Scientific Programming

Advances in Big Data Programming and System Software


Lead Editor

1University of Hertfordshire, Hertfordshire, UK

2Huazhong University of Science and Technology, Wuhan, China

3University of Delaware, Newark, USA

4University of Victoria, Victoria, Canada

Advances in Big Data Programming and System Software


In the era of big data, developing modern computing systems and system software that can scale to massive amounts of data becomes a key challenge to both researchers and practitioners. Scalability in distributed system usually means that the performance of a system should increase proportionally with the increase of resources. However, this is not sufficient in the big data era. The system should be designed in a way so that all the five Vs of big data can be tackled.

Driven by this insight, this special issue aims at presenting the current state-of-the-art research and future trends on various aspects of big data programming and system software techniques for big data processing. We look for papers discussing how to build highly adaptive big data systems that can automatically adapt their behaviors to the amount of available resources, including methodologies, modeling, analysis, and newly introduced applications. Besides the latest research achievements, this special issue also covers innovative commercial data management systems, innovative commercial applications of big data technology, and experience in applying recent research advances to real-world problems. The papers will be peer reviewed and selected on the basis of both their quality and their relevance to the theme of this special issue.

Potential topics include but are not limited to the following:

  • Big data programming models
  • Big data runtime systems
  • Compiler techniques for big data
  • Database techniques and systems for big data
  • New memory techniques
  • Big data tools and visualization
  • Parallelizing and vector compiler techniques
  • Compiler optimizations for superscalar/VLIW architectures
  • Intermediate representations and flow analysis
  • Languages and libraries for high-performance computing
  • Run-time optimizations
  • Parallel programming environments
  • Cluster and grid computing and applications
  • Data-intensive applications and parallel I/O
  • Pervasive and ubiquitous computing
  • Compiler/OS/architecture for low power
  • High availability architectures
  • Scalable and reconfigurable systems
  • Scientific applications
Scientific Programming
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