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From Amani Alonazi <>
Subject Re: Suggestions on problem sizes for giraph performance benchmarking
Date Mon, 09 Jul 2012 12:57:09 GMT
Actually, I had the same problem of running out of memory with Giraph when
trying to implement strongly connected components algorithm on Giraph. My
input graph is 1 million nodes and 7 million edges.

I'm using cluster of 21 computers.

On Mon, Jul 9, 2012 at 3:44 PM, Benjamin Heitmann <> wrote:

> Hello Stephen,
> sorry for the very late reply.
> On 28 Jun 2012, at 02:50, Fleischman, Stephen (ISS SCI - Plano TX) wrote:
>  Hello Avery and all:****
> I have a cluster of 10  two-processor/48 GB RAM servers, upon which we are
> conducting Hadoop performance characterization tests.  I plan to use the
> Giraph pagerank and simple shortest path example tests as part of this
> exercise and would appreciate guidance on problem sizes for both tests.
> I’m looking at paring down an obfuscated Twitter dataset and it would save
> a lot of time if someone has some knowledge on roughly how the time and
> memory scales with number of nodes in a graph.
> ****
> I can provide some suggestions for the kind of algorithm and data which
> does currently surpass the scalability of giraph.
> While the limits to my knowledge of Giraph and Hadoop are probably also to
> blame for this, please see the recent discussions on this list,
> and on JIRA for other indications that the scalability of Giraph needs
> improvement:
> * post  by Yuanyuan Tian in the thread "wierd communication errors" on
> * GIRAPH-234 about GC overhead
> If you want to stretch the limits of Giraph, then you need to try an
> algorithm which is conceptually different from PageRank, and you need a big
> data set.
> If you use an algorithm which has complex application logic (maybe even
> domain specific logic), which needs to be embedded in the algorithm,
> then the nodes need to have a lot of state. In addition, such algorithms
> probably send around a lot of messages, and each of the messages might have
> a payload
> which is more complex then one floating point number. In addition, it
> helps to have a graph format, which requires strings on the edges and
> vertices.
> The strings are required for the domain specific business logic which the
> graph algorithm needs to follow.
> Finally, imagine a data set which has a big loading time, and where one
> run of the algorithm only provides results for one user.
> The standard Hadoop paradigm is to throw away the graph after loading it.
> So if you have 100s or 1000s of users, then you need a way to execute the
> algorithm multiple times in parallel.
> Again this will add a lot of state, as each of the vertices will need to
> hold one state object for each user who has visited the vertex.
> In my specific case, I had the following data and algorithm:
> Data:
> * an RDF graph with 10 million vertices and 40 million edges
> I used my own import code to map the RDF graph to a undirected graph with
> a limit of one edge between any two nodes (so it was not a multi-graph)
> * each vertex and each edge uses a string as an identity to represent a
> URI in the RDF graph (required for the business logic in the algorithm)
> Algorithm:
> * spreading activation.
> You can think of it as depth first search guided by domain specific logic.
> A short introduction here:
> The wikipedia article only mentions using spreading activation on weighted
> graphs, however I used it on graphs which have additional types on the
> edges.
> The whole area of using the semantics of the edges to guide the algorithm
> is an active research topic, so thats why I can't point you to a good
> article on that.
> * parallel execution:
> I need to run the algorithm once for every user in the system, however
> loading the data set takes around 15 minutes alone.
> So each node has an array of states, one for each user for which the
> algorithm has visited a node.
> I experimented with user numbers between 30 and 1000, anything more did
> not work for concurrent execution of the algorithm.
> Infrastructure:
> * a single server with 24 Intel Xeon 2.4 GHz cpus and 96 GB of RAM
> * Hadoop 1.0, pseudo-distributed setup
> * between 10 and 20 Giraph workers
> A few weeks ago I stopped work on my Giraph based implementation, as
> Giraph ran out of memory almost immediately after loading and initialising
> the data.
> I made sure that the Giraph workers do not run out of memory, so it was
> probably due to IPC and messaging.
> The general discussion on the Giraph mailing list strongly indicates that
> I did hit the current IPC scalability limits.
> Currently I am working on a non-Hadoop version of the algorithm which is
> not as scalable but which is fast for *one* user. ( less then a second per
> user, but single threaded).
> In addition, this new version allows me to better integrate with an
> existing ecosystem of technologies (Semantic Web technologies) to which
> Hadoop and Giraph is currently completely disconnected.
> However, I will probably revisit Giraph at some time on the future.
> If you want to look at the code or the data or any other asset which I
> have, then I will gladly share that with you.
> I would really like Giraph to reach the maturity required for this kind of
> algorithm.
> However, I have the feeling that the current development focus is on
> clear-cut numerical algorithms such as pagerank.

Amani AlOnazi
MSc Computer Science
King Abdullah University of Science and Technology
Kingdom of Saudi Arabia | +966 (0) 555 191 795


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