Mercurial > hg > Papers > 2018 > parusu-master

changeset 33:6da0158a17a1

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Add bitonicSort
author Tatsuki IHA <innparusu@cr.ie.u-ryukyu.ac.jp>
date Sat, 03 Feb 2018 22:46:48 +0900
parents ebcf093795f3
children 6b1fa842b85c
files paper/evaluation.tex paper/fig/twice.pdf paper/fig/twice.svg paper/fig/twice.xbb paper/master_paper.pdf
diffstat 5 files changed, 77 insertions(+), 47 deletions(-) [+]
line wrap: on
line diff
--- a/paper/evaluation.tex	Sat Feb 03 20:56:35 2018 +0900
+++ b/paper/evaluation.tex	Sat Feb 03 22:46:48 2018 +0900
@@ -44,8 +44,8 @@
 Twice は並列実行の依存関係もなく、データ並列での実行に適した課題である。
 そのため、 通信時間を考慮しなければ CPU よりコア数が多い GPU が有利となる。
 
-要素数$2^{23}$ のデータに対する Twice の実行結果を \tabref{twice}、\figref{twice}に示す。
-CPU 実行の際は $2^{23}$ のデータを 64 個のTask に分割して並列実行を行っている。
+要素数$2^{24}$ のデータに対する Twice の実行結果を \tabref{twice}、\figref{twice}に示す。
+CPU 実行の際は $2^{24}$ のデータを 64個のTask に分割して並列実行を行っている。
 ここでの ``GPU`` は CPU、 GPU 間のデータの通信時間を含めた時間、 ``GPU(kernel only)`` は kernel のみの実行時間である。
 
 \begin{table}[htbp]
@@ -61,7 +61,7 @@
             GPU & 542.816\\ \hline
             GPU(kernel only)& 0.755\\ \hline
         \end{tabular}
-        \caption{$2^{23}$ のデータに対する Twice}
+        \caption{$2^{24}$ のデータに対する Twice}
         \label{tab:twice}
     \end{center}
 \end{table}
@@ -91,9 +91,44 @@
     \begin{center}
         \includegraphics[scale=0.6]{./fig/bitonicNetwork.pdf}
     \end{center}
-    \caption{要素数8の BtionicNetwork}
+    \caption{要素数8の BitonicNetwork}
     \label{fig:bitonicNetwork}
 \end{figure}
 
 BitonicSort はステージ毎に決まった2点間の要素の入れ替えを並列に実行することによってソートを行う。
+Gears OS ではこのステージ毎に Output Data Gear を書き出し、 次のステージの Code Gear の Input Data Gear として記述することで BitonicSort を実現する。
 
+要素数$2^{24}$ のデータに対する BitonicSort の実行結果を \tabref{bitonicSort}、\figref{bitonicSort}に示す。
+こちらも Twice と同じくCPU 実行の際は $2^{24}$ のデータを 64個のTask に分割して並列実行を行っている。
+つまり生成される Task は 64 * ステージ数 となる。
+
+\begin{table}[htbp]
+    \begin{center}
+        \begin{tabular}{|l||l|} \hline
+            Processor & Time(s) \\ \hline
+            1 CPU &  41.416 \\ \hline
+            2 CPUs & 23.340\\ \hline
+            4 CPUs & 11.952\\ \hline
+            8 CPUs & 6.320\\ \hline
+            16 CPUs & 3.336\\ \hline
+            32 CPUs & 1.872\\ \hline
+            GPU & 5.420\\ \hline
+            GPU(kernel only)& 0.163\\ \hline
+        \end{tabular}
+        \caption{$2^{24}$ のデータに対する Twice}
+        \label{tab:bitonicSort}
+    \end{center}
+\end{table}
+
+\begin{figure}[htbp]
+    \begin{center}
+        \includegraphics[scale=0.6]{./fig/bitonicSort.pdf}
+    \end{center}
+    \caption{$2^{24}$ のデータに対する BitonicSort}
+    \label{fig:bitonicSort}
+\end{figure}
+
+1 CPU と 32 CPU で 約22.12 倍 の速度向上が見られた。
+GPU では通信時間を含めると 8 CPU の約1.16倍となり、 kernel のみの実行では 32 CPU の約11.48倍となった。
+現在の Gears OS の CUDA 実装では、 Output Data Gear を書き出す際に一度 GPU から CPU へ kernel の実行結果の書き出しを行っており、その処理の時間で差が出たと考えられる。
+GPU で実行される Task 同士の依存関係の解決の際はCuDevicePtr などのGPU のメモリへのポインタを渡し、CPU でデータが必要になったときに初めて GPU から CPU へデータの通信を行う実装が必要となる。
Binary file paper/fig/twice.pdf has changed
--- a/paper/fig/twice.svg	Sat Feb 03 20:56:35 2018 +0900
+++ b/paper/fig/twice.svg	Sat Feb 03 22:46:48 2018 +0900
@@ -42,96 +42,91 @@
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+	<path stroke='black'  d='M71.9,54.1 L80.9,54.1 M557.3,54.1 L548.3,54.1  '/>	<g transform="translate(63.6,58.0)" stroke="none" fill="black" font-family="Arial" font-size="12.00"  text-anchor="end">
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+		<text><tspan font-family="Arial" >gpu(kernel only)</tspan></text>
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-		<text><tspan font-family="Arial" >CPUs</tspan></text>
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+	<g transform="translate(314.6,31.0)" stroke="none" fill="black" font-family="Arial" font-size="12.00"  text-anchor="middle">
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--- a/paper/fig/twice.xbb	Sat Feb 03 20:56:35 2018 +0900
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Binary file paper/master_paper.pdf has changed