WHPBDC 2018 - 3rd International Workshop on High Performance Big Data Computing (WHPBDC 2018)

During the past few decades, there has been considerable evolution in the innovations in hardware architectures as the multi-core parallel machines or MIC (Many Integrated Cores) and the progress in the field of parallelism and techniques used to managing and processing Big Data. Processing large datasets for extracting information and knowledge has always been a fundamental problem. Today this problem is further exacerbated, and the sequential resolution is unable to find solutions in a reasonable time. However, parallelization in this context seeks to exploit the massive computing resources increasingly available on the multicore computers. The interplay between cores and architectures dramatically complicates the design of new approaches towards the parallelization of new frameworks that are central to achieving optimal performance.
The Workshop on High Performance Big Data Computing provides a platform for the dissemination of recent research efforts that explicitly aim at addressing these challenges. It supports the presentation of solutions for a modern HPC systems and the associated middleware which have been exploiting the advances in HPC technologies. It also support the new approach used to address the Big Data problems in cloud infrastructure.
Topics of Interest
The High Performance Big Data Computing workshop calls for contributions that address fundamental research and system issues in High Performance Big Data Computing including but not limited to the following: :
High performance Big Data frameworks, programming, models and tools
High-level parallel methods for structured and semi-structured datasets
High-level parallelism in programming languages and libraries
Big Data system on HPC and Grid computing
Many-Task Computing in the Cloud
World Wide Web with HPC and Grid computing
Linked Data in cloud architectures
Data mining, knowledge acquisition and data sources profiling
Analytic, prediction and evaluation of Big Data system
Prediction performance system
Evaluating Data Quality with HPC
Teaching experience with high-level tools and methods
Parallel algorithms for data-intensive applications
Domain-specific languages: design, implementation and applications
Energy-efficient data-intensive computing
Learning Algorithms