Open AccessJournal Article
A Compressive Sensing Algorithm for Many-Core Architectures
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A parallel algorithm for solving the l1- compressive sensing problem takes advantage of shared memory, vectorized, parallel and many-core microprocessors such as Graphics Processing Units (GPUs) and standard vectorized multi-core processors (e.g. quad-core CPUs).Abstract:
This paper describes a parallel algorithm for solving the l1- compressive sensing problem. Its design takes advantage of shared memory, vectorized, parallel and many-core microprocessors such as Graphics Processing Units (GPUs) and standard vectorized multi-core processors (e.g. quad-core CPUs). Experiments are conducted on these architectures, showing evidence of the efficiency of our approach.read more
Citations
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Parallelization of Fast 1-Minimization for Face Recognition
TL;DR: In this article, the augmented Lagrangian method (ALM) solvers are used for face recognition in both GPU and CPU hardware, and the proposed implementations significantly out-perform naive library-based implementations.
High-performance and Hardware-aware Computing, Proceedings of the First International Workshop on New Frontiers in High-performance and Hardware-aware Computing (HipHaC'11)
Rainer Buchty,Jan-Philipp Weiß +1 more
TL;DR: This work analyzes Convey’s programming paradigm and the associated programming effort, and presents practical results on the HC-1, a dual-target compiler that interprets pragma-extended C/C++ or Fortran code and produces implementations running on both, host and accelerator.
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