PyGASP: Python-based GPU-accelerated signal processing

Bowman, Nathaniel; Carrier, Erin; Wolffe, Greg

doi:10.1109/eit.2013.6632683

Cited by 1 publication

(1 citation statement)

References 11 publications

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“…In our research, we explore the potential of a hardware-based parallel processing architecture to execute the FHSI algorithm [20,21], recognized as one of the most effective reconstruction algorithms to date. We leveraged the GPU-accelerated capabilities of the Nvidia Xavier platform, employing PyCUDA [22,23] to develop kernels aimed at optimizing bottleneck operations. Our optimization strategies included the use of shared memory with padding to prevent bank conflicts [24], coalescing global memory accesses for increased throughput [25], pre-computation of constants to diminish runtime calculations [26], loop unrolling [27], and warp divergence minimization through conditional optimization [4].…”

Section: Introductionmentioning

confidence: 99%

Accelerating the Fast Hadamard Single-Pixel Imaging (FHSI) on GPUs: Techniques and Optimizations

Quero,

Durini,

Rangel-Magdaleno

et al. 2024

Preprint

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The recent advancements in edge computing power are primarily attributable to technological innovations enabling accelerators with extensive hardware parallelism. One practical application is in computer imaging (CI), where GPU acceleration is pivotal, especially in reconstructing 2D images through techniques like Single-Pixel Imaging (SPI). In SPI, compressive sensing (CS) algorithms, deep learning, and Fourier transformation are essential for 2D image reconstruction. These algorithms derive substantial performance enhancements through parallelism, thereby reducing processing times. These techniques fully utilize the potential of the GPU by implementing several strategies. These include optimizing memory accessed, expanding loops for efficiency, designing effective computational kernels to reduce the number of operations, using asynchronous operations for better performance, and increasing the number of actively running threads and warps. In lab scenarios, integrating embedded GPUs becomes essential for algorithmic optimization on SoC-GPUs. This study focuses on quickly improving the Fast Hadamard Single-Pixel Imaging (FHSI) for 2D image reconstruction on Nvidia's Xavier platform. By implementing various parallel computing techniques in PyCUDA, we managed to speed up the process by approximately 10 times, significantly reducing processing times to nearly real-time levels.

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