High Performance Computing: Trends in Parallel Computing, Research & Python

University of Colorado Boulder - Wednesday September 18, 2013 @ 6:00pm MST

NOTE: For folks unable to attend in person register and we will email you a livestream link 2 hours prior to event.

Location: ATLAS - 1125 18th St Bldg 223, Boulder, CO - Room 100

Map: http://goo.gl/maps/XTJ9v

Agenda:

6:00 - 6:15 Schmooze - Food will be served in Lobby.

6:15 - 7:30 Trends in Research Cyberinfrastructure by Dr. Thomas Hauser

7:30 - 8:30 Many-task Computing for Everyone: How Python is Making Parallel Computing Accessible by Monte Lunacek

8:30 - 9:30 Network at The Sink at 1165 13th Street.

See: http://bit.ly/ND8Kp

Trends in Research Cyberinfrastructure - Abstract

High Performance Computing (HPC) has evolved in the past decade to provide "supercomputing" capabilities at significantly lower costs. Modern HPC uses parallel processing techniques for solving complex computational problems. HPC technology focuses on developing parallel processing algorithms and systems by incorporating both administration and parallel computational techniques.

The integration of computing resources, software, and networking, along with data storage, information management, and human resources to advance scholarship and research is a fundamental goal of cyberinfrastructure (CI). Such integration creates opportunities for researchers, educators, and learners to share ideas, expertise, tools, and facilities in new and powerful ways that cannot be realized if each of these components is applied independently.

This presentation will discuss the newest trends in three dimensions of CI: Computational systems, including high-performance and high-througput computing and networks/communications. Information management, including data creation, storage, handling, retrieval, distribution, interpretation, as well as policies on research data. Human/social aspects of CI, including industry, federal, and campus partnerships.

HPC technology today is implemented in multidisciplinary areas including:

• Biosciences

• Astrophysics

• Finance and trading

• Geographical data

• Oil and gas industry

• Electronic design automation

• Climate research

• Media and entertainment

In the future, HPC plus data science will help public and private organizations get actionable, valuable intelligence from massive volumes of data and use predictive and prescriptive analytics to make better decisions and create game-changing strategies.

Bio

Dr. Thomas Hauser is the Director of Research Computing (https://www.rc.colorado.edu/about/director) at the University of Colorado Boulder (https://www.rc.colorado.edu/). The research computing group provides support and training for computational needs including large-scale computing, storage of research data, high-speed networking, consulting and training, and partnering on grant proposals.

Dr. Hauser earned his Ph.D. in mechanical engineering in computational fluid dynamics from the University of Technology in Munich, Germany. Before coming to CU Boulder, Dr. Hauser was the Director of the Center for High Performance Computing at Utah State University, and Associate Director for Research Computing at Northwestern University. Dr. Hauser was also the founding director of the Center for High Performance Computing at Utah State University.

Many-task Computing for Everyone: How Python is Making Parallel Computing Accessible - Abstract

Researchers in life and social sciences are increasingly utilizing high performance compute and storage systems to advance their work. One common question that continues to surface for these scientists is: what's the most efficient way to execute thousands of independent tasks on a cluster? This body of research is called Many-Task Computing (MTC) and in this talk we compare the characteristics and scaling of several different ways to address this problem using Python.

A decade ago, distributed computing usually implied using the Message Passing Interface (MPI) standard with a language like C, C++, or Fortran, possibly running as a hybrid code using OpenMP. But today more accessible languages, such as Python, give users different options for utilizing an HPC resource in very efficient ways. The talk will include many examples from different domains. Our results suggest that Python is an excellent way to manage many-task computing and that no single technique is the obvious choice in every situation.

Bio

Monte Lunacek is a consultant and educator in the Research Computing group at the University of Colorado Boulder. Monte received his PhD in Computer Science from Colorado State University and was a postdoc in the Computational Science group at the National Renewable Energy Lab (NREL) before joining University of Colorado. His expertise are in high performance computing and parameter optimization.