GraphLab: A Distributed Abstraction for Machine Learning
Speaker: Carlos Guestrin , Carnegie Mellon University
Date: February 13 2012
Time: 4:00PM to 5:00PM
Location: 32-G449
Host: Tommi Jaakkola and Sam Madden, CSAIL
Contact: Francis Doughty, 253-4602, doughty@mit.edu
Relevant URL: Today, machine learning (ML) methods play a central role in industry and
science. The growth of the Web and improvements in sensor data collection
technology have been rapidly increasing the magnitude and complexity
of the ML tasks we must solve. This growth is driving the need for scalable,
parallel ML algorithms that can handle "Big Data." Unfortunately, designing
and implementing efficient parallel ML algorithms is challenging. Existing
high-level parallel abstractions such as MapReduce and Pregel are insufficiently
expressive to achieve the desired performance, while low-level tools such as
MPI are difficult to use, leaving ML experts repeatedly solving the same design
challenges.
In this talk, I will describe the GraphLab framework, which naturally expresses
asynchronous, dynamic graph computations that are key for state-of-the-art
ML algorithms. When these algorithms are expressed in our higher-level abstraction,
GraphLab will effectively address many of the underlying parallelism challenges,
including data distribution, optimized communication, and guaranteeing sequential
consistency, a property that is surprisingly important for many ML algorithms. On a
variety of large-scale tasks, GraphLab provides 20-100x performance improvements
over Hadoop. In recent months, GraphLab has received thousands of downloads, and
is being actively used by a number of startups, research labs and universities.
This talk represents joint work with Yucheng Low, Joey Gonzalez, Joseph Bradley,
Aapo Kyrola, Jay Gu, Danny Bickson, and Joseph M. Hellerstein.
Carlos Guestrin is the Finmeccanica Associate Professor in
the Machine Learning and in the Computer Science Departments
at Carnegie Mellon University. He is also the
co-founder of Flashgroup, a start up focused on addressing
information and social overload on the web. Previously, he was a
senior researcher at the Intel Research Lab in Berkeley.
Carlos received his PhD and Masters from Stanford University, and a
Mechatronics Engineer degree from the University of Sao
Paulo, Brazil. Carlos' work received awards at a
number of conferences and two journals: KDD 2007 and 2010,
IPSN 2005 and 2006, VLDB 2004, NIPS 2003 and 2007, UAI 2005,
ICML 2005, AISTATS 2010, JAIR in 2007, and JWRPM in
2009. He is also a recipient of the ONR Young
Investigator Award, NSF Career Award, Alfred P. Sloan
Fellowship, IBM Faculty Fellowship, the Siebel Scholarship
and the Stanford Centennial Teaching Assistant Award. Carlos
was named one of the 2008 `Brilliant 10' by Popular Science
Magazine, received the IJCAI Computers and Thought Award and
the Presidential Early Career Award for Scientists and
Engineers (PECASE). He is a former member of the
Information Sciences and Technology (ISAT) advisory group
for DARPA.
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