[Crib-list] Speaker: OSKAR MENCER -- "Computational Research in Boston and Beyond Seminar" -- Friday, March 1, 2013 -- TIME: 12:00 Noon in Building 32, Room 141 (Stata Center)

[Shirley Entzminger]

 		     COMPUTATIONAL RESEARCH  in BOSTON and BEYOND SEMINAR


DATE:		FRIDAY, MARCH 1, 2013
TIME:		12:00 Noon
LOCATION:	Building 32, Room 141   (Stata Center)

Pizza and beverages will be provided at 11:45 AM outside Room 32-141


TITLE:		Multiscale Dataflow Computing

SPEAKER:	OSKAR MENCER   (CEO, Maxeler Technologies)


ABSTRACT:

Complexity of computation is a function of the underlying representation. 
We are extending this basic concept to consider representation of 
computational problems on the application level, the model level, the 
architecture level, arithmetic level and gate level of computation. In 
particular, the first step is to consider and optimize the discretization 
of a problem in time, space and value. Discretization of value is 
particularly painful, both in Physics where atomic discretization ruins 
many nice theories, and in computation, where most people just blindly use 
IEEE double precision floating point so they don't have to worry about 
details, until they do.  Multiscale Dataflow Computing provides a process 
by which one can optimize the discretization of time, space and value 
based on particular underlying computer architecture, and in fact, iterate 
the molding of the computer architecture and the discretization of the 
computational challenge.

The above methods have been able to achieve 10-50x faster computation per 
cubic foot and per Watt, resulting in less nodes per computation and 
therefore exponentially improved reliability and resiliency.  Results 
published by users worldwide include financial modeling (American Finance 
Technology Award for most cutting edge technology, 2011), commercial 
deployment in the Oil&Gas industry (see Society of Exploration 
Geophysicists meetings and reports), weather modeling (reducing time to 
compute a Local Area Model - LAM from 2 hours to 2 minutes) and even 
sparse matrix solvers which cannot be parallelized, running 20-40x faster.

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Massachusetts Institute of Technology
Cambridge, MA


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