Advances in Parallel and Distributed Computing and Communications

Abstract

This special issue of Concurrency and Computation: Practice and Experience provides a forum for presenting advances of current research and development in all aspects of Parallel and Distributed Computing and Communications.Because of the tremendous advances in a broad spectrum of technologies and topics including wireless networking, cloud computing and sensor systems, distributed computing and communications has evolved into an active and important area of research and development. The past decade has witnessed a proliferation of powerful parallel and distributed systems for practice of high performance computing and communications, which has become a key technology in determining future research and development activities in many academic and industrial branches, especially when the solution of large and complex problems must cope with very tight timing schedules. This special issue aims to present and discuss advances of current research and development in all aspects of parallel and distributed computing and communications.This special issue contains papers selected from a set of invited papers presented at the 14th IEEE International Conference on High Performance Computing and Communications (HPCC-2012) or the associated workshops, held in Liverpool, UK, June 26-28, 2012. The objective of HPCC-2012 was to provide a forum for scientists and engineers in academia and industry to exchange and discuss their experiences, new ideas, research results and applications about all aspects of high performance computing and communications. This Special Issue is focused on new research and developments in distributed Computing and Communications, aiming for contributions on cloud computing, content delivery networks, graphics processing unit (GPU) based algorithms, delay tolerant networking, load balancing, UAV navigation, video streaming and network routing.The paper GPU-UPGMA: high-performance computing for UPGMA algorithm based on Graphics Processing Units, by Lin et al.[1], describes a novel GPU-UPGMA approach capable of providing rapid construction of extremely large datasets for biologists. Experimental results indicate that the proposed GPU-UPGMA approach achieves an approximately 95× speedup ratio on NVIDIA Tesla C2050 GPU over the implementation with 2.13 GHz CPU. The developed techniques can also be applied to solve the classification problem for large data set.In the paper entitled FOFEM: A New Load Balancing Method for Extended OTIS-n-Cube Networks, Jehad [2] develops an efficient algorithm for load balancing on the promising Extended OTIS-n-Cube interconnection networks. Experimental results demonstrate superiority of the proposed algorithm over the well-known Clustered Dimension Exchange Method (CDEM) algorithm in terms of execution time, number of communication steps and speed, while maintaining the same level of accuracy.The paper A Congestion Control Scheme Based on Probabilistic Packet Acceptance and Drop in Delay Tolerant Networks, by An et al. [3], proposes a Probabilistic packet Acceptance and Dro...