Added more documentation

Author: Yucheng Low

Doxyfile

@@ -31,7 +31,7 @@ PROJECT_NAME           = "GraphLab: Distributed Graph-Parallel API"
 # This could be handy for archiving the generated documentation or 
 # if some version control system is used.
 
-PROJECT_NUMBER         = 2.1
+PROJECT_NUMBER         = 2.2
 
 # The OUTPUT_DIRECTORY tag is used to specify the (relative or absolute) 
 # base path where the generated documentation will be put. 

Doxyfile_internal

@@ -31,7 +31,7 @@ PROJECT_NAME           = "GraphLab: Distributed Graph-Parallel API"
 # This could be handy for archiving the generated documentation or 
 # if some version control system is used.
 
-PROJECT_NUMBER         = 2.0
+PROJECT_NUMBER         = 2.2
 
 # The OUTPUT_DIRECTORY tag is used to specify the (relative or absolute) 
 # base path where the generated documentation will be put. 

demoapps/pagerank/CMakeLists.txt

@@ -3,6 +3,6 @@ project(GraphLab)
 
 add_graphlab_executable(simple_pagerank simple_pagerank.cpp)
 
-add_graphlab_executable(warp_pagerank warp_pagerank.cpp)
+add_graphlab_executable(warp_parfor_pagerank warp_parfor_pagerank.cpp)
 
 add_graphlab_executable(warp_engine_pagerank warp_engine_pagerank.cpp)

demoapps/pagerank/warp_engine_pagerank.cpp

@@ -30,7 +30,7 @@ using namespace graphlab;
 
 // The graph type is determined by the vertex and edge data types
 typedef distributed_graph<float , graphlab::empty> graph_type;
-typedef warp_engine<graph_type> warp_engine_type;
+typedef warp::warp_engine<graph_type> warp_engine_type;
 
 /*
  * A simple function used by graph.transform_vertices(init_vertex);

demoapps/pagerank/warp_parfor_pagerank.cpp

@@ -43,22 +43,13 @@ void init_vertex(graph_type::vertex_type& vertex) { vertex.data() = 1; }
 float pagerank_map(graph_type::edge_type edge, graph_type::vertex_type other) {
   return other.data() / other.num_out_edges();
 }
-void transform_edge(graph_type::edge_type edge, graph_type::vertex_type other) {
-  edge.data() = other.data();
-}
 
 void pagerank(graph_type::vertex_type vertex) {
   vertex.data() = 0.15 + 0.85 * warp::map_reduce_neighborhood(vertex,
                                                               IN_EDGES,
                                                               pagerank_map);
-  warp::transform_neighborhood(vertex, OUT_EDGES, transform_edge);
 }
 
-
-
-
-
-
 /*
  * We want to save the final graph so we define a write which will be
  * used in graph.save("path/prefix", pagerank_writer()) to save the graph.

src/graphlab/docs/overview.dox

@@ -14,33 +14,35 @@
   The GraphLab project started in 2009 to develop a new parallel
   computation abstraction tailored to machine learning. GraphLab 1.0
   represents our first shared memoy design which, through the addition
-  of several matrix factorization toolkits contributed by our post-doc
-  Danny Bickson, started to grow a community of users.
+  of several matrix factorization toolkits, started to grow a community of users.
 
   In the last couple of years, we have focused our development effort
   on the distributed environment. Unfortunately, it took nearly a year
   to figure out that distributing the GraphLab 1 abstraction was
   excessively complicated and is unable to scale up to power-law
   graphs commonly seen in the real world.
 
-  GraphLab 2.1 represents the latest evolution of the GraphLab
-  abstraction and is a complete redesign of the GraphLab 1 framework
+  In GraphLab 2.1, we completely redesign of the GraphLab 1 framework
   for the distributed environment. The implementation is distributed
   by design and a "shared-memory" execution is essentially running a
-  distributed system on a cluster of 1 machine. Not all toolkits from
-  GraphLab 1 have been ported over yet; some complex algorithms may
-  take some time.
-
-  There are two starting points where one may begin using GraphLab.
-  \li \ref toolkits "Toolkits" You can lookup the toolkit documentation here if you have a
-  computation task which is already implemented by one of our toolkits.
-  \li \ref using_graphlab "GraphLab C++ Tutorial" If you have a computation task which is
-  not implemented by our toolkits, you could try implementing yourself! For now
-  a certain degree of C++ knowledge is required. However, we are trying to
-  provide an interface to other languages such as Javascript and Python.
-  Contact the developers (here) if you want to beta-test these interfaces, or
-  come back in a couple of months when we may have something stable.
-
+  distributed system on a cluster of 1 machine. 
+  
+  And in this new release of GraphLab 2.2, we introduce the new \ref warp
+  which through the use of fine-grained user-mode threading, introduces a new
+  API which brings about a major increase in useability, and will allow us to 
+  provide new capabilities more easily in the future.
+
+  There are two starting points where one may begin using GraphLab.  \li \ref
+  toolkits "Toolkits" You can lookup the toolkit documentation here if you have
+  a computation task which is already implemented by one of our toolkits.  \li
+  \ref using_graphlab "GraphLab C++ Tutorial" If you have a computation task
+  which is not implemented by our toolkits, you could try implementing
+  yourself! For now a certain degree of C++ knowledge is required. 
+
+  The new GraphLab 2.2 \ref warp is available for experimentation. A
+  \ref using_warp tutorial is provided, and we are are looking for feedback 
+  to continue extending and improving the Warp system. Performance tuning is 
+  also underway.
 
   Software Stack
   =============