The best grid shape is determined by the algorithm implemented in
the library and the underlying physical network. A one link physical
network will favor 
or 
. This affects the scalabilty
of the algorithm, but reduces the overhead due to message collisions.
It is possible to predict the best grid shape given the number
of processes available. The current algorithms for the factorization
or reduction routines can be split into two categories.
If at every step of the algorithm a block of columns and/or rows
needs to be broadcast, as in the LU or QR factorizations,
it is possible to pipeline this communication phase and overlap it
with some computation. The direction of the pipeline determines the
shape of the grid. For example, the LU, QR and QL factorizations
perform better for ``flat'' process grids (
). These
factorizations share a common bottleneck of performing a reduction
operation along each column (for pivoting in LU, and for computing
a norm in QR and QL). The first implication of this observation
is that large latency message passing perform better on a ``flat''
grid than on a square grid. Secondly, after this reduction has been
performed, it is important to update the next block of columns as
fast as possible. This is done by broadcasting the current block of
columns using a ring topology, i.e, feeding the ongoing communication
pipe. Similarly, the performance of the LQ and RQ factorizations take
advantage of ``tall'' grids (
) for the same reasons, but
transposed.
The theoretical efficiency of the LU factorization can be estimated by (see (1), (2)):
For large 
, the last term in the denominator dominates, and it is
minimized by choosing a 
slightly smaller than 
. 
works
well on Intel machines. For smaller , the middle term dominates,
and it becomes more important to choose a small . Suppose that
we keep the ratio 
constant as 
increases, thus we
have 
and 
, where 
and 
are constant [9]. Moreover, let ignore the 
factor for a moment. In this case, 
and 
are proportional to 
and 
must grow with
to maintain efficiency. For sufficient large , the
factor cannot be ignored, and the performance will slowly degrade
with the number of processors . This phenomenon is observed in
practice as shown in the plot above showing the efficiency of the
LU factorization on the Intel Paragon.
Click here to view the left figure
Click here to view the right figure
The second group of routines physically transpose a block of columns and/or rows at every step of the algorithm. In these cases, it is not usually possible to maintain a communication pipeline, and thus square or near square grids are more optimal. This is the case for the algorithms used for implementing the Cholesky factorization, the matrix inversion and the reduction to bidiagonal form (BRD), Hessenberg form (HRD) and tridiagonal form (TRD). For example, the update phase of the Cholesky factorization of a lower symmetric matrix physically transposes the current block of columns of the lower triangular factor.
Assume now that at most processes are available. A natural
question arising is: could we decide what process grid
should be used? Similarly, depending
on , it is not always possible to factor 
to
create the appropriate grid. For example, if is prime, the
only possible grids are 
and 
. If such
grids are particularly bad for performance, it may be beneficial
to let some processors remain idle, so the remainder can be formed
into a ``squarer'' grid [15].
These problems can be analyzed by a complicated function of the
machine and problem parameters. It is possible to develop models
depending on the machine and problem parameters which accurately
estimate the impact of modifying the shape of the grid on the total
execution time, as well as predicting the necessary amount of extra
memory required for each routine.