Accelerating Scientific and Engineering Applications through Cloud-based GPU Computing

14 pages•Published: December 11, 2023

Abstract

As the extensibility of GPU computing rapidly increases, we often find them useful for different applications in the field of science and engineering. Libraries written for engineer- ing tasks such as CULA (Cuda Linear Algebra), cuFFT-Cuda Fast Fourier Transforms, and cuBLAS library- Cuda Basic Linear Algebra Subprograms) have made it easier for programmers to achieve a significant performance increase when solving problems in the fields of engineering and math. In signal processing we can use the GPU to perform discrete Fourier transforms on time-domain signal strength to represent the data in the frequency domain. With the data in this format, we can calculate signal strength of various frequen- cies very efficiently, and further determine if a transmission on a particular frequency has taken place. Speedups in excess of 70 were achievable using a GPU-based implementation utilizing the cuFFT library over a CPU implementation utilizing the most performance optimized CPU-based FFT library, FFTW.

Keyphrases: anomaly detection, cuBLAS (Cuda Basic Linear Algebra Subprograms), CUDA API, cuFFT (Cuda Fast Fourier Transforms), CULA (Cuda Linear Algebra), Discrete Fourier Transform (DFT), Electrical Engineering, frequency domain analysis, GPU computing, parallel computing, performance optimization, Radio Signal Detection, signal processing, triangulation, wireless communication

In: Lindsay Quarrie (editor). Proceedings of 2023 Concurrent Processes Architectures and Embedded Systems Hybrid Virtual Conference, vol 17, pages 1--14

Links:	https://easychair.org/publications/paper/Sm5J
	https://doi.org/10.29007/kf4l