The special issue (to be published in Elsevier Parallel Computing) is aimed at receiving papers related to the synergic use of HPC systems (e.g., multi-GPU systems, rack-based and large-scale clusters) for Data Stream Processing. The complete CFP can be found in the Special Issue web page. In the following a partial list of possible topics of interest:

-Multi-GPUs accelerated data stream processing
-FPGA-based accelerators for real-time stream processing
-State-aware management of streaming data and operators for rack-scale DSP systems
-Efficient data-movement of streams in heterogeneous many-cores systems
-Non-intrusive autonomic/elastic supports for highly-parallel data stream processing
-QoS-driven performance modeling for topologies of streaming operators
-Power-aware, energy-efficient data streaming algorithms and techniques
-Concurrent data structures for data streaming
-Optimization of existing DSP frameworks for highly-parallel many-cores and hybrid systems
-Use cases and applications of real-time analytics based on rack-scale DSP systems in various domains, including cyber-physical systems, healthcare, Internet of Things, Smart Cities, and social networks

Submitted papers must be written in English and must describe original research that has not been published, and is not currently under review by other journals or conferences.

A submission based on one or more papers that appeared elsewhere has to comprise major value-added extensions over what appeared previously (at least 40% new material). Authors are requested to attach to the submitted paper their relevant, previously published articles and a summary document explaining the enhancements made in the journal version.

Please consult and follow the Guide for Authors provided by the journal:
www.elsevier.com/journals/parallel-computing/0167-8191/guide-for-authors

All manuscripts and any supplementary material must be submitted using the journal submission system, which is accessible using the “Submit Your Paper” button on the journal home page:
www.journals.elsevier.com/parallel-computing