Network Coding in Heterogeneous Multicore IoT Nodes with DAG Scheduling of Parallel Matrix Block Operations

Simon Wunderlich, Juan A. Cabrera, Frank H.P. Fitzek, Martin Reisslein

Research output: Contribution to journalArticle

27 Scopus citations

Abstract

Random linear network coding (RLNC) has the potential to improve the performance of current and future Internet of Things (IoT) communication systems, but is computationally demanding due to matrix multiplications and inversions. Some single-core RLNC implementations achieve already sufficient coding speeds for contemporary multimedia streaming formats. However, advances in multimedia streaming formats and IoT applications will require the exploitation of heterogeneous multicore architectures, which are becoming common for a wide range of IoT nodes, including smartphones. In this paper, we introduce and evaluate efficient RLNC computing strategies for IoT node architectures, including the emerging heterogeneous big.LITTLE multicore architectures with multiple big (fast) cores and multiple LITTLE (slow) cores. In contrast to existing RLNC implementation strategies, we build on and adapt highly optimized dense matrix operations from the high performance computing field to RLNC on heterogeneous multicore IoT nodes. Our approach includes the optimization of RLNC matrix operations through optimized operations on matrix blocks with single instruction multiple data instructions. We schedule block operations on the heterogeneous cores through a directed acyclic graph that avoids artificial synchronization points while ensuring the data dependencies. We examine priority scheduling according to the number of outgoing dependencies of a task and data locality of cached blocks. Our extensive measurements with several heterogeneous big.LITTLE multicore IoT node and smartphone processor boards demonstrate higher RLNC encoding and decoding throughputs than existing approaches. Moreover, our measurements indicate that the utilization of more cores decreases energy consumption, which is an important goal for IoT nodes.

Original languageEnglish (US)
Article number7926320
Pages (from-to)917-933
Number of pages17
JournalIEEE Internet of Things Journal
Volume4
Issue number4
DOIs
StatePublished - Aug 2017

Keywords

  • Directed acyclic graph (DAG)
  • Internet of Things (IoT) node
  • heterogeneous multicore architecture
  • matrix inversion
  • matrix multiplication
  • parallel computing
  • random linear network coding (RLNC)
  • smartphone

ASJC Scopus subject areas

  • Signal Processing
  • Information Systems
  • Hardware and Architecture
  • Computer Science Applications
  • Computer Networks and Communications

Fingerprint Dive into the research topics of 'Network Coding in Heterogeneous Multicore IoT Nodes with DAG Scheduling of Parallel Matrix Block Operations'. Together they form a unique fingerprint.

  • Cite this