TransMR: Data-Centric Programming Beyond Data Parallelism


Abstract :

MapReduce and related data-centric programming models have proven to be
effective for a variety of large-scale distributed computations, in
particular, those that manifest data parallelism. Independent execution of
tasks in Mapreduce, however, limits its applicability to data-parallel
applications. This application scope can be increased by allowing
data-sharing across concurrent computations. The fault-tolerance model
of MapReduce relies on deterministic replay, which makes data-sharing
(side-effects) across computations harder to support. This talk:
(i) motivates the use of distributed key-value stores (as opposed to
distributed file systems) as the underlying storage substrate; (ii)
investigates data sharing (side-effects) in programming models operating on
distributed key-value stores, specifically, the inconsistencies between the
fault recovery mechanisms in execution and storage layers; (iii) defines
semantics for a novel programming model, TransMR (Transactional MapReduce),
which addresses these inconsistencies; and (iv) demonstrates broad
application scope and enhanced performance through data-sharing across
computations for a prototype implementation of the proposed semantics.
It proposes efficient solutions to a number of problems encountered
in supporting transactional execution of maps and broader applicability
of these optimizations.



Work in collaboration with Karthik Kambatla, Naresh Rapolu, and Adnan
Hassan, supported by the US National Science Foundation.


Speaker Bio:


Ananth Grama is the Director of the Computational Science and Engineering
Program at Purdue and the Associate Director of the Center for Science of
Information, a Science and Technology Center of the National Science
Foundation. He has been an Assistant (1996-2001), Associate (2001-2006), and
Professor (2006 - ) of Computer Science at Purdue University. He was the
University Faculty Scholar from 2002 - 07. Ananth received his Ph.D. in
Computer Science from the University of Minnesota in 2006. His areas of
research include Parallel and Distributed Systems and Large-Scale Data
Handling and Analysis.

