Thanks for the detail on your experiments. I certainly agree that it would be very useful to make some sort of scalability/performance testing framework to evaluate improvements. Definitely would appreciate your help in putting one together. We have a few benchmarks (PageRankBenchmark and RandomMessageBenchmark), but would appreciate any help you would like to provide.

Otherwise, if that doesn't interest you, please have a look at the open JIRAs

and see what's interesting for you. If nothing there interests you, feel freel to discuss here or on giraph-dev or open up a JIRA. =)


On 12/11/11 1:23 PM, Jon Allen wrote:
Hi Avery,

Thanks for the response. I reached out to the graph user mailing list because I 
am quite interested in helping develop / execute standardized scalability 
testing for Giraph, so I'm glad to see that there is interest!

Here's some follow up to some of the points you raised / questions you asked:

Currently, the biggest limitation faced by GoldenOrb is the capacity issue; it 
can't handle more than roughly 100,000 vertices per node. This low maximum 
vertices per node limitation, coupled with instability issues, obviously 
hampered the ability to conduct ideal scalability testing, but even with graphs 
totaling 100,000 to 250,000 vertices a clear power law slope can be found 
before hitting an inevitable communication bottleneck. This can be seen by 
noting that the log-log slopes of the 20k, 50k, and 100k graphs (for SSSP) 
remain fairly constant, and negative, as the number of nodes in the cluster 
grows, unlike the slopes for 5k, 2k, and 1k graphs which demonstrate a 
framework overhead bottleneck, corresponding to the point where the slope 
changes from negative to roughly 0 or positive (which appears to happen at 
around 1k vertices per node).

On to the second issue you brought up…

Graph problems can be notoriously difficult to implement scalability testing 
for precisely for the reasons you brought up. A few things were done to allow 
an apples-to-apples comparison with the Pregel results. First, the single 
source shortest path algorithm used for testing comes directly from the Pregel 
paper. Second, just as in the Pregel tests, binary tree graphs were used to 
ensure that each vertex had the same fixed, low order, outdegree. Last, the 
tests were repeated using non-binary tree graphs (generated by a python script) 
with a non-constant, but low order, average outdegree per vertex (average 10 
edges per vertex, then again with graphs averaging 90 edges per vertex), the 
results of which were seen to be quite close to the binary tree graph data.

As mentioned in passing, the scalability test results allow for a direct 
comparison with the Pregel results, but should also allow for a meaningful 
comparison to your scalability results for Giraph precisely because the edges 
per vertex have been fixed. While this is not ideal (I would prefer a 
standardized set of tests which everybody runs in standardized configurations), 
the proposition that the results can be meaningfully compared is backed up by 
two points; First, the log-log slope of the data you presented is right in line 
with the value reported by Pregel for their SSSP tests, both of which are 
realistic values (and show very good parallelization!), meaning that both 
algorithms display similar properties for configurations in the regime not 
dominated by a framework overhead bottleneck. And second, the GoldenOrb SSSP 
results being compared are also from configurations which have reached a steady 
power law slope over the range of nodes considered, for runs using the same 
algorithm as the Pregel results. These two points, I feel, justify the 
comparisons made (though, again, it would be better to have a standardized set 
of configurations for testing to facilitate comparing results, even between 
algorithms). Since all three sets of scalability tests yield fairly linear 
complexity plots (execution time vs. number of vertices in the graph, slide 29 
of your talk), it also makes sense to compare weak scaling results, a 
proposition supported by the consistency of the observed GoldenOrb weak scaling 
results for SSSP across multiple test configurations.

As for the results found in your October 2011 talk, they are impressive and clearly 
demonstrate an ability to effectively scale to large graph problems (shown by the weak 
scaling slope of ~ 0.01) and to maximize the benefit of throwing additional computational 
resources at a known problem (shown by the strong scaling slope of ~ -0.93), so I'm 
interested to see the results of the improvements that have been made. I'm a big 
proponent of routine scalability testing using a fixed set of configurations as part of 
the software testing process, as the comparable results help to quantify 
"improvement" as the software is developed further and can often help to 
identify unintended side effects of changes / find optimal configurations for various 
regimes of problems, and would like to see Giraph succeed, so let me know if there's any 
open issues which I might be able to dig into (I'm on the dev mailing list as well, 
though haven't posted there).


