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From Avery Ching <ach...@apache.org>
Subject Re: workload used to measure Giraph performance number
Date Wed, 09 Oct 2013 21:11:22 GMT
Hi Wei,

For best performance, please be sure to tune the GC settings, use Java 
7, tune the number of cores used for computation, communication, etc. 
and the combiner.

We also have some numbers on our recent Facebook blog post.

https://www.facebook.com/notes/facebook-engineering/scaling-apache-giraph-to-a-trillion-edges/10151617006153920

Avery

On 10/8/13 7:43 PM, Wei Zhang wrote:
>
> Hi Sebastian,
>
> Thanks a lot for the help! Sorry for the late response!
>
> At this point, I would only need a random graph that complies with 
> JsonLongDoubleFloatDoubleVertexInputFormat of Giraph to measure the 
> pagerank example (of giraph) performance.
> I am wondering how to convert the data from Koblenz to the  such a 
> graph ? Is there any pointer of doing this ? (This is the same kind of 
> question that I raised to Alok on SNAP)
>
> Thanks!
>
> Wei
>
> P.S.: I forgot to mention in all my previous emails that I just get 
> started with distributed graph engine, so please forgive if my 
> questions are too naive.
>
> Inactive hide details for Sebastian Schelter ---10/02/2013 12:41:27 
> PM---Another option is to use the Koblenz network collectioSebastian 
> Schelter ---10/02/2013 12:41:27 PM---Another option is to use the 
> Koblenz network collection [1], which offers even more (and larger) dat
>
> From: Sebastian Schelter <ssc@apache.org>
> To: user@giraph.apache.org,
> Date: 10/02/2013 12:41 PM
> Subject: Re: workload used to measure Giraph performance number
>
> ------------------------------------------------------------------------
>
>
>
> Another option is to use the Koblenz network collection [1], which
> offers even more (and larger) datasets than Snap.
>
> Best,
> Sebastian
>
> [1] http://konect.uni-koblenz.de/
>
>
> On 02.10.2013 17:41, Alok Kumbhare wrote:
> > There are a number real (medium sized) graphs at
> > http://snap.stanford.edu/data/index.html which we use for similar
> > benchmarks. It has a good mix of graph types, sparse/dense, ground truth
> > graphs (e.g. social networks that follow power law distribution 
> etc.). So
> > far we have observed that the type of graph has a high impact on the
> > performance of algorithms that Claudio mentioned.
> >
> >
> > On Wed, Oct 2, 2013 at 8:22 AM, Claudio Martella 
> <claudio.martella@gmail.com
> >> wrote:
> >
> >> Hi Wei,
> >>
> >> it depends on what you mean by workload for a batch processing 
> system. I
> >> believe we can split the problem in two: generating a realistic 
> graph, and
> >> using "representative" algorithms.
> >>
> >> To generate graphs we have two options in giraph:
> >>
> >> 1) random graph: you specify the number of vertices and the number of
> >> edges for each vertex, and the edges will connect two random 
> vertices. This
> >> creates a graph with (i) low clustering coefficient, (ii) low 
> average path
> >> length, (ii) a uniform degree distribution
> >>
> >> 2) watts strogatz: you specify the number of vertices, the number of
> >> edges, and a rewire probability beta. giraph will generate a ring 
> lattice
> >> (each vertex is connected to k preceeding vertices and k following
> >> vertices) and rewire some of the edges randomly. This will create a 
> graph
> >> with (i) high clustering coefficient, (ii) low average path length, 
> (iii)
> >> poisson-like degree distribution (depends on beta). This graph will
> >> resemble a small world graph such as a social network, except for the
> >> degree distribution which will not a power law.
> >>
> >> To use representative algorithms you can choose:
> >>
> >> 1) PageRank: it's a ranking algorithm where all the vertices are active
> >> and send messages along the edges at each superstep (hence you'll 
> have O(V)
> >> active vertices and O(E) messages)
> >>
> >> 2) Shortest Paths: starting from a random vertex you'll visit al the
> >> vertices in the graph (some multiple times). This will have an 
> aggregate
> >> O(V) active vertices and O(E) messages, but this is only a lower 
> bound. In
> >> general you'l have different areas of the graph explored at each 
> superstep,
> >> and hence potentially a varying workload across different supersteps.
> >>
> >> 3) Connected Components: this will have something opposite to (2) as it
> >> will have many active vertices at the beginning, where the detection is
> >> refined towards the end.
> >>
> >>
> >> Hope this helps,
> >> Claudio
> >>
> >>
> >> On Wed, Oct 2, 2013 at 4:59 PM, Wei Zhang <weiz@us.ibm.com> wrote:
> >>
> >>> Hi,
> >>>
> >>> I am interested in measuring some performance numbers of Giraph on my
> >>> machine.
> >>>
> >>> I am wondering are there some pointers where I can get some
> >>> (configurable) reasonably large workload to work on ?
> >>>
> >>> Thanks!
> >>>
> >>> Wei
> >>>
> >>
> >>
> >>
> >> --
> >>    Claudio Martella
> >>    claudio.martella@gmail.com
> >>
> >
>
>


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