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).
Thanks,
Jon
On Dec 11, 2011, at 1:02 PM, Avery Ching wrote:
Hi Jon,
-golden...@googlegroups.com (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...=)
Thanks,
Avery
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 (
http://incubator.apache.org/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:
http://wwwrel.ph.utexas.edu/Members/jon/golden_orb/
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 (http://www.raveldata.com ) developers, including myself,
with extensive knowledge of the GoldenOrb code base and optimal system
configurations, ensuring the most optimal settings were used for scalability
testing.
RESULTS SUMMARY:
MAX CAPACITY:
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.
STRONG SCALING (SSSP):
Note: Optimal parallelization corresponds to the minimum value -1.0.
Deviation from the minimum possible value of -1.0 corresponds to non-optimal
parallelization.
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)
WEAK SCALING (SSSP):
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
http://wwwrel.ph.utexas.edu/Members/jon/golden_orb/ Please let me know if
you have any questions.
Thanks,
Jon
