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Real-time simulation of time-of-flight sensors

SIMULATION MODELLING PRACTICE AND THEORY 2009年第5期17卷 967-978页

作者： Keller, Maik Kolb, Andreas Univ Siegen Comp Graph Grp Inst Vis & Graph D-57076 Siegen Germany

Today's time-of-flight (TOF) sensors measure full-range distance information by estimating the elapsed time between emission and receiving of active light in real-time. Such sensors are inexpensive, compact, and they have a high performance, which especially fits real-time applications, e.g. in the fields of automotive, robotics, 3D imaging, and visualization. The simulation of such sensors is an essential building block for hardware design and application development. Therefore, the simulation data must capture the major sensor characteristics. This paper introduces a simulation approach, which is motivated by physics, for the Photonic Mixing Device (PMD) sensor which is a specific type of time-of-flight sensor. Dynamic motion blurring and resolution artifacts such as flying pixels as well as the typical deviation error are prominent effects of real world systems. Flying pixels arise when an area of inhomogeneous depth is covered by a single PMD-pixel whereas the deviation error is based on the anharmonic properties of the optical signal. The modeling of these artifacts is essential for an authentic simulation approach. We present a detailed comparison between a real PMD-device and the simulation data regarding the sensor characteristics. The proposed algorithms are implemented in a hardware accelerated solution which makes use of the programmability of modern Graphics Processing Units (gpus). This way, an interactive simulation feedback is provided for applications and further data processing. The simulation takes place in real-time and thus all required control mechanisms are accessible in real-time, too. (C) 2009 Elsevier B.V. All rights reserved.

关键词： Sensor simulation Time-of-flight Photonic Mixing Device Real-time simulation gpu programming

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Software pipelining for graphic processing unit acceleration: Partition, scheduling and granularity

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INTERNATIONAL JOURNAL OF HIGH PERFORMANCE COMPUTING APPLICATIONS 2016年第2期30卷 169-185页

作者： Liu, Bozhong Qiu, Weidong Jiang, Lin Gong, Zheng Shanghai Jiao Tong Univ Sch Informat Secur Engn 800 Dongchuan Rd Shanghai 200030 Peoples R China S China Normal Univ Sch Comp Sci Guangzhou Guangdong Peoples R China

The graphic processing unit (gpu) is becoming increasingly popular as a performance accelerator in various applications requiring high-performance parallel computing capability. In a central processing unit (CPU) or gpu hybrid system, software pipelining is a major task in order to deliver accelerated performance, where hiding CPU-gpu communication overheads by splitting a large task into small units is the key challenge. In this paper, we carry out a systematic investigation into task partitioning in order to achieve maximum performance gain. We first validate the advantage of even partition strategy, and then propose the optimal scheduling, with detailed study into how to achieve optimal unit size (data granularity) in an analytical framework. Experiments on AMD and NVIDIA gpu platforms demonstrate that our approaches achieve around 31 - 59% performance improvement using software pipelining.

关键词： Parallel computing high-performance computing gpu programming software pipelining optimal scheduling

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Dynamic partitioning-based JPEG decompression on heterogeneous multicore architectures

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CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 2016年第2期28卷 517-536页

作者： Sodsong, Wasuwee Hong, Jingun Chung, Seongwook Lim, Yeongkyu Kim, Shin-Dug Burgstaller, Bernd Yonsei Univ Dept Comp Sci Seoul 120749 South Korea LG Elect Seoul South Korea

With the emergence of social networks and improvements in computational photography, billions of JPEG images are shared and viewed on a daily basis. Desktops, tablets, and smartphones constitute the vast majority of hardware platforms used for displaying JPEG images. Despite the fact that these platforms are heterogeneous multicores, no approach exists yet that is capable of joining forces of a system's CPU and graphics processing unit (gpu) for JPEG decoding. In this paper, we introduce a novel JPEG decoding scheme for heterogeneous architectures consisting of a CPU and a general-purpose gpu. We employ an offline profiling step to determine the performance of a system's CPU and gpu with respect to JPEG decoding. For a given JPEG image, our performance model uses: (1)the CPU and gpu performance characteristics, (2)the image entropy, and (3)the width and height of the image to balance the JPEG decoding workload on the underlying hardware. Our run-time partitioning and scheduling scheme exploits task, data, and pipeline parallelism by scheduling the non-parallelizable entropy-decoding task on the CPU, whereas inverse discrete cosine transformations, color conversions, and upsampling are conducted on both the CPU and the gpu. We have implemented the proposed method in the context of the libjpeg-turbo library, which is an industrial-strength JPEG encoding and decoding engine. Libjpeg-turbo's hand-optimized SIMD routines for ARM and x86 architectures constitute a competitive yardstick for the comparison with the proposed approach. We have evaluated our approach for a total of 7194JPEG images across four high-end and middle-end CPU-gpu combinations including a mobile gpu. We achieve speedups of up to 5.2x over the SIMD version of libjpeg-turbo, and speedups of up to 10.5x over its sequential code. Taking into account the non-parallelizable JPEG entropy-decoding part, our approach achieves up to 97% of the theoretically attainable maximal speedup, with an average of 94%. C

关键词： JPEG decoding heterogeneous multicores offline profiling dynamic work partitioning gpu programming OpenCL

