Proceedings

Introduction

Opening Talk

  • Randy Bryant, Carnegie Mellon University: Data Intensive Scalable Computing: Finding the Right Programming Models

Data Analysis Requirements from Instruments and Sensors

  • Duncan Brown, Syracuse University: Computational Challenges in Gravitational Wave Astronomy
  • Thomas Hacker, Purdue University: NEEShub: A Data Cyberinfrastructure for Earthquake Engineering
  • John Orcutt, University of California at San Diego: Stream Processing of Multi-scale, Near-Real-Time Environmental Data
  • Kirk Borne, George Mason University: Petascale Data Challenges and Design Decisions for the Large Synoptic Survey Telescope Science Data System
  • Art Wetzel, Pittsburgh Supercomputing Center: Connectomics: Challenges in Reconstructing Neural Circuitry from PBytes of Electron Microscopy Data

Data-Intensive Science, Approaches and Algorithms I

  • Homa Karimabadi, UCSD: Physics Mining: New Approach to Science Data Discovery in Petascale Systems
  • William Cohen, Carnegie Mellon University: Learning to Extract a Broad-Coverage Knowledge Base from the Web
  • Michael Schatz, Cold Spring Harbor Laboratory: Cloud Computing and the DNA Data Race
  • Chris Hill, Massachusetts Institute of Technology: Infrastructure for Extreme Data Intensive Computing
  • Yucheng Low, Carnegie Mellon University: GraphLab: A Distributed Framework for Machine Learning
  • Jeff Gardner, University of Washington: Data Intensive Scalable Computing for Astronomy
  • John Johnson, Pacific Northwest National Laboratory: Data-Intensive Cyber Data Analytics

Data-Intensive Science, Approaches and Algorithms II

  • Joohyun Kim, Louisiana State University: DARE-NGS: Towards an Extensible and Scalable NGS Analytics on the TeraGrid/XD
  • Wayne Pfeiffer, San Diego Supercomputer Center: Compute- and Data-Intensive Analyses in Bioinformatics
  • James Taylor, Emory University: Accessible, Transparent, and Reproducible Data Analysis with Galaxy
  • Rupert Croft, Carnegie Mellon University: Visualization of Petascale Cosmological Simulations

Panel: Systems for Data-Intensive Analysis

  • Scott Ahern, National Institute for Computational Sciences: The University of Tennessee Center for Remote Data Analysis and Visualization (RDAV)
  • Jay Alameda, National Center for Supercomputing Applications: NCSA Resources for Data-Intensive Analysis
  • Nick Nystrom, Pittsburgh Supercomputing Center: Blacklight: A Very Large Hardware-Coherent Shared Memory for Data Analytics and Data-Intensive Simulation
  • Allan Snavely, San Diego Supercomputer Center: Gordon Applications
  • Dan Stanzione, Texas Advanced Computing Center: The Longhorn System for Visualization and Data-Intensive Computing