A Parallel Adaptive Gauss-Jordan Algorithm
The Journal of Supercomputing
Parallel LU Decomposition on a Transputer Network
Proceedings of the Shell Conference on Parallel Computing
An algebraic approach to network coding
IEEE/ACM Transactions on Networking (TON)
Minimum-cost multicast over coded packet networks
IEEE/ACM Transactions on Networking (TON) - Special issue on networking and information theory
Code torrent: content distribution using network coding in VANET
MobiShare '06 Proceedings of the 1st international workshop on Decentralized resource sharing in mobile computing and networking
Comprehensive view of a live network coding P2P system
Proceedings of the 6th ACM SIGCOMM conference on Internet measurement
XORs in the air: practical wireless network coding
IEEE/ACM Transactions on Networking (TON)
Codecast: a network-coding-based ad hoc multicast protocol
IEEE Wireless Communications
IEEE Transactions on Information Theory
A Random Linear Network Coding Approach to Multicast
IEEE Transactions on Information Theory
Design and evaluation of random linear network coding Accelerators on FPGAs
ACM Transactions on Embedded Computing Systems (TECS)
Reconfigurable and parallelized network coding decoder for VANETs
Mobile Information Systems
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In this paper, we investigate parallel implementation techniques for network coding to enhance the performance of Peer-to-Peer (P2P) file sharing applications. It is known that network coding mitigates peer/piece selection problems in P2P file sharing systems; however, due to the decoding complexity of network coding, there have been concerns about adoption of network coding in P2P file sharing systems and to improve the decoding speed the exploitation of parallelism has been proposed previously. In this paper, we argue that naive parallelization strategies of network coding may result in unbalanced workload distribution and thus limiting performance improvements. We further argue that higher performance enhancement can be achieved through load balancing in parallelized network coding and propose new parallelization techniques for network coding. Our experiments show that, on a quad-core processor system, proposed algorithms exhibit up to 30% of speed-up compared to an existing approach using 1 Mbytes data with 2048×2048 coefficient matrix size.