The Tensor-Core Beamformer: A High-Speed Signal-Processing Library for Multidisciplinary Use
Abstract
Beamforming is a well-known technique to combine signals from multiple sensors. It has a wide range of application domains. This paper introduces the Tensor-Core Beamformer: a generic, optimized beamformer library that harnesses the computational power of GPU tensor cores to accelerate beamforming computations. The library hides the complexity of tensor cores from the user, and supports 16-bit and 1-bit precision. An extensive performance evaluation on NVIDIA and AMD GPUs shows that the library outperforms traditional beamforming on regular GPU cores by a wide margin, at much higher energy efficiency. In the 16-bit mode, it achieves over 600 TeraOps/s on an AMD MI300X GPU, while approaching 1 TeraOp/J. In the 1-bit mode, it breaks the 3 PetaOps/s barrier and achieves over 10 TeraOps/J on an NVIDIA A100 GPU. The beamforming library can be easily integrated into existing pipelines. We demonstrate its use for medical ultrasound and radio-astronomical instruments.
Cite
@article{arxiv.2505.03269,
title = {The Tensor-Core Beamformer: A High-Speed Signal-Processing Library for Multidisciplinary Use},
author = {Leon Oostrum and Bram Veenboer and Ronald Rook and Michael Brown and Pieter Kruizinga and John W. Romein},
journal= {arXiv preprint arXiv:2505.03269},
year = {2025}
}
Comments
11 pages, 7 figures, accepted at the IEEE International Parallel & Distributed Processing Symposium (IPDPS) 2025