Added results description and observations

Michał Szydłowski
1 parent f5cb99cb
Showing 5 changed files with 48 additions and 38 deletions
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bare_jrnl_compsoc.tex
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+\bibcite{IEEEhowto:kopka}{1}
+\bibcite{polybench}{2}
+\@writefile{toc}{\contentsline {section}{Appendix\nobreakspace  A: Proof of the First Zonklar Equation}{3}}
+\@writefile{toc}{\contentsline {section}{Appendix\nobreakspace  B}{3}}
+\@writefile{toc}{\contentsline {section}{References}{3}}
 \@writefile{toc}{\contentsline {section}{Biographies}{3}}
 \@writefile{toc}{\contentsline {subsection}{Michael Shell}{3}}
 \@writefile{toc}{\contentsline {subsection}{John Doe}{3}}
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@@ -718,13 +718,13 @@ Subsubsection text here.
 \section{Results}
-(Introduction)
+The efficiency of our proposed solution has been measured by compiling and running benchmarks available in Polybench \cite{polybench}. Some of the benchmarks (\textit{cholesky}, \textit{correlation}, \textit{dynprog } and \textit{gramshmidt}) had to be excluded from our analysis, since Polly was unable to detect a valid SCoP in them. Also the \textit{floyd-warshall} benchmark has been skipped due to a very high amount of time needed to run in on a large dataset that has been used for other benchmarks. The results are summarized in \ref{tab:small} and \ref{tab:large}. The final tile size column denotes the tile size on which our algorithm has settled after N = 40 executions of the test program. Every time result is an average value over those measurements. 
 \newcolumntype{C}{>{\centering\arraybackslash}X}
-\begin{minipage}{\linewidth}
+\begin{minipage}{\linewidth\hspace{-2mm}}
 \begin{center}
-\captionof{table}{Average time measurements for Polybench benchmarks with small dataset}\label{tab:time}
+\captionof{table}{Average time measurements for Polybench benchmarks with small dataset}\label{tab:small}
 \begin{tabularx}{\textwidth}{ |C|C|C|C|C| } 
 \hline
  name & default tile size time [\(\mu\)s] & dynamic tiling time [\(\mu\)s] & final tile size & relative time gain [\%] \\
@@ -787,7 +787,7 @@ trmm &amp;	7299.68 &amp;	7063.86 &amp;	16 &amp;	3.23 \\
 \begin{minipage}{\linewidth}
 \begin{center}
-\captionof{table}{Average time measurements for Polybench benchmarks with large dataset}\label{tab:time}
+\captionof{table}{Average time measurements for Polybench benchmarks with large dataset}\label{tab:large}
 \begin{tabularx}{\textwidth}{ |C|C|C|C|C| } 
 \hline
  name & default tile size time [ms] & dynamic tiling time [ms] & final tile size & relative time gain [\%] \\
@@ -848,8 +848,11 @@ trmm &amp;	37597.88 &amp;	66351.85 &amp;	8 &amp;	-76.48 \\
 \end{center}
 \end{minipage}
+\vspace{1cm}
+
+Looking at the results, we can easily observe, that our parametric tiling system performs much better for small datasets. There are some time losses as well, but they are mostly insignificant (less than 5\%), and only 4 of them exhibit a substantial overhead. All other results remained roughly the same, or display a massive time gain. 
-(Observations)
+The reason the efficiency of the system drops significantly for large datasets, is that apparently loops with much larger iteration spaces no longer respond well to further decrease of tile size (both exemplary tile sizes that we used - 8 and 16 - are smaller than the default 32). It is possible that in this case it is better to increase the tile size instead. 
 \section{Conclusion}
 The conclusion goes here.
@@ -932,6 +935,9 @@ The authors would like to thank...
 H.~Kopka and P.~W. Daly, \emph{A Guide to \LaTeX}, 3rd~ed.\hskip 1em plus
   0.5em minus 0.4em\relax Harlow, England: Addison-Wesley, 1999.
+\bibitem{polybench}
+\emph{Polybench}, \\ URL: http://web.cse.ohio-state.edu/\~{}pouchet/software/polybench/
+
 \end{thebibliography}
 % biography section