Acceleration of acoustic emission signal processing algorithms using CUDA standard
Offline processing of acoustic emission (AE) signal waveforms recorded during a long-term AE monitoring session is a challenging problem in AE testing area. This is due to the fact that today's AE systems can work with up to hundreds of channels and are able to process tens of thousands of AE events per second. The amount of data recorded during the session is very high. This paper proposes a way to accelerate signal processing methods for acoustic emission and to accelerate similarity calculation using the Graphic Processing Unit (GPU). GPU-based accelerators are an affordable High Performance Computing (HPC) solution which can be used in any industrial workstation or laptop. They are therefore suitable for onsite AE monitoring. Our implementation, which is based on Compute Unified Device Architecture (CUDA), proves that GPU is able to achieve 30 times faster processing speed than CPU for AE signal preprocessing. The similarity calculation is accelerated by up to 80 times. These results prove that GPU processing is a powerful and low-cost accelerator for AE signal processing algorithms.
acoustic emission cuda standard gpu multicore platforms