Extending the API with a master.compute() function.
Issue Type: New Feature
Components: bsp, examples, graph
Reporter: Semih Salihoglu
First of all, sorry for the long explanation to this feature.
I want to expand the API of Giraph with a new function called master.compute(),
that would get called at the master before each superstep and I will try to
explain the purpose that it would serve with an example. Let's say we want to
implement the following simplified version of the k-means clustering algorithm.
* Input G(V, E), k, numEdgesThreshold, maxIterations
* int numEdgesCrossingClusters = Integer.MAX_INT;
* int iterationNo = 0;
* while ((numEdgesCrossingCluster> numEdgesThreshold)&& iterationNo<
* int clusterCenters = pickKClusterCenters(k, G);
* findClusterCenters(G, clusterCenters);
* numEdgesCrossingClusters = countNumEdgesCrossingClusters();
The algorithm goes through the following steps in iterations:
1) Pick k random initial cluster centers
2) Assign each vertex to the cluster center that it's closest to (in Giraph,
this can be implemented in message passing similar to how ShortestPaths is
3) Count the nuimber of edges crossing clusters
4) Go back to step 1, if there are a lot of edges crossing clusters and we
haven't exceeded maximum number of iterations yet.
In an algorithm like this, step 2 and 3 are where most of the work happens and
both parts have very neat message-passing implementations. I'll try to give an
overview without going into the details. Let's say we define a Vertex in Giraph
to hold a custom Writable object that holds 2 integer values and sends a
message with upto 2 integer values.
Step 2 is very similar to ShortestPaths algorithm and has two stages: In the
first stage, each vertex checks to see whether or not it's one of the cluster
centers. If so, it assigns itself the value (id, 0), otherwise it assigns
itself (Null, Null). In the 2nd stage, the vertices assign themselves to the
minimum distance cluster center by looking at their neighbors (cluster centers,
distance) values (received as 2 integer messages) and their current values, and
changing their values if they find a lower distance cluster center. This
happens in x number of supersteps until every vertex converges.
Step 3, counting the number of edges crossing clusters, is also very easy to implement in Giraph.
Once each vertex has a cluster center, the number of edges crossing clusters can be counted by an
aggregator, let's say called "num-edges-crossing". It would again have two stages: First
stage, every vertex just sends its cluster id to all its neighbors. Second stage, every vertex
looks at their neighbors' cluster ids in the messages, and for each cluster id that is not equal to
its own cluster id, it increments "num-edges-crossing" by 1.
The other 2 steps, step 1 and 4, are very simple sequential computations. Step 1 just
picks k random vertex ids and puts it into an aggregator. Step 4 just compares
"num-edges-crossing" by a threshold and also checks whether or not the
algorithm has exceeded maxIterations (not supersteps but iterations of going through
Steps 1-4). With the current API, it's not clear where to do these computations. There is
a per worker function preSuperstep() that can be implemented, but if we decide to pick a
special worker, let's say worker 1, to pick the k vertices then we'd waste an entire
superstep where only worker 1 would do work, (by picking k vertices in preSuperstep()
and put them into an aggregator), and all other workers would be idle. Trying to do this
in worker 1 in postSuperstep() would not work either because, worker 1 needs to know that
all the vertices have converged to understand that it's time to pick k vertices or it's
time do check in step 4, which would only be available to it in the beginning of the next
A master.compute() extension would run at the master and before the superstep
and would modify the aggregator that would keep the k vertices before the
aggregators are broadcast to the workers, which are all very short sequential
computations, so they would not waste resources the way a preSuperstep() or
postSuperstep() approach would do. It would also enable running new algorithms
like kmeans that are composed of very vertex-centric computations glued
together by small sequential ones. It would basically boost Giraph with
sequential computation in a non-wasteful way.
I am a phd student at Stanford and I have been working on my own BSP/Pregel
implementation since last year. It's called GPS. I haven't distributed it,
mainly because in September I learned about Giraph and I decided to slow down
on working on it :). We have basically been using GPS as our own research
platform. The source code for GPS is here if any one is interested
(https://subversion.assembla.com/svn/phd-projects/gps/trunk/). We have the
master.compute() feature in GPS, and here's an example of KMeans implementation
in GPS with master.compute():
(Aggregators are called GlobalObjects in GPS). There is another example
which I'll skip explaining because it's very detailed and would make the
similar points that I am trying to make with k-means. Master.compute() in
general would make it possible to glue together any graph algorithm that is
composed of multiple stages with different message types and computations that
is conducive to run with vertex.compute(). There are many examples of such
algorithms: recursive partitioning, triangle counting, even much simpler things
like finding shortests paths for 100 vertices in pieces (first to 5 vertices,
then to another 5, then to another 5, etc..), which would be good because
trying to find shortests paths to 100 vertices require a very large messages
(would need to store 100 integers per message)).
If the Giraph team approves, I would like to take a similar approach in
implementing this feature in Giraph as I've done in GPS. Overall:
Add a Master.java to org.apache.giraph.graph, that is default Master, with a
compute function that by default aggregates all aggregators and does the check
of whether or not the computation has ended (by comparining numVertices with
numFinishedVertices). This would be a refactoring of
org.apache.giraph.graph.BspServiceMaster class (as far as I can see).
Extend GiraphJob to have a setMaster() method to set a master class (by default
it would be the default master above)
The rest would be sending the custom master class to probably all workers but
only the master would instantiate it with reflection. I need to learn more on
how to do these, I am not familiar with that part of the Giraph code base yet.
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