DIMACS Workshop on Parallelism: A 2020 Vision

March 14 - 16, 2011
DIMACS Center, CoRE Building, Rutgers University, Piscataway, NJ

Organizers:

Phil Gibbons, Intel

Howard Karloff, AT & T Research, howard at research.att.com

Sergei Vassilvitskii, Yahoo! Research

Slides:

• Foto Afrati, National Technical University of Athens
  Cluster Computing, Recursion and Datalog

• Werner Backes, Stevens Institute
  Lattice Basis Reduction and Multi-Core

• Graham Cormode, ATT Labs-Research
  Distributed Summaries

• Jonathan Eckstein, Rutgers University
  A Survey of Parallelism in Solving Numerical Optimization and Operations Research Problems

• James Edwards, University of Maryland
  Can PRAM Graph Algorithms Provide Practical Speedups on Many-Core Machines?

• Mike Goodrich, UC Irvine
  Sorting, Searching, and Simulation in the MapReduce Framework

• John Kubiatowicz, UC Berkeley
  Locally Limited but Globally Unbounded: Dealing with Resources in an Explicitly Parallel World

• John Langford, Yahoo! Research
  A Summary of Parallel Learning Efforts

• S. Muthu Muthukrishnan, Rutgers University
  Mapreduce With Parallelizable Reduce

• Jeffrey Oldham, Google
  Experiences Scaling Use of Google's Sawzall

• Kunle Olukotun, Stanford
  Taming Heterogeneous Parallelism with Domain Specific Languages

• Vijaya Ramachandran, University of Texas
  Resource Oblivious Parallel Computing

• Alejandro Salinger, University of Waterloo
  Theoretical Modeling of Multicore Computation

• Tamas Sarlos, Yahoo! Research
  On Scheduling in Map-Reduce and Flow-Shops

• Gokarna Sharma, Louisiana State University
  Scalable Transactional Memory Scheduling

• Marc Snir, University of Illinois
  The Post-Moore Era and Exascale Computing: On the Need for New Foundations

• Sid Suri, Yahoo! Research
  Counting Triangles and the Curse of the Last Reducer

• Kanat Tangwongsan, Carnegie-Mellon University
  Efficient Parallel Approximation Algorithms: What We Learn From Facility Location

• Philippas Tsigas, Chalmers University, Sweden
  Design Challenges for Scalable Concurrent Data Structures

• Uzi Vishkin, University of Maryland
  From Asymptotic PRAM Speedups To Easy-To-Obtain Concrete XMT Ones

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