The Tensor-Core Beamformer: A High-Speed Signal-Processing Library for Multidisciplinary Use

TL;DR

The Tensor-Core Beamformer is a GPU-accelerated library that significantly speeds up beamforming computations across various applications by leveraging tensor cores, achieving high performance and energy efficiency.

Contribution

It introduces a generic, optimized beamforming library utilizing GPU tensor cores with support for low-precision modes, outperforming traditional implementations.

Findings

Achieves over 600 TeraOps/s in 16-bit mode on AMD GPUs.

Breaks 3 PetaOps/s barrier in 1-bit mode on NVIDIA GPUs.

Demonstrates applicability in medical ultrasound and radio astronomy.

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.