updated kmeans doc

tsung-wei-huang · tsung-wei-huang · commit 03804fe14eb1 · 2020-04-17T23:46:14.000-06:00
diff --git a/docs/Examples.html b/docs/Examples.html
@@ -104,7 +104,7 @@
 <div class="contents">
 <div class="textblock"><p>This page contains several examples to learn Cpp-Taskflow in real use case.</p>
 <p><a class="el" href="matrix_multiplication.html">Matrix Multiplication</a> <br />
-<a class="el" href="kmeans.html">k-means clustering</a> <br />
+<a class="el" href="kmeans.html">k-means Clustering</a> <br />
 </p>
 </div></div><!-- contents -->
 </div><!-- doc-content -->
diff --git a/docs/Examples.js b/docs/Examples.js
@@ -6,7 +6,7 @@ var Examples =
       [ "GPU-based Acceleration", "matrix_multiplication.html#GPUAcceleratedMatrixMultiplication", null ],
       [ "Benchmarking", "matrix_multiplication.html#MatrixMultiplicationBenchmarking", null ]
     ] ],
-    [ "k-means clustering", "kmeans.html", [
+    [ "k-means Clustering", "kmeans.html", [
       [ "Problem Formulation", "kmeans.html#KMeansProblemFormulation", null ],
       [ "Parallel k-means using CPUs", "kmeans.html#ParallelKMeansUsingCPUs", null ],
       [ "Parallel k-means using GPUs", "kmeans.html#ParallelKMeansUsingGPUs", null ],
diff --git a/docs/kmeans.html b/docs/kmeans.html
diff --git a/docs/pages.html b/docs/pages.html
@@ -122,7 +122,7 @@
 <tr id="row_1_6_"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><a class="el" href="chapter6.html" target="_self">C6: CPU-GPU Tasking</a></td><td class="desc"></td></tr>
 <tr id="row_2_" class="even"><td class="entry"><span style="width:0px;display:inline-block;">&#160;</span><span id="arr_2_" class="arrow" onclick="toggleFolder('2_')">&#9660;</span><a class="el" href="Examples.html" target="_self">Learning from Examples</a></td><td class="desc"></td></tr>
 <tr id="row_2_0_"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><a class="el" href="matrix_multiplication.html" target="_self">Matrix Multiplication</a></td><td class="desc"></td></tr>
-<tr id="row_2_1_" class="even"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><a class="el" href="kmeans.html" target="_self">k-means clustering</a></td><td class="desc"></td></tr>
+<tr id="row_2_1_" class="even"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><a class="el" href="kmeans.html" target="_self">k-means Clustering</a></td><td class="desc"></td></tr>
 <tr id="row_3_"><td class="entry"><span style="width:16px;display:inline-block;">&#160;</span><a class="el" href="FAQ.html" target="_self">Frequently Asked Questions</a></td><td class="desc"></td></tr>
 <tr id="row_4_" class="even"><td class="entry"><span style="width:16px;display:inline-block;">&#160;</span><a class="el" href="Reference.html" target="_self">Reference</a></td><td class="desc"></td></tr>
 </table>
diff --git a/docs/search/all_a.js b/docs/search/all_a.js
diff --git a/docs/search/pages_2.js b/docs/search/pages_2.js
@@ -1,4 +1,4 @@
 var searchData=
 [
-  ['k_2dmeans_20clustering',['k-means clustering',['../kmeans.html',1,'Examples']]]
+  ['k_2dmeans_20clustering',['k-means Clustering',['../kmeans.html',1,'Examples']]]
 ];
diff --git a/doxygen/examples/kmeans.dox b/doxygen/examples/kmeans.dox
@@ -1,6 +1,6 @@
 namespace tf {
 
-/** @page kmeans k-means clustering
+/** @page kmeans k-means Clustering
 
 We study a fundamental clustering problem in unsupervised learning, <em>k-means clustering</em>. 
 We will begin by discussing the problem formulation and then learn how to 
@@ -293,7 +293,7 @@ __global__ void compute_new_means(float* mx, float* my, float* sx, float* sy, in
 @endcode
 
 When we recompute the cluster centroids to be the mean of all points assigned to a particular centroid,
-multiple GPU threads may access the sum arrays, @c sx and @c sy, and the count array, @c.
+multiple GPU threads may access the sum arrays, @c sx and @c sy, and the count array, @c c.
 To avoid data race, we use a simple @c atomicAdd method.
 Based on the two kernels, the entire code of CPU-GPU collaborative tasking is described as follows:
 
@@ -402,7 +402,10 @@ void kmeans_gpu(int N, int K, int M, cconst std::vector<float>& px,  const std::
 }
 @endcode
 
-The first dump before executing the taskflow produces the following diagram:
+The first dump before executing the taskflow produces the following diagram.
+The condition tasks introduces a cycle between itself and @c update_means.
+Each time it goes back to @c update_means, the %cudaFlow is reconstructed with captured 
+parameters in the closure and offloaded to the GPU.
 
 @image html images/kmeans_3.svg width=80%
 

Original file line number	Diff line number	Diff line change
`@@ -1,4 +1,4 @@`
`1`	`1`	`var searchData=`
`2`	`2`	`[`
`3`		`- ['k_2dmeans_20clustering',['k-means clustering',['../kmeans.html',1,'Examples']]]`
	`3`	`+ ['k_2dmeans_20clustering',['k-means Clustering',['../kmeans.html',1,'Examples']]]`
`4`	`4`	`];`