distributed among
four processes using block 1-D partitioning.

Block 1D with p < n
• The above algorithm can be easily adapted to the case
when p < n.
• In the kth iteration, a processor with all rows belonging to
the active part of the matrix performs (n – k -1) / np
multiplications and subtractions.
• In the pipelined version, for n > p, computation dominates
communication.
• The parallel time is given by:
or approximately, n3/p.
• While the algorithm is cost optimal, the cost of the parallel
algorithm is higher than the sequential run time by a factor
of 3/2.

Block 1D with p < n
Computation load on different processes in block and cyclic
1-D partitioning of an 8 x 8 matrix on four processes during the
Gaussian elimination iteration corresponding to k = 3.

Block 1D with p < n
• The load imbalance problem can be alleviated by using a
cyclic mapping.
• In this case, other than processing of the last p rows,
there is no load imbalance.
• This corresponds to a cumulative load imbalance
overhead of O(n2p) (instead of O(n3) in the previous
case).

2-D Mapping
• Assume an n x n matrix A mapped onto an n x n mesh
of processors.
• Each update of the partial matrix can be thought of as a
scaled rank-one update (scaling by the pivot element).
• In the first step, the pivot is broadcast to the row of
processors.
• In the second step, each processor locally updates its
value. For this it needs the corresponding value from the
pivot row, and the scaling value from its own row.
• This requires two broadcasts, each of which takes log n
time.
• This results in a non-cost-optimal algorithm.

2-D Mapping
Various steps in the Gaussian elimination iteration
corresponding to k = 3 for an 8 x 8 matrix on 64
processes arranged in a logical two-dimensional mesh.

2-D Mapping with Pipelining
• We pipeline along two dimensions. First, the pivot value is pipelined
along the row. Then the scaled pivot row is pipelined down.
• Processor Pi,j (not on the pivot row) performs the elimination step
A[i, j] := A[i, j]] - A[i, k] A[k, j] as soon as A[i, k] and A[k, j] are
available.
• The computation and communication for each iteration moves
through the mesh from top-left to bottom-right as a ``front.''
• After the front corresponding to a certain iteration passes through a
process, the process is free to perform subsequent iterations.
• Multiple fronts that correspond to different iterations are active
simultaneously.

• If each step (division, elimination, or communication) is
assumed to take constant time, the front moves a single
step in this time. The front takes Θ(n) time to reach Pn-1,n-
1.
• Once the front has progressed past a diagonal
processor, the next front can be initiated. In this way, the
last front passes the bottom-right corner of the matrix
Θ(n) steps after the first one.
• The parallel time is therefore O(n) , which is cost-optimal.

Pipelined Gaussian elimination for a 5 x 5 matrix with 25 processors.

2-D Mapping with Pipelining and p < n
• In this case, a processor containing a completely active
part of the matrix performs n2/p multiplications and
subtractions, and communicates words along its
row and its column.
• The computation dominates communication for n >> p.
• The total parallel run time of this algorithm is (2n2/p) x n,
since there are n iterations. This is equal to 2n3/p.
• This is three times the serial operation count!

The communication steps in the Gaussian elimination
iteration corresponding to k = 3 for an 8 x 8 matrix on 16
processes of a two-dimensional mesh.

Computational load on different processes in block and cyclic
2-D mappings of an 8 x 8 matrix onto 16 processes during
the Gaussian elimination iteration corresponding to k = 3.

2-D Cyclic Mapping
• The idling in the block mapping can be alleviated using a
cyclic mapping.
• The maximum difference in computational load between
any two processes in any iteration is that of one row and
one column update.
• This contributes to the overhead function. Since
there are n iterations, the total overhead is .

with Partial Pivoting
• For numerical stability, one generally uses partial
pivoting.
• In the k th iteration, we select a column i (called the pivot
column) such that A[k, i] is the largest in magnitude
among all A[k, i] such that k ≤ j < n.
• The k th and the i th columns are interchanged.
• Simple to implement with row-partitioning and does not
add overhead since the division step takes the same
time as computing the max.
• Column-partitioning, however, requires a global
reduction, adding a log p term to the overhead.
• Pivoting precludes the use of pipelining.

Gaussian Elimination with Partial Pivoting:
2-D Partitioning
• Partial pivoting restricts use of pipelining, resulting in
performance loss.
• This loss can be alleviated by restricting pivoting to
specific columns.
• Alternately, we can use faster algorithms for broadcast.

Solving a Triangular System:
Back-Substitution
• The upper triangular matrix U undergoes back-
substitution to determine the vector x.
A serial algorithm for back-substitution.

Back-Substitution
• The algorithm performs approximately n2/2 multiplications and
subtractions.
• Since complexity of this part is asymptotically lower, we should
optimize the data distribution for the factorization part.
• Consider a rowwise block 1-D mapping of the n x n matrix U with
vector y distributed uniformly.
• The value of the variable solved at a step can be pipelined back.
• Each step of a pipelined implementation requires a constant amount
of time for communication and Θ(n/p) time for computation.
• The parallel run time of the entire algorithm is Θ(n2/p).

Back-Substitution
• If the matrix is partitioned by using 2-D partitioning on a
logical mesh of processes, and the elements of
the vector are distributed along one of the columns of the
process mesh, then only the processes containing
the vector perform any computation.
• Using pipelining to communicate the appropriate
elements of U to the process containing the
corresponding elements of y for the substitution step
(line 7), the algorithm can be executed in time.
• While this is not cost optimal, since this does not
dominate the overall computation, the cost optimality is
determined by the factorization.

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