Welcome to the home page of the
 2nd Workshop on Parallel Programming for Analytics Applications 
to be held during the 20th 

February 7-11, 2015
San Francisco, California


December 8, 2014: Paper submission deadline.
January 10, 2015: Decision on refereed papers.
January 24, 2015: Camera ready copies due. Accepted papers will be published in the ACM digital library after the workshop.
February 8, 2015: Workshop!

Motivation and Scope

Analytics applications are scaling rapidly in terms of the size and variety of data analyzed, the complexity of models explored and tested, and the number of analytics professionals or data scientists supported concurrently.  Consumer behavior modeling, IT infrastructure security and resiliency, and fraud detection and prevention are examples of application areas where the scaling is stressing the computational capabilities of current systems.  At the same time hardware systems are embracing new technologies like on-chip and off-chip accelerators, vector extensions to the instruction sets, and solid state disks.  New programming methodologies and run-times to support them are emerging to facilitate the development of the new analytics applications, and to leverage the emerging systems.  This workshop provides a forum for the applications community, run-time and development-environment community,and systems community to exchange the outlook for progress in each of these areas, and exchange ideas on how to cross leverage the progress. Topics of interest include, but are not limited to:

  • System and hardware support for big data analytics
    • Exploitation of GPUs, FPGAs and on-chip vector processing units for analytics applications
    • Efficient exploitation of the memory hierarchy, particularly solid state disks
    • Parallel I/O to support distributed file systems
    • System management issues for attaining the desired levels of reliability and performance for the above
  • Parallel run-times and middleware for analytics
    • Columnar databases, large data warehouses, data cubes and OLAP engines
    • In memory analysis for real-time queries on large data
    • No-SQL databases
    • Graph databases
    • Concurrency in large tabular data analytics
    • Distributed file systems
  • Parallel programming models and languages, and application development frameworks for analytics
    • Application Frameworks for large graph applications 
    • Computational models and programming languages for large graph applications
    • Domain specific languages for analytics
  • Parallel algorithms for large graphs and other big data analytics applications
    • Algorithms to exploit the hardware, run-times, middleware and programming models listed above
    • Performance attainable on the hardware, run-times, middleware and programming models listed above
  • Parallelism in Social Media and other big dataapplications
    • Applications in consumer modeling and customer behavior 
    • Financial fraud detection and intrusion detection in IT infrastructure
    • Applications in healthcare and other industries
    • Analytics applications and solutions in homeland security