Abstract
Nowadays, the aim of the technology industry is intensively shifting to improve the ratio Gflop/watt of computation. Many processors implement the low power design of ARM architecture like, e.g. the NVIDIA TK1, a chip which also includes a GPU embedded in the same die to improve performance at a low energy consumption. This type of devices are very suitable target machines to be used on applications that require mobility like, e.g. those that manage and reproduce real acoustics environments. One of the most used algorithms in these reproduction environments is the Beamformer Algorithm. We have implemented the variant called Beamformer QR-LCMV, based on the QR decomposition, which is a very computationally demanding operation. We have explored different options differing basically in the high performance computing library used. Also we have built our own version with the aim of approaching the real-time processing goal when working on this type of low power devices.
This work has been supported by projects TEC2015-67387-C4-1-R of the Spanish Ministerio de Economía y Competitividad and PROMETEOII/2014/003 of the Generalitat Valenciana.
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ARM. (2016) ARM processors. http://www.arm.com/products/processors/
Dongarra, J., et al.: “PLASMA users’ guide”, Electrical Engineering, Computer Science Department, Univesity of Tennessee, Knoxville, Tennessee 37996, Technical report (2015). http://icl.cs.utk.edu/plasma
Benesty, Y.H.J., Chen, J., Dmochowski, J.: On microphone-array beamforming from a mimo acoustic signal processing perspective. IEEE Trans. Audio Speech Lenguage Process. 15, 1053–1065 (2007)
Lorente, J., Piñero, G., Vidal, A., Belloch, J., González, A.: Parallel implementations of beamforming design and filtering for microphone array applications. In: Proceedings of the 19th European Signal Processing Conference (EUSIPCO), Barcelona, Spain, pp. 501–505 (2011)
NVIDIA: NVIDIA CUDA Basic Linear Algebra Subroutines. https://developer.nvidia.com/cublas
NVIDIA. (2015) NVIDIA Jetson TK1. http://www.nvidia.es/object/jetson-tk1-embedded-dev-kit-es.html
NVIDIA. (2016) NVIDIA Kepler. http://www.nvidia.es/object/nvidia-kepler-es.html
OpenBLAS: An optimized BLAS library. http://www.openblas.net/
Tomov, S., Dongarra, J., Baboulin, M.: Towards dense linear algebra for hybrid GPU accelerated manycore systems. Parallel Comput. 36(5–6), 232–240 (2010). http://icl.cs.utk.edu/magma
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Alventosa, F.J., Alonso, P., Piñero, G., Vidal, A.M. (2016). Implementation of the Beamformer Algorithm for the NVIDIA Jetson. In: Carretero, J., et al. Algorithms and Architectures for Parallel Processing. ICA3PP 2016. Lecture Notes in Computer Science(), vol 10049. Springer, Cham. https://doi.org/10.1007/978-3-319-49956-7_16
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DOI: https://doi.org/10.1007/978-3-319-49956-7_16
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