Distributed Computing on Wireless Sensor Networks with QR Decomposition Demonstration
Explore the innovative use of QR decomposition in distributed computing on wireless sensor networks, showcasing a new algorithm for achieving optimized trade-offs between parallelism and communication costs. This research presents a resource-aware distribution strategy that enhances speed and energy efficiency significantly compared to centralized approaches.
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Presentation Transcript
QR Decomposition: Demonstration of Distributed Computing on Wireless Sensor Networks By Sherine Abdelhak, Soumik Ghosh, Rabi Chaudhuri, Magdy Bayoumi CACS, University of Louisiana at Lafayette Lafayette, LA 70504 {spa9242, sxg5317, rxc2763, mab} @ cacs.louisiana.edu High Performance Embedded Computing (HPEC) Workshop 22 23 September 2009 (A) Approved for public release; distribution is unlimited.
QR Decomposition: Demonstration of Distributed Computing on WSN Scope Kernel for distributed numerical algorithms on Energy and Resource- constrained Wireless Embedded Platforms Wireless Sensor Network is an example of a severely resource constrained platform QR in WSN QR-based least mean square (LMS) QR-based recursive least mean square (RLS) RLS and LMS in adaptive filtering and beamforming Proposed QR Blend of Householder reflections, Givens rotations, and AllReduce Introduction
QR Decomposition: Demonstration of Distributed Computing on WSN Develop a new distributed QR algorithm Achieve a balanced tradeoff between degree of parallelism and communication cost Propose aresource-aware distribution Achieve 2x speedup and 2x energy savings per node vs. centralized (single node) approach Contributions Introduction
QR Decomposition: Demonstration of Distributed Computing on WSN Givens Phase Proposed Algorithm Introduction Proposed Scheme
QR Decomposition: Demonstration of Distributed Computing on WSN Proposed Distribution on WSN Introduction Proposed Scheme
