From: Raphael Couturier <raphael.couturier@univ-fcomte.fr>
Date: Wed, 17 Oct 2012 13:23:25 +0000 (+0200)
Subject: new
X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/book_gpu.git/commitdiff_plain/1a01129f963257afbf1ca4effbb4e6e1f378cefa?ds=inline;hp=-c

new
---

1a01129f963257afbf1ca4effbb4e6e1f378cefa
diff --git a/BookGPU/Chapters/chapter11/ch11.tex b/BookGPU/Chapters/chapter11/ch11.tex
index 1bf638e..7e319fd 100644
--- a/BookGPU/Chapters/chapter11/ch11.tex
+++ b/BookGPU/Chapters/chapter11/ch11.tex
@@ -395,94 +395,98 @@ u_i=\max_{k\leq i} \min_{l \geq i} \hat y(k,l),
 $$
 with $\hat y(k,l)$ being the unrestricted maximum likelihood estimator of $y_k\ldots,y_l$. For quadratic cost function $\hat y(k,l)$ is the mean, as in PAV and MLS algorithms, for the absolute deviations it becomes the median, and for other cost functions an M-estimator of location. The MLS algorithm can be applied to such isotone regression problems with very little modification, while linear in time algorithm may not be available. Our parallel MLS algorithm will be valuable in such cases.
 
-%\renewcommand{\baselinestretch}{1}
-\begin{table}[!h]
-\begin{center}
-\caption{The average CPU time (sec) of the serial PAVA, MLS and parallel MLS algorithms.  } \label{ch11:table1}
-\begin{tabular}{|r|r|r|r|}
-
-Data  & PAVA & MLS & GPU MLS \\ \hline
-
-monotone increasing $f$ & & & \\
-$n=5\times 10^4$ &0.01&5& 0.092\\
-$n=10^5$ &0.03&40& 0.35\\
-$n=5\times 10^5$ &0.4&1001&8.6 \\
-$n=10^6$ &0.8& 5000& 38 \\
-$n=2 \times 10^6$ & 1.6 &-- &152 \\
-$n=10 \times 10^6$ & 2 &-- & 3500 \\
-$n=20 \times 10^6$ & 4.5&-- & --\\
-$n=50 \times 10^6$ & 12 &-- & --\\
-\hline
-
-constant or decreasing $f$ & & & \\
-$n=10^6$ &0.2&0.1& 38\\
-$n=10 \times 10^6$ &1.9& 1.9& 3500 \\
-$n=20 \times 10^6$ &3.5& 4.0&-- \\
-$n=50 \times 10^6$ &11& 11& -- \\
-
-\end{tabular}
-\end{center}
-\end{table}
-%\renewcommand{\baselinestretch}{2}
-
-
-\begin{figure}[!hp]
- \begin{alltt}
-\begin{center}
-\begin{minipage}{13cm}\small
-template<typename Tx>	 
-__device__ Tx Aver(Tx z,int i,int j, Tx *z) \{return (z-z[j+1])/(j-i+1);\}
-
-template<typename Tx>
-__global__ void monotonizekernel(Tx *y, Tx *z, Tx *u, int *key, int n)  
-\{ int i = threadIdx.x + blockIdx.x * blockDim.x;
-   if(i<n) \{
-      int smallestJ = i;
-      Tx curP, smallestP, curz=z[i];
-      smallestP=Aver(curz,i,i,z);
-      for(int j = i+1; j < n; j++) \{
-          curP=Aver(curz,i,j,z);
-          if(smallestP>curP) \{
-               smallestJ = j;
-               smallestP = curP;
-          \}	
-      \}
-      curP=y[i];
-      if(curP > smallestP) t=smallestP;
-                      else smallestJ=i;
-      key[i]=smallestJ;
-      u[i]=t;
-   \}
-\}
-
-template< typename Tx >
-void MonotonizeData(Tx *y, int n, Tx *u) \{
-    thrust::less_equal<int> binary_pred;
-    thrust::maximum<Tx>     binary_op2;
-    thrust::device_vector<Tx> z_d(n+1);
-    thrust::device_vector<int> keys_d(n);	
-    thrust::device_ptr<Tx> y_d(y), u_d(u);
-    thrust::fill(u_d, u_d+n, -1e100);
-    thrust::fill(keys_d.begin(), keys_d.end(), 0);
-
-    thrust::reverse_iterator< typename thrust::device_vector<Tx>::iterator >
-            y_reverse_b(y_d+n), y_reverse_end(y_d), z_reverse_b(z_d.end());
+
+
+%% %\renewcommand{\baselinestretch}{1}
+%% \begin{table}[!h]
+%% \begin{center}
+%% \caption{The average CPU time (sec) of the serial PAVA, MLS and parallel MLS algorithms.  } \label{ch11:table1}
+%% \begin{tabular}{|r|r|r|r|}
+
+%% Data  & PAVA & MLS & GPU MLS \\ \hline
+
+%% monotone increasing $f$ & & & \\
+%% $n=5\times 10^4$ &0.01&5& 0.092\\
+%% $n=10^5$ &0.03&40& 0.35\\
+%% $n=5\times 10^5$ &0.4&1001&8.6 \\
+%% $n=10^6$ &0.8& 5000& 38 \\
+%% $n=2 \times 10^6$ & 1.6 &-- &152 \\
+%% $n=10 \times 10^6$ & 2 &-- & 3500 \\
+%% $n=20 \times 10^6$ & 4.5&-- & --\\
+%% $n=50 \times 10^6$ & 12 &-- & --\\
+%% \hline
+
+%% constant or decreasing $f$ & & & \\
+%% $n=10^6$ &0.2&0.1& 38\\
+%% $n=10 \times 10^6$ &1.9& 1.9& 3500 \\
+%% $n=20 \times 10^6$ &3.5& 4.0&-- \\
+%% $n=50 \times 10^6$ &11& 11& -- \\
+
+%% \end{tabular}
+%% \end{center}
+%% \end{table}
+%% %\renewcommand{\baselinestretch}{2}
+
+
+%% \begin{figure}[!hp]
+%%  \begin{alltt}
+%% \begin{center}
+%% \begin{minipage}{13cm}\small
+%% template<typename Tx>	 
+%% __device__ Tx Aver(Tx z,int i,int j, Tx *z) \{return (z-z[j+1])/(j-i+1);\}
+
+%% template<typename Tx>
+%% __global__ void monotonizekernel(Tx *y, Tx *z, Tx *u, int *key, int n)  
+%% \{ int i = threadIdx.x + blockIdx.x * blockDim.x;
+%%    if(i<n) \{
+%%       int smallestJ = i;
+%%       Tx curP, smallestP, curz=z[i];
+%%       smallestP=Aver(curz,i,i,z);
+%%       for(int j = i+1; j < n; j++) \{
+%%           curP=Aver(curz,i,j,z);
+%%           if(smallestP>curP) \{
+%%                smallestJ = j;
+%%                smallestP = curP;
+%%           \}	
+%%       \}
+%%       curP=y[i];
+%%       if(curP > smallestP) t=smallestP;
+%%                       else smallestJ=i;
+%%       key[i]=smallestJ;
+%%       u[i]=t;
+%%    \}
+%% \}
+
+%% template< typename Tx >
+%% void MonotonizeData(Tx *y, int n, Tx *u) \{
+%%     thrust::less_equal<int> binary_pred;
+%%     thrust::maximum<Tx>     binary_op2;
+%%     thrust::device_vector<Tx> z_d(n+1);
+%%     thrust::device_vector<int> keys_d(n);	
+%%     thrust::device_ptr<Tx> y_d(y), u_d(u);
+%%     thrust::fill(u_d, u_d+n, -1e100);
+%%     thrust::fill(keys_d.begin(), keys_d.end(), 0);
+
+%%     thrust::reverse_iterator< typename thrust::device_vector<Tx>::iterator >
+%%             y_reverse_b(y_d+n), y_reverse_end(y_d), z_reverse_b(z_d.end());
 	