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Dynamic stabilization of an asymmetric nonlinear bubble oscillator

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NONLINEAR DYNAMICS 2018年第1期94卷 307-324页

作者： Hegedus, Ferenc Kalmar, Csanad Budapest Univ Technol & Econ Fac Mech Engn Dept Hydrodynam Syst Budapest Hungary

The present study focuses on the possibilities of dynamic stabilization of a gas-vapour bubble below Blake's critical threshold (Blake in the onset of cavitation in liquids, 1949) by harmonic forcing. In bubble dynamics, in terms of the ambient pressure, this threshold is known as a special limit where bubbles tend to grow infinity due to the non-strictly dissipative nature of the governing equations. The employed model is the harmonically excited Rayleigh-Plesset equation that is a nonlinear, second-order ordinary differential equation. Partial results have already been published in the literature (Hegedus in Ultrasonics 54(4):1113, 2014), Hegedus in Phys Lett A 380(9-10):1012, 2016). Throughout this paper, however, the investigated parameter space is significantly extended: excitation properties (pressure amplitude and frequency), ambient pressure, bubble size and liquid viscosity (amount of dissipation). The numerical results have indicated that domains where stable oscillations exist can always be found below Blake's threshold. However, from application point of view, it is mandatory to raise the dissipation rate of the system to significantly increase the extent of these domains making the process of dynamic stabilization robust.

关键词： Dynamic stabilization Bubble dynamics Bifurcation structure gpu programming Rayleigh-Plesset equation Nonlinear dynamics

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Change detection by probabilistic segmentation from monocular view

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MACHINE VISION AND APPLICATIONS 2014年第5期25卷 1175-1195页

作者： Hernandez-Lopez, Francisco J. Rivera, Mariano Ctr Invest Matemat AC Guanajuato Gto 36240 Mexico

We present a method for foreground/background video segmentation (change detection) in real-time that can be used, in applications such as background subtraction or analysis of surveillance cameras. Our approach implements a probabilistic segmentation based on the Quadratic Markov Measure Field models. This framework regularizes the likelihood of each pixel belonging to each one of the classes (background or foreground). We propose a new likelihood that takes into account two cases: the first one is when the background is static and the foreground might be static or moving (Static Background Subtraction), the second one is when the background is unstable and the foreground is moving (Unstable Background Subtraction). Moreover, our likelihood is robust to illumination changes, cast shadows and camouflage situations. We implement a parallel version of our algorithm in CUDA using a NVIDIA Graphics Processing Unit in order to fulfill real-time execution requirements.

关键词： Change detection QMMF segmentation Graph Cut segmentation Background likelihood Illumination changes Shadow detection Camouflage Background maintenance gpu programming CUDA

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Real-time terrain reconstruction using 3D flag map for point clouds

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MULTIMEDIA TOOLS AND APPLICATIONS 2015年第10期74卷 3459-3475页

作者： Song, Wei Cho, Kyungeun North China Univ Technol Coll Informat Engn Beijing 100144 Peoples R China Dongguk Univ Seoul Dept Multimedia Engn Seoul 100715 South Korea

Mobile robot operators need to make quick decisions based on information about the robot's surrounding environment. This study proposes a graphics processing unit (gpu)-based terrain modeling system for large-scale LiDAR (Light Detection And Ranging) dataset visualization using a voxel map and a textured mesh. A 3D flag map is proposed for incrementally registering large-scale point clouds in a terrain model in real time. The sensed 3D point clouds are quantized into regular 3D grids that are allocated in the gpu memory to remove redundant spatial and temporal points. Subsequently, the sensed vertices are segmented as ground and non-ground classes. The ground indices are rendered using a textured mesh to represent the ground surface, and the non-ground indices, using a colored voxel map by a particle rendering method. The proposed approach was tested using a mobile robot equipped with a LiDAR sensor, video camera, GPS receiver, and gyroscope. The simulation was evaluated through a test in an outdoor environment containing trees and buildings, demonstrating the real-time visualization performance of the proposed method in a large-scale environment.

关键词： Mobile robot Terrain reconstruction gpu programming Large-scale point cloud Real-time visualization

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OpenACC acceleration for the PN-PN-2 algorithm in Nek5000

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JOURNAL OF PARALLEL AND DISTRIBUTED COMPUTING 2019年 132卷 69-78页

作者： Otero, Evelyn Gong, Jing Min, Misun Fischer, Paul Schlatter, Philipp Laure, Erwin KTH Mech Linne FLOW Ctr SE-10044 Stockholm Sweden KTH Royal Inst Technol PDC HPC SE-10044 Stockholm Sweden Argonne Natl Lab 9700 S Cass Ave Argonne IL 60439 USA

Due to its high performance and throughput capabilities, gpu-accelerated computing is becoming a popular technology in scientific computing, in particular using programming models such as CUDA and OpenACC. The main advantage with OpenACC is that it enables to simply port codes in their "original" form to gpu systems through compiler directives, thus allowing an incremental approach. An OpenACC implementation is applied to the CFD code Nek5000 for simulation of incompressible flows, based on the spectral-element method. The work follows up previous implementations and focuses now on the P-N-PN-2 method for the spatial discretization of