The Cluster Devils are part of an undergraduate outreach effort by the ASU Ira A. Fulton High Performance Computing Initiative. This six-member team prepares during the months leading up to the fall Student Cluster Competition in November, where universities from all over the world congregate to determine what school has the best compute cluster. This year the competition takes place in Portland, OR, during the week long Supercomputing SC09 conference.  

Call for Proposals: Fulton HPCI Allocations

Due: Midnight, November 18, 2009.

Fulton HPCI is now accepting proposals for additional
allocations of CPU time on HPCI computer systems. Awards granted consist of a
maximum of 200,000 CPU-hours for one year. Approximately 2 million CPU-hours
are available for allocation.

Please visit http://hpc.asu.edu/services/facultyresources for detailed information about the RFP.

"High Performance Computing at ASU: Accelerating Your Research with Supercomputing"
Dr. Scott Menor
Research Scientist, High Performance Computing Institute
Location: Biodesign Institute Auditorium
Time: 11 a.m. - 12 p.m.
For more information <http://biodesign.asu.edu/assets/pdfs/seminars/Menor.pdf>

Ashay Rane and Dan Stanzione Ph.D.
{ashay.rane, dstanzi}@asu.edu

Fulton High Performance Computing Initiative, Arizona State University

Abstract

The Hybrid method of parallelization (using MPI for inter-node communication and OpenMP for intra-node communication) seems a natural fit for the way most clusters are built today. It is generally ex- pected to help programs run faster due to factors like availability of greater bandwidth for intra-node communication. However, optimizing hybrid applications for maximum speedup is difficult primarily due to inadequate transparency provided by the OpenMP constructs and also due to the dependence of the resulting speedup on the combination in which MPI and OpenMP is used. In this paper we mention some of our experiences in trying to optimize applications built using MPI and OpenMP. More specifically, we talk about the different techniques that could be helpful to other researchers working on hybrid applications. To demonstrate the usefulness of these optimizations, we provide results from optimizing a few typical scientific applications. Using these opti-mizations, one hybrid code ran up to 34% faster than pure-MPI code.

By Jeff Conner
Arizona State University

Abstract:

This paper describes in a general sense how the Hadoop API can be
extended to deal with multiple file formats beyond ASCII text.  This
technique is applied to binary image files in order to enable Hadoop to
implement image processing techniques on a large scale.  Several image
processing algorithms are utilized to demonstrate this technique as
well as a few different approaches to image file segmentation, similar
to what is already done for ASCII text file segmentation.

Introduction:

Since its conception, Hadoop has traditionally been thought of as an
ASCII text file processing utility, however, given the nature of the
technology driving cloud computing and the growing development of
toolsets and techniques, it becomes useful to extend Hadoop to deal
with a wide variety of file types beyond ASCII text files.  By
extending the current API in the Hadoop library we have built a system
that allows for large scale image analysis using any number of image
processing techniques.  In this paper I will introduce the API changes
we made in order to allow Hadoop to handle images as well demonstrate
this technique using a few sample image processing algorithms.

From a cursory examination of the Hadoop API it appears that it was

Andy Lee, a graduate research assistant in electrical engineering, is quick to admit that he is not a computer programmer. But when his mentor, electrical engineering Regents’ professor Constantine Balanis encouraged him to learn more computer science, Lee committed fully, taking an introductory course in parallel programming from Dan Stanzione, the director of the High Performance Computing Initiative (HPCI) at ASU. The skills he developed in the class have since formed the basis for achieving his master’s degree, as he adapts a nonparallelized code from former students in his program. The result has been a complete shift in his graduate school experience.