Leaner and meaner: Network coding in SIMD enabled commercial devices

TY - GEN

T1 - Leaner and meaner: Network coding in SIMD enabled commercial devices

AU - W. Sørensen, Chres

AU - Paramanathan, Achuthan

AU - Cabrera, Juan A.

AU - Pedersen, Morten V.

AU - Lucani Rötter, Daniel Enrique

AU - Fitzek, Frank H P

PY - 2016/9/12

Y1 - 2016/9/12

N2 - Although random linear network coding (RLNC) constitutes a highly efficient and distributed approach to enhance communication networks and distributed storage, it requires additional processing to be carried out in the network and in end devices. For mobile devices, this processing translates into energy use that may reduce the battery life of a device. This paper focuses not only on providing a comprehensive measurement study of the energy cost of RLNC in eight different computing platforms, but also explores novel approaches (e.g., tunable sparse network coding) and hardware optimizations for Single Instruction Multiple Data (SIMD) available in the latest generations of Intel and Advanced RISC Machines (ARM) processors. Our measurement results show that the former provides gains of two-To six-fold from the underlying algorithms over RLNC, while the latter provides gains for all schemes from 2x to as high as 20x. Finally, our results show that the latest generation of mobile processors reduce dramatically the energy per bit consumed for carrying out network coding operations compared to previous generations, thus making network coding a viable technology for the upcoming 5G communication systems, even without dedicated hardware.

AB - Although random linear network coding (RLNC) constitutes a highly efficient and distributed approach to enhance communication networks and distributed storage, it requires additional processing to be carried out in the network and in end devices. For mobile devices, this processing translates into energy use that may reduce the battery life of a device. This paper focuses not only on providing a comprehensive measurement study of the energy cost of RLNC in eight different computing platforms, but also explores novel approaches (e.g., tunable sparse network coding) and hardware optimizations for Single Instruction Multiple Data (SIMD) available in the latest generations of Intel and Advanced RISC Machines (ARM) processors. Our measurement results show that the former provides gains of two-To six-fold from the underlying algorithms over RLNC, while the latter provides gains for all schemes from 2x to as high as 20x. Finally, our results show that the latest generation of mobile processors reduce dramatically the energy per bit consumed for carrying out network coding operations compared to previous generations, thus making network coding a viable technology for the upcoming 5G communication systems, even without dedicated hardware.

UR - http://www.scopus.com/inward/record.url?scp=84989880706&partnerID=8YFLogxK

U2 - 10.1109/WCNC.2016.7565066

DO - 10.1109/WCNC.2016.7565066

M3 - Article in proceedings

BT - 2016 IEEE Wireless Communications and Networking Conference, WCNC 2016

PB - IEEE

T2 - WCNC 2016

Y2 - 3 April 2016 through 6 April 2016

ER -