Hi Avery,
Thanks a lot for the pointer! I haven't quite got to the point where I can
tune the Giraph performance yet (hopefully I can get there soon)
I am still at the early stage of finding/generating the right workload to
measure the performance. I am wondering is there some pointer (in Giraph or
elsewhere) that I can use to generate a random or representative workload
(for pagerank, for now), which complies with
JsonLongDoubleFloatDoubleVertexInputFormat t? Any pointer is greatly
appreciated.  Sorry for being a slow starter.
I have written a small program myself to generate a random graph that has
an average outdegree of 6 connected graph that complies with the JSON
format. For a 2000vertice graph, it takes about 12 seconds to run the
pagerank example provided by Giraph (30 supersteps)
I am only using 1 core of my machine (as I run in the Hadoop's "local" mode
so that I could jdb into the Giraph job to observe how code runs if
needed). Each core (out of 8 in total) of my machine is Intel(R) Core(TM)
i73740QM CPU @ 2.70GHz, there is 16GB memory in total. Assuming the linear
speedup when turning on all the 8 cores, it is about 1.5 seconds to run the
work  not sure if it is a reasonable number or not .
Thanks!
Wei
From: Avery Ching <aching@apache.org>
To: user@giraph.apache.org,
Date: 10/09/2013 05:11 PM
Subject: Re: workload used to measure Giraph performance number
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/facebookengineering/scalingapachegiraphtoatrillionedges/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
PMAnother option is to use the Koblenz network
collectioSebastian Schelter 10/02/2013 12:41:27 PMAnother
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.unikoblenz.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)
>> poissonlike 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
>>
>