-    thrust::inclusive_scan(y_reverse_b, y_reverse_end, z_reverse_b+1);
-
-    monotonizekernel<<<grid, block>>>(y, thrust::raw_pointer_cast(&z_d[0]), 
-                               u, thrust::raw_pointer_cast(&keys_d[0]), n );
-
-    thrust::sort(keys_d.begin(), keys_d.end());
-    thrust::inclusive_scan_by_key(keys_d.begin(), keys_d.end(), 
-                                  u_d, u_d, binary_pred, binary_op2);
-\}
-\end{minipage}
-\end{center}
-\end{alltt}
-\caption{Fragments of implementation of a parallel version of the MLS algorithm using Thrust library.}
-\label{ch11:algMLS}
-\end{figure}
+%%     thrust::inclusive_scan(y_reverse_b, y_reverse_end, z_reverse_b+1);
+
+%%     monotonizekernel<<<grid, block>>>(y, thrust::raw_pointer_cast(&z_d[0]), 
+%%                                u, thrust::raw_pointer_cast(&keys_d[0]), n );
+
+%%     thrust::sort(keys_d.begin(), keys_d.end());
+%%     thrust::inclusive_scan_by_key(keys_d.begin(), keys_d.end(), 
+%%                                   u_d, u_d, binary_pred, binary_op2);
+%% \}
+%% \end{minipage}
+%% \end{center}
+%% \end{alltt}
+%% \caption{Fragments of implementation of a parallel version of the MLS algorithm using Thrust library.}
+%% \label{ch11:algMLS}
+%% \end{figure}
+
+\lstinputlisting[label=ch11:algMLS,caption=Fragments of implementation of a parallel version of the MLS algorithm using Thrust library.]{Chapters/chapter11/code4.cu}
 
