Computer Science Colloquium
Time+Place : Tuesday 28/12/2010 14:30 room 337-8 Taub  Bld.
Speaker    : Danny Bickson
Affiliation: Carnegie Mellon University
Host       : Johann Makowsky
Title      : GraphLab: Asynchronous Graph Computation in the Clouds
             and Beyond
Abstract   :
As the amounts of collected data and computing power grows (multicore, GPUs,
clusters, clouds), modern datasets no longer fit into one computing node.
Efficient distributed/parallel algorithms for handling large scale data are
required. Efficient solutions must take into account both system aspects and
algorithmic aspects; solutions that ignore other aspects will not scale. In
this talk, I will cover my work concerning both the theoretical and the
practical aspects of parallel and distributed large scale computing on
massive datasets. My work has focused on the following three elements:
efficient algorithm design, compact yet accurate data representation and
efficient large scale system building.
Regarding efficient algorithm design, my work is mainly focused on the
design of asynchronous iterative algorithms. Asynchronous algorithms are
especially suitable for heterogeneous computing environments since they
allow for flexible scheduling and thus efficient utilization of all
computing resources. As for compact data models, I have worked on developing
the theory and methods for handling heavy-tailed distributions, which
naturally characterize many system workloads like network flows in a
communication network. Having more accurate data representation allows for
savings both in storage space and significant reduction of the required
As a case study for this approach, I will present the GraphLab framework
which is a parallel programming abstraction targeted for sparse asynchronous
graph algorithms. The Distributed GraphLab implementation is targeted to
allow execution of large scale machine learning algorithms in parallel on
multiple platforms, such as multicores, clusters and clouds. GraphLab
provides a high level programming interface, allowing a rapid deployment of
distributed algorithms. GraphLab is an open source project available for
download on  <>
Joint work with: Carlos Geustrin, Joseph Gonzalez, Yucheng Low, Aapo Kyrola,
Joseph Bradely from Carnegie Mellon University and Joseph Hellerstein from
Refreshments served from 14:15 on,
 	Lecture starts at 14:30
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