PPAA 2016 - Workshop on Parallel Programming for Analytics Applications

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