SIAM PP08 Conference
MS5: Parallel Computing with MATLAB
MATLAB has emerged as one of the languages most commonly used by scientists for
technical computing, with ~1,000,000 users worldwide. The primary benefits of
MATLAB are reduced code development time via high levels of abstractions,
interactive programming, and powerful mathematical graphics. Computationally
intensive MATLAB tasks can significantly benefit from the increased performance
offered by parallel computing. There exists a number of parallel programming
solutions for MATLAB. This workshop will provide a unique opportunity to
interact between researchers from the growing field of parallel MATLAB
computing and the technical leaders in this area.
Organizer:
Piotr Luszczek
The MathWorks, Inc
Jeremy Kepner
Massachusetts Institute of Technology
- 10:00-10:25 Parallel Computing Toolbox (PCT) and Parallel
Programming in MATLAB
-
Piotr Luszczek, The MathWorks, Inc
Abstract:
Parallel Computing Toolbox (PCT) addresses computationally
and data-intensive problems using MATLAB and Simulink
in a multiprocessor computing environment. The toolbox
allows both several independent tasks or a single parallel
computation by harnessing computing clusters and a variety
of batch queuing software implementation. The toolbox
provides high-level constructs, such as parallel loops
and algorithms, and MPI-based functions. Also, low-level
constructs for resource management are included. The Parallel
Command Window provides interactive environment
for developing parallel applications.
[PDF]
- 10:30-10:55 Parallel Programming in MATLAB: Best
Practices
-
Jeremy Kepner, Massachusetts Institute of Technology
Abstract:
Matlab is one of the most commonly used languages
for scientific computing with approximately one million
users worldwide. The Lincoln pMatlab library
(http://www.ll.mit.edu/pMatlab), The Mathworks DCT,
and StarP from ISC have brought parallel computing to
the this community using the distributed array programming
paradigm. This talk provides an introduction to distributed
array programming and will describe the best programming
practices for using distributed arrays to produce
well performing parallel Matlab programs.
[PPT]
- 11:00-11:25 Parallel MATLAB in Production Supercomputing with
Applications in Signal and Image Processing
-
Ashok Krishnamurthy, David Hudak, John Nehrbass, Siddharth Samsi, and Vijay Gadepally, Ohio Supercomputer Center
Abstract:
Parallel MATLAB enables the large community of MATLAB
users to harness the increased computing capacity
and memory of distributed memory clusters. At the Ohio
Supercomputer Center we provide our users with three varieties
of Parallel MATLAB. In this talk, we will describe
how we run these Parallel MATLAB environments within
a traditional batch oriented queuing system. We will also
describe our experiences in developing three signal and image
processing applications within this environment.
[PPT]
- 11:30-11:55 Interactive Data Exploration with Star-P
-
Viral B. Shah, University of California, Santa Barbara
Abstract:
High performance applications increasingly combine numerical
and combinatorial algorithms. Past research on
high performance computation has focused mainly on numerical
algorithms, and there is a rich variety of tools
for high performance numerical computing. On the other
hand, few tools exist for large scale combinatorial computing.
We describe our efforts to build a common infrastucture
for numerical and combinatorial computing by using
parallel sparse matrices to implement parallel graph algorithms.
[PDF | PPT]