[Crib-list] TODAY...Special COMPUTATIONAL RESEARCH in BOSTON Seminar -- Tuesday, 11/24/2009 -- 12:30 PM -- Room 2-139 (fwd)

[Shirley Entzminger]

T O D A Y . . .

				Special
		COMPUTATIONAL RESEARCH in BOSTON SEMINAR


NOTE:  Different day and location...

DATE:		Tuesday, NOVEMBER 24, 2009
TIME:		12:30 PM
LOCATION:	Building 2, Room 139

Pizza and beverages will be provided at 12:15 PM outside Room 2-139.


TITLE:		An Online High Performance Computing Service for 
		Genetic Linkage Analysis


SPEAKER:	MARK SILBERSTEIN  (Technion-Israel Institute of 
				   Technology)


ABSTRACT:

In this talk I will describe the algorithms and mechanisms underlying a 
distributed system for genetic linkage analysis, called Superlink-online.  
It is a production online system which serves hundreds of geneticists 
worldwide allowing for faster analysis of genetic data via automatic 
parallelization and execution on thousands of non-dedicated computers.

I will describe the following innovative technologies forming the core of 
this system 

1.	Practical scheduling and execution of embarrassingly parallel Bags 
	of Tasks in multiple non-dedicated computing environments (SC09).  
	Our approach allows for virtualization of multiple grids, clouds and 
	Volunteer gids as a single computing platform by building an overlay
	of execution clients over the physical resources; another component 
	is a generic mechanism for dynamic scheduling policies to reduce the 
	turnaround time in the presence of resource failures and 
	heterogeneity. Our system has executed hundreds of Bags of Tasks 
	with over 9 million jobs during 3 months alone; these have been 
	invoked on 25,000 hosts from the local clusters, the Open Science 
	Grid, EGEE, UW Madison pool and Superlink at Technion community grid.

2.	A general technique for designing memory-bound algorithms on GPUs 
	through software-managed cache (ICS08).  This technique was 
	successfully applied to the probabilistic network inference 
	yielding an order of magnitude performance improvement versus the 
	performance without such a cache.  Overall we achieved up to three 
	orders of magnitude speedup when executing our GPU-based algorithm 
	versus single CPU performance.

3.	Coarse- and fine-grained parallel algorithms for the inference in 
	probabilistic networks on large-scale non-dedicated environments and 
	GPUs.  We devised and implemented an algorithm suitable for loosely 
	coupled environments with unreliable resources (American Journal of 
	Human Genetics 2006, HPDC06) and adapted it for heterogeneous GPU-CPU 
	supercomputer TSUBAME in Tokyo Tech.


http://math.mit.edu/crib/09/nov24.html

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Massachusetts Institute of Technology
Cambridge, MA  02139