Nowadays distributed and parallel data processing is ubiquitous. Parallel cores are available on smartphones, laptops, servers and supercomputing nodes. Many devices cooperate in fully distributed and heterogeneous systems to provide even basic services. Despite astonishing progress in recent years, many challenges remain. We urgently need better or specific solutions for scalability and efficient communication in more and more complex systems. We also need more robust algorithms to cope with failures, and malicious or selfish behaviour. For better algorithm design, it is more important than ever to have theoretical tools enabling scalability, modeling and understanding of parallel algorithms, as well as data structures for exploiting parallelism.

High quality, original papers are solicited on this general topic of the theory of parallel and distributed algorithms.

Topics of interest include, but are not limited to, the theoretical aspects of:

• Design and algorithm engineering practice of distributed and parallel algorithms    
• Foundations, complexity theory, models, and emerging paradigms for parallel and distributed computation    
• Data structures for parallel and distributed algorithms    
• Algorithms and models for big data/data-intensive parallel computing        
• Scalability, concurrency and performance    
• Fault tolerance, error resilient and self-stabilizing algorithms    
• Distributed storage and distributed data processing    
• Power/energy-efficient algorithms    
• Algorithms on GPUs and accelerators    
• Instruction level parallelism research    
• Algorithmic aspects of packing, scheduling, and resource management in distributed and parallel systems    
• Algorithms for routing and information dissemination, communication networks    
• Algorithms for cloud computing    
• Algorithmic game theory related to parallel and distributed systems    
• Algorithms for computational and collaborative learning    
• Algorithms for social networks    
• Lower bounds