 \section{Conclusion} \label{ch11:conc}
 
diff --git a/BookGPU/Chapters/chapter11/code1.cu b/BookGPU/Chapters/chapter11/code1.cu
index 3b77cc2..4f2316e 100644
--- a/BookGPU/Chapters/chapter11/code1.cu
+++ b/BookGPU/Chapters/chapter11/code1.cu
@@ -1,7 +1,6 @@
 
 template<typename Tx, typename Ty>
-__global__ void CalculateCoefficientsKnots( Tx *u, Ty *v, double *b, double *c,
-  double *t, double *alpha, double *beta, double *gamma, int N )
+__global__ void CalculateCoefficientsKnots( Tx *u, Ty *v, double *b, double *c, double *t, double *alpha, double *beta, double *gamma, int N )
 {
   int tid = threadIdx.x + blockIdx.x * blockDim.x;
   int s = tid*2;
diff --git a/BookGPU/Chapters/chapter11/code3.cu b/BookGPU/Chapters/chapter11/code3.cu
index de460f6..fa658eb 100644
--- a/BookGPU/Chapters/chapter11/code3.cu
+++ b/BookGPU/Chapters/chapter11/code3.cu
@@ -31,10 +31,9 @@ __global__ void d_MonSplineValue(Tx* z, int K, double* t, double * alpha, double
 }
 
 template<typename Tx, typename Ty>	
-void MonotoneSplineValue(Tx *z, int K, double* t,
-   double * alpha, double * beta, double * gamma, int T, Ty* result)
+void MonotoneSplineValue(Tx *z, int K, double* t, double * alpha, double * beta, double * gamma, int T, Ty* result)
 {	
   int blocks,threads=256;
   blocks=(K-1)/threads+1;
   d_MonSplineValue<<<blocks,threads>>>(z,K,t,alpha,beta,gamma,T,result);
-}
\ No newline at end of file
+}
diff --git a/BookGPU/Chapters/chapter11/code4.cu b/BookGPU/Chapters/chapter11/code4.cu
new file mode 100644
index 0000000..6eb646d
--- /dev/null
+++ b/BookGPU/Chapters/chapter11/code4.cu
@@ -0,0 +1,46 @@
+template<typename Tx>	 
+__device__ Tx Aver(Tx z,int i,int j, Tx *z) {return (z-z[j+1])/(j-i+1);}
+
+template<typename Tx>
+__global__ void monotonizekernel(Tx *y, Tx *z, Tx *u, int *key, int n)  
+{ int i = threadIdx.x + blockIdx.x * blockDim.x;
+   if(i<n) {
+      int smallestJ = i;
+      Tx curP, smallestP, curz=z[i];
+      smallestP=Aver(curz,i,i,z);
+      for(int j = i+1; j < n; j++) {
+          curP=Aver(curz,i,j,z);
+          if(smallestP>curP) {
+               smallestJ = j;
+               smallestP = curP;
+          }	
+      }
+      curP=y[i];
+      if(curP > smallestP) 
+        t=smallestP;
+      else 
+				smallestJ=i;
+      key[i]=smallestJ;
+      u[i]=t;
+   }
+}
+
+template< typename Tx >
+void MonotonizeData(Tx *y, int n, Tx *u) {
+    thrust::less_equal<int> binary_pred;
+    thrust::maximum<Tx>     binary_op2;
+    thrust::device_vector<Tx> z_d(n+1);
+    thrust::device_vector<int> keys_d(n);	
+    thrust::device_ptr<Tx> y_d(y), u_d(u);
+    thrust::fill(u_d, u_d+n, -1e100);
+    thrust::fill(keys_d.begin(), keys_d.end(), 0);
+
+    thrust::reverse_iterator< typename thrust::device_vector<Tx>::iterator >  y_reverse_b(y_d+n), y_reverse_end(y_d), z_reverse_b(z_d.end());
+	
+    thrust::inclusive_scan(y_reverse_b, y_reverse_end, z_reverse_b+1);
+
+    monotonizekernel<<<grid, block>>>(y, thrust::raw_pointer_cast(&z_d[0]), u, thrust::raw_pointer_cast(&keys_d[0]), n );
+
+    thrust::sort(keys_d.begin(), keys_d.end());
+    thrust::inclusive_scan_by_key(keys_d.begin(), keys_d.end(), u_d, u_d, binary_pred, binary_op2);
+}