PELGA 2016 - 2nd Workshop on Performance Engineering for Large Scale Graph Analytics

The knowledge economy is based on data, of which graphs represent an increasing part, in advanced marketing, in social networking, in life sciences, in health and bioinformatics services, in academic networks, in hiring of professionals, etc. As a consequence, graph analytics is fast becoming a significant consumer of computing resources, due to ever larger graphs of hundreds of millions up to hundreds of billions of edges, and to increased complexity of analysis tasks. To enable existing algorithms to fit modern architectures and scale with these new requirements, there is a growing need for performance engineering.
This workshop is a venue for specialists from both industry and academia to discuss the state of the art of graph processing systems, with a special focus on performance. Contributions focusing on graph-centric performance engineering tools and methods, workload characterization, and performance modeling are especially welcome. We also invite contributions covering surveys, performance studies, comparative analyses, new algorithms and new graph processing systems. This broad mix of ideas will stir discussion and lead to new collaborations and new ideas.
Topics
We invite both mature (regular) and work-in-progress (short) papers on topics that include, but are not limited to:
Systems
new graph processing systems focused on high?‐performance analytics
performance studies of existing systems to be used for graph processing
comparative and/or in-depth analysis of graph processing systems
Algorithms, Applications, and Architectures
new high?-performance graph processing algorithms
new performance-aware applications for graph processing algorithms
platform-specific algorithms and their performance optimization (e.g.,GPUs, Xeon Phi, heterogeneous platforms) for graph analytics
Algorithms and/or architectures for large scale graph analytics
partitioning methods for large-scale or otherwise challenging graphs
performance characterization, modeling, and engineering
graph models for performance tuning and/or prediction of analytics workloads
performance models for prediction or ranking of graph processing platforms
performance analysis and engineering of existing graph processing algorithms
tools and benchmarks for graph-centric performance engineering