On Improving Parallelized Network Coding with Dynamic Partitioning

Abstract

In this paper, we investigate parallel implementation techniques for network coding. It is known that network coding is useful for both wired and wireless networks and it also 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 practical network systems and to improve the decoding performance, 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 a higher performance enhancement can be achieved through balanced partitioning methods 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 5.69 speedup which is better than the linear speedup with the influence of additional cache. Moreover, on an octal-core system, our algorithms even achieve speedup of 8.46 compared to a sequential network coding and 43.3 percent faster than an existing parallelized technique using 1 Mbytes data with 1;024 Â 1;024 coefficient matrix size.Index Terms-Conversion from sequential to parallel forms, parallel algorithms, concurrent programming, data communications, network communications.

Energy Efficient QoS-Aware Random Network Coding on Smartphones

“…A number of works have considered ways to improve the throughput and reduce the decoding latency of RNC [3][4][5][6][7][8][9][10][11][12][13]. RNC was proposed by Ho et al [1] as a capacity-achieving scheme for multicasting in wired networks following the work by Ahlswede et al [2] who first suggested network coding and showed its utility in wireline networks.…”

“…Otherwise, users of the system cannot experience the benefit of RNC. To overcome the computational overhead of RNC, a number study focused on the performance of RNC [3][4][5][6][7][8][9][10][11][12][13]; recent advances have succeeded in mitigating the concerns. According to one recent study [13], the throughput of an optimized RNC decoder on smartphone platforms is over 1 Gbps, which well exceeds H.264 1080p30 FHD bandwidth requirement of 30 Mbps.…”

Semi-progressive Network Coding Algorithm on Multi-core Processor

“…It significantly improves the decoding performance but the workload between threads is unbalanced. So Park [19] proposed dynamic partitioning algorithm to make load balanced and improve the performance of network coding. However, their dynamic partitioning method produced additional delay because each row received was partitioned at Stage A and Stage E with different methods.…”

“…We propose the Modified Dynamic Partitioning (MDP) method based on the Dynamic Partitioning (DP) method discussed in [19]. As shown in Fig.…”

Fast Parallel Implementation for Random Network Coding on Embedded Sensor Nodes

“…Conventional parallelized Gauss-Jordan elimination algorithms such as parallel adaptive Gauss-Jordan algorithm [18] and other related algorithms such as parallel matrix inversion [19] and parallel LU decomposition [20] require the entire data before starting the decoding process and thus will incur additional decoding latency on the receiver compared to the progressive decoding. Park et al also have proposed efficient parallelized progressive network coding algorithm with dynamic partitioning algorithms for multicore CPUs [5]. Shojania et al in [7] have proposed GPGPU based parallelized progressive decoding algorithm and in [8] parallel multisegment decoding algorithm for buffered data.…”