Advanced Computing, Mathematics and Data
Staff Awards & Honors

October 2013

PNNL Scientists Take the Lead at ParLearning 2014

In another demonstration of PNNL's leadership position in the big data research area, several members of the Computational Sciences and Mathematics and Computational and Statistical Analytics divisions are leading efforts involved in the upcoming Workshop on Parallel and Distributed Computing for Large Scale Machine Learning and Big Data Analytics, or ParLearning 2014, to be held in Phoenix on May 23, 2014. Those involved include:

• Abhinav Vishnu, General Co-chair (CSMD High-performance Computing group
• George Chin, Publicity Co-chair (Computational and Statistical Analytics)
• Sutanay Choudhury, Program Committee Member (CSMD Scientific Data Management group).

ParLearning 2014 will focus on the potential of parallel and distributed computing to deal with the massive data sets generated by today's computing platforms. Of primary consequence, these data often are stored away without establishing their rightful place in a knowledge representation and determining subsequent actions. To manage the data deluge, a convergence between parallel and distributed computing and the interdisciplinary science involved in artificial intelligence (AI) becomes critical. Thus, ParLearning 2014 is uniting leaders from both the AI and parallel and distributed computing communities to identify research areas that require most convergence and assess their impact on the broader technical landscape.

Vishnu, Chin, and Choudhury (left to right)

As part of the workshop, a call for papers has been issued seeking submissions related to the interplay between parallel and distributed computing techniques and learning/inference applications, including algorithm design and libraries/framework development on multicore/manycore architectures, graphic processing units, clusters, supercomputers, or cloud computing platforms.

Paper submissions will be accepted through Monday, December 30, 2013. All manuscripts should be submitted through the EDAS portal for ParLearning 2014 (login required). All accepted papers will be included in the Proceedings of the IEEE International Symposium on Parallel & Distributed Processing with some recommended for publication in Parallel & Cloud Computing.

Contact Abhinav Vishnu for more information.