X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/book_gpu.git/blobdiff_plain/4932967a1c684dc3f7ed04c19144101278b79972..2ce2baf7820f44ab044b4df98722576116551e57:/BookGPU/Chapters/chapter16/exp.tex?ds=inline

diff --git a/BookGPU/Chapters/chapter16/exp.tex b/BookGPU/Chapters/chapter16/exp.tex
index 6fd3eb9..a527ba4 100644
--- a/BookGPU/Chapters/chapter16/exp.tex
+++ b/BookGPU/Chapters/chapter16/exp.tex
@@ -47,7 +47,7 @@ Section~\ref{sec:gpu}.
   \includegraphics[width=.6\textwidth]{./Chapters/chapter16/figures/flyback_zoomin_emb.eps}
   \label{fig:flybackZoom}
 }
-\caption{Flyback converter solution calculated by envelope-following.
+\caption[Flyback converter solution calculated by envelope-following.]{Flyback converter solution calculated by envelope-following.
 The red curve is traditional SPICE simulation result, and
 the back curve is the envelope-following output with simulation points
 marked.}
@@ -64,19 +64,19 @@ marked.}
 
 We use several integrated on-chip converters as simulation examples
 to measure running time and speedup. They include a Buck converter,
-a quasi-resonant flyback converter (shown in Fig.~\ref{fig:flyback}),
+a quasi-resonant flyback converter (shown in Figure~\ref{fig:flyback}),
 and two boost converters.
 Each converter is directly integrated with on-chip power grid networks,
-since the performance of converters should be studied with their loads and
+since the performance of the converters should be studied with their loads and
 we can easily observe the waveforms at different nodes in a power
-grid (see Fig.~\ref{fig:pg} for a simplified power grid structure).
+grid (see Figure~\ref{fig:pg} for a simplified power grid structure).
 
-Fig.~\ref{fig:flyback_wave}
-and Fig.~\ref{fig:buck_wave}
-shows the waveform at output node of the resonant flyback converter
+Figure~\ref{fig:flyback_wave}
+and Figure~\ref{fig:buck_wave}
+show the waveform at output node of the resonant flyback converter
 and the Buck converter.
 Note that on the envelope curve, the darker
-dots in separated segments indicate the real simulation points were
+dots in separated segments indicate the real simulation points that were
 calculated in those cycles, and the segments without dots are the
 envelope jumps where no simulation were done.
 It can be verified that the proposed Gear-2 envelope-following method
@@ -84,11 +84,6 @@ produces a envelope matching the original waveform well.
 
 \begin{table}
 \centering
-\caption{CPU and GPU time comparisons (in seconds) for solving Newton update equation
-  with the proposed Gear-2 sensitivity.
-}
-\vspace{0.1in}
-\label{table:circuit}
 {%\normalsize
 \begin{tabular}{@{}c|c|c|c|c|c|c@{}}
 \hline\hline
@@ -102,18 +97,22 @@ Boost-2    & 1093   & 729.5  & 720.8 &  71.0  &  8.5 & 9.9$\times$ \\
 \hline\hline
 \end{tabular}
 }
+\caption{CPU and GPU time comparisons (in seconds) for solving Newton update equation
+  with the proposed Gear-2 sensitivity.
+}
+\label{table:circuit}
 \end{table}
 
 For the comparison  of running time spent in solving
 Newton update equation, Table~\ref{table:circuit} lists the time
-costed by direct method, explicit GMRES, matrix-free GMRES,
+cost by direct method, explicit GMRES, matrix-free GMRES,
 and GPU matrix-free GMRES. All methods carry out the Gear-2 based
 envelope-following method, but they handle the sensitivity and
 equation solving in different implementation steps.
 It is obvious that as long as the sensitivity matrix is explicitly formed,
-such as the cases in direct method and explicitly GMRES,
+such as in the cases of direct method and explicit GMRES,
 the cost is much higher than the implicit methods.
-When matrix-free technique is applied to generate matrix-vector
+When the matrix-free technique is applied to generate matrix-vector
 products implicitly, the computation cost is greatly reduced.
 Thus, for the same example, implicit GMRES would be one order
 of magnitude faster than explicit GMRES. Furthermore, our GPU parallel