On Dec 11, 2011, at 1:02 PM, Avery Ching wrote:

Hi Jon, (so as to not clog up their mailing list uninvited)

First of all, thank you for sharing this comparison.  I would like to note a 
few things.  The results I posted in October 2011 were actually a bit old (done 
in June 2011) and do not have several improvements that reduce memory usage 
significantly (i.e. GIRAPH-12 and GIRAPH-91).  The number of vertices loadable 
per worker is highly dependent on the number of edges per worker, the amount of 
available heap memory, number of messages, the balancing of the graph across 
the workers, etc.  In recent tests at Facebook, I have been able to load over 
10 million vertices / worker easily with 20 edges / vertex.  I know that you 
wrote that the maximum per worker was at least 1.6 million vertices for Giraph, 
I just wanted to let folks know that it's in fact much higher.  We'll work on 
continuing to improve that in the future as today's graph problems are in the 
billions of vertices or rather hundreds of billions =).

Also, with respect to scalability, if I'm interpreting these results correctly, 
does it mean that GoldenOrb is currently unable to load more than 250k vertices 
/ cluster as observed by former Ravel developers?  if so, given the small tests 
and overhead per superstep, I wouldn't expect the scalability to be much 
improved by more workers.  Also, the max value and shortest paths algorithms 
are highly data dependent to how many messages are passed around per superstep 
and perhaps not a fair scaling comparison with Giraph's scalability designed 
page rank benchmark test (equal messages per superstep distributed evenly 
across vertices).  Would be nice to see an apples-to-apples comparison if 
someone has the time...=)



On 12/10/11 3:16 PM, Jon Allen wrote:
Since GoldenOrb was released this past summer, a number of people have asked 
questions regarding scalability and performance testing, as well as a 
comparison of these results with those of Giraph ( ), so I went forward with running tests to 
help answer some of these questions.

A full report of the scalability testing results, along with methodology 
details, relevant information regarding testing and analysis, links to data 
points for Pregel and Giraph, scalability testing references, and background 
mathematics, can be found here:

Since this data will also be of interest to the Giraph community (for 
methodology, background references, and analysis reasons), I am cross posting 
to the Giraph user mailing list.

A synopsis of the scalability results for GoldenOrb, and comparison data points 
for Giraph and Google's Pregel framework are provided below.

The setup and execution of GoldenOrb scalability tests were conducted by three 
former Ravel ( ) developers, including myself, with 
extensive knowledge of the GoldenOrb code base and optimal system 
configurations, ensuring the most optimal settings were used for scalability 



Pregel (at least): 166,666,667 vertices per node.

Giraph (at least): 1,666,667 vertices per worker.

GoldenOrb: ~ 100,000 vertices per node, 33,333 vertices per worker.

Note: Optimal parallelization corresponds to the minimum value -1.0. Deviation 
from the minimum possible value of -1.0 corresponds to non-optimal 

Pregel: -0.924 (1 billion total vertices)

Giraph: -0.934 (250 Million total vertices)

GoldenOrb: -0.031 Average, -0.631 Best (100000 total vertices), 0.020 Worst 
(1000 total vertices)

Note: Optimal weak scalability corresponds to the value 0.0. Deviation from the 
optimal value of 0.0, corresponds to non-optimal usage of computational 
resources as managed by the framework.

Pregel: No Data Available

Giraph: 0.01 (1,666,667 vertices per worker)

GoldenOrb: 0.37 Average, 0.23 Best (500 vertices per node), 0.48 Worst (12500 
vertices per node)

I hope this answers some of the many questions which have been posted regarding 
scalability and performance. Be sure to check out the full scalability testing 
report at  Please let me 
know if you have any questions.


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