This paper develops implementation strategy and method to accelerate the propagation and backpropagation (PBP) tomographic imaging algorithm using Graphic Processing Units (GPUs). The Compute Unified Device Architecture (CUDA) programming model is used to develop our parallelized algorithm since the CUDA model allows the user to interact with the GPU resources more efficiently than traditional ...

Although deconvolution can improve the quality of any type of microscope, the high computational time required has so far limited its massive spreading. Here we demonstrate the ability of the scaled-gradient-projection (SGP) method to provide accelerated versions of the most used algorithms in microscopy. To achieve further increases in efficiency, we also consider implementations on graphic pr...

Though discrete particle simulation (DPS) has been widely used for investigating gas-solid flows from a more detailed level as compared to traditional two-fluid models (TFM), it is still seriously limited by the computational cost when large scale systems are simulated. GPUs (graphic processing units), with their massive parallel architecture and high floating point performance, provide new pos...

General purpose computation using Graphic Processing Units (GPUs) is a wellestablished research area focusing on high-performance computing solutions for massively parallelizable and time-consuming problems. Classical methodologies in machine learning and data mining cannot handle processing of massive and high-speed volumes of information in the context of the big data era. GPUs have successfu...

The staggered-grid finite difference (FD) method demands significantly computational capability and is inefficient for seismic wave modelling in 2-D viscoelastic media on a single PC. To improve computation speedup, a graphic processing units (GPUs) accelerated method was proposed, for modern GPUs have now become ubiquitous in desktop computers and offer an excellent cost-toperformance-ratio pa...

v High complexity of computer-generated holography (CGH) is already well known to researchers in the field of optical engineering. Therefore, in order to improve the practicability of CGH, it should be substantially reduced. In this paper, we proposed a method that can quickly generate digital video holograms using a spatiotemporal sampling scheme and general purpose graphic processing units (G...

Graphics Processing Units (GPUs) have become the focus of much interest with the scientific community lately due to their highly parallel computing capabilities, and cost effectiveness. They have evolved from simple graphic rendering devices to extremely complex parallel processors, used in a plethora of scientific areas. This paper outlines experimental results of a comparison between GPUs and...

Interest in parallel architectures applied to real time selections is growing in High Energy Physics (HEP) experiments. In this paper we describe performance measurements of Graphic Processing Units (GPUs) and Intel Many Integrated Core architecture (MIC) when applied to a typical HEP online task: the selection of events based on the trajectories of charged particles. We use as benchmark a scal...

Future architectures will feature hundreds to thousands of simple processors and on-chip memories connected through a network-on-chip. Architectural simulators will remain primary tools for design space exploration, performance (and power) evaluation of these massively parallel architectures. However, architectural simulation performance is a serious concern, as virtual platforms and simulation...

Finite difference is a simple, fast and effective numerical method for seismic wave modeling, and has been widely used in forward waveform inversion and reverse time migration. However, intensive calculation of three-dimensional seismic forward modeling has been restricting the industrial application of 3D pre-stack reverse time migration and inversion. Aiming at this problem, in this paper, a ...