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[IEEE 2018 Conference on Design and Architectures for Signal and Image Processing (DASIP) - Porto, Portugal (2018.10.10-2018.10.12)] 2018 Conference on Design and Architectures for Signal and Image Processing (DASIP) - GPU Based Quarter Spectral Correlation Density Function
摘要： In this study we investigate the parallelization of a key feature extraction method called spectral correlation density (SCD) function, which is used in signal classification systems particularly under low signal-to-noise ratio conditions for classifying numerous signals. In order to reduce the computation complexity of the SCD function, we introduce a method called Quarter SCD (QSCD) that allows extracting features of a given signal by processing only quarter of the input signal data. We then parallelize the QSCD by targeting general purpose graphics processing unit (GPU) through architecture specific optimization strategies. We present experimental evaluations on identifying the parallelization configuration for maximizing the efficiency of the program architecture in utilizing the threading power of the GPU architecture. We show that algorithmic and architecture specific optimization strategies result with improving the throughput of the state of the art GPU based Full SCD from 120 signals/second to 2719 signals/second.
关键词： GPGPU,spectral correlation density,signal classification
更新于2025-09-23 15:22:29
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cuFFS: A GPU-accelerated code for Fast Faraday rotation measure Synthesis
摘要： Rotation measure (RM) synthesis is a widely used polarization processing algorithm for reconstructing polarized structures along the line of sight. Performing RM synthesis on large datasets produced by telescopes like LOFAR can be computationally intensive as the computational cost is proportional to the product of the number of input frequency channels, the number of output Faraday depth values to be evaluated and the number of lines of sight present in the data cube. The required computational cost is likely to get worse due to the planned large area sky surveys with telescopes like the Low Frequency Array (LOFAR), the Murchison Widefield Array (MWA), and eventually the Square Kilometre Array (SKA). The massively parallel General Purpose Graphical Processing Units (GPGPUs) can be used to execute some of the computationally intensive astronomical image processing algorithms including RM synthesis. In this paper, we present a GPU-accelerated code, called cuFFS or CUDA-accelerated Fast Faraday Synthesis, to perform Faraday rotation measure synthesis. Compared to a fast single-threaded and vectorized CPU implementation, depending on the structure and format of the data cubes, our code achieves an increase in speed of up to two orders of magnitude. During testing, we noticed that the disk I/O when using the Flexible Image Transport System (FITS) data format is a major bottleneck and to reduce the time spent on disk I/O, our code supports the faster HDFITS format in addition to the standard FITS format. The code is written in C with GPU-acceleration achieved using Nvidia’s CUDA parallel computing platform. The code is available at https://github.com/sarrvesh/cuFFS.
关键词： Computing methodologies: graphics processors,Techniques: image processing,Techniques: polarimetric,GPGPU,Methods: data analysis
更新于2025-09-23 15:22:29
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[IEEE 2018 - 3DTV-Conference: The True Vision - Capture, Transmission and Display of 3D Video (3DTV-CON) - Helsinki (2018.6.3-2018.6.5)] 2018 - 3DTV-Conference: The True Vision - Capture, Transmission and Display of 3D Video (3DTV-CON) - DYNAMIC FRACTURING OF 3D MODELS FOR REAL TIME COMPUTER GRAPHICS
摘要： This work proposes a method of fracturing one-sided 3D objects, in real time, using standard GPU shaders. Existing implementations include either pre-fracturing objects and replacing them at run-time, or precomputing the fracture patterns and using them to fracture the objects depending on user interaction. In this article we describe a novel method in which the fracturing calculations are handled by the GPU and only having the initial positions of the fracture fields handled by the CPU. To obtain higher resolutions of fractures, scalable tessellation is also implemented. As a result, this method allows for fast fracturing that could be utilized in real-time applications such as videogames.
关键词： fracture in computer graphics,GPGPU,VFX,Voronoi tessellation,3D games
更新于2025-09-23 15:21:01
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A CUDA-based GPU engine for gprMax: Open source FDTD electromagnetic simulation software
摘要： The Finite-Difference Time-Domain (FDTD) method is a popular numerical modelling technique in computational electromagnetics. The volumetric nature of the FDTD technique means simulations often require extensive computational resources (both processing time and memory). The simulation of Ground Penetrating Radar (GPR) is one such challenge, where the GPR transducer, subsurface/structure, and targets must all be included in the model, and must all be adequately discretised. Additionally, forward simulations of GPR can necessitate hundreds of models with different geometries (A-scans) to be executed. This is exacerbated by an order of magnitude when solving the inverse GPR problem or when using forward models to train machine learning algorithms. We have developed one of the first open source GPU-accelerated FDTD solvers specifically focussed on modelling GPR. We designed optimal kernels for GPU execution using NVIDIA’s CUDA framework. Our GPU solver achieved performance throughputs of up to 1194 Mcells/s and 3405 Mcells/s on NVIDIA Kepler and Pascal architectures, respectively. This is up to 30 times faster than the parallelised (OpenMP) CPU solver can achieve on a commonly-used desktop CPU (Intel Core i7-4790K). We found the cost-performance benefit of the NVIDIA GeForce-series Pascal-based GPUs – targeted towards the gaming market – to be especially notable, potentially allowing many individuals to benefit from this work using commodity workstations. We also note that the equivalent Tesla-series P100 GPU – targeted towards data-centre usage – demonstrates significant overall performance advantages due to its use of high-bandwidth memory. The performance benefits of our GPU-accelerated solver were demonstrated in a GPR environment by running a large-scale, realistic (including dispersive media, rough surface topography, and detailed antenna model) simulation of a buried anti-personnel landmine scenario.
关键词： GPGPU,Finite-Difference Time-Domain,GPU,CUDA,GPR,NVIDIA
更新于2025-09-11 14:15:04