A tile selection algorithm for data locality and cache interference
ICS '99 Proceedings of the 13th international conference on Supercomputing
A Parallel Adaptive Gauss-Jordan Algorithm
The Journal of Supercomputing
Tiling, Block Data Layout, and Memory Hierarchy Performance
IEEE Transactions on Parallel and Distributed Systems
An algebraic approach to network coding
IEEE/ACM Transactions on Networking (TON)
Optimizing locality and scalability of embedded Runge--Kutta solvers using block-based pipelining
Journal of Parallel and Distributed Computing
Network coding based reliable disjoint and braided multipath routing for sensor networks
Journal of Network and Computer Applications
UUSee: large-scale operational on-demand streaming with random network coding
INFOCOM'10 Proceedings of the 29th conference on Information communications
On Improving Parallelized Network Coding with Dynamic Partitioning
IEEE Transactions on Parallel and Distributed Systems
A reputation system for wireless mesh networks using network coding
Journal of Network and Computer Applications
Review: Survey of network coding-aware routing protocols in wireless networks
Journal of Network and Computer Applications
IEEE Transactions on Information Theory
IEEE Transactions on Information Theory
A Random Linear Network Coding Approach to Multicast
IEEE Transactions on Information Theory
Hi-index | 0.00 |
Network coding helps improve communication rate and save bandwidth by performing a special coding at the sending or intermediate nodes. However, encoding/decoding at the nodes creates computation overhead on large input data that causes coding delays. Therefore the progressive method which can hide decoding delay in waiting time is proposed in the previous works. However, the network speed has been greatly accelerated and progressive schemes are no longer the most efficient decoding method. Thus, we present non-progressive decoding algorithm that can be more aggressively parallelized than the progressive network coding, which can diminish the advantages of hidden decoding time of progressive methods by utilizing the multi-core processors. Moreover, the block algorithm implemented by non-progressive decoding helps to reduce cache misses. Through experiments, our scheme which relies on matrix inversion and multiplication shows 46.0% improved execution time and 89.2% last level cache miss reduction compared to the progressive method on multi-core systems.