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Commit 9bebe5b1e3d7 authored by Ashwin Vishnu's avatar Ashwin Vishnu
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FluidSim: comparison with sequential walltimes

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......@@ -687,10 +687,12 @@
\subsubsection*{Scalability}
Scalability can be quantified by speedup $S$ which is a measure of the time taken
to complete $N$ iterations for different number of processes, $n_p$. We shall
refrain from comparing sequential runs in this context, since the operators used
for the sequential mode differ from the parallel mode, especially the FFT
class. Speedup is formally defined here as:
to complete $N$ iterations for different number of processes, $n_p$.
%We shall
%refrain from comparing sequential runs in this context, since the operators used
%for the sequential mode differ from the parallel mode, especially the FFT
%class.
Speedup is formally defined here as:
\begin{equation}
S_\alpha(n_p) = \frac
......@@ -743,6 +745,30 @@
\fluidpack{fft}, namely \codeinline{fft2d.mpi\_with\_fftw1d} and
\codeinline{fft2d.mpi\_with\_fftwmpi2d}.
Although the FFT classes are different for sequential and parallel
runs, for the sake of comparison, we have tabulated the actual
walltime elapsed in Table~\ref{table:seqpar}. Clearly due to the differences
in the implementations, for \codeinline{fftw1d}-based classes it takes a
performance hit with parallelization, while for classes based on
\codeinline{fftw2d} the performance improves.
\begin{table}
\centering
\begin{tabular}{lrlr}
\hline
FFT class & Time ($n_p=1$) & FFT class & Time ($n_p=2$) \\
\hline
\codeinline{fft2d.with\_fftw1d} & 6.63 &
\codeinline{fft2d.mpi\_with\_fftw1d} & 7.63 \\
\codeinline{fft2d.with\_fftw2d} & 5.59 &
\codeinline{fft2d.mpi\_with\_fftwmpi2d} & 3.91 \\
\hline
\end{tabular}
\caption{Elapsed times (in seconds) for twenty time steps of 1024 x 1024 case
with the 2D Navier-Stokes 2D (\codeinline{fluidsim.solvers.ns2d}) solver.}
\label{table:seqpar}
\end{table}
\begin{figure}[htp]
\centering
\includegraphics[width=\linewidth]{tmp/fig_bench_strong2d}
......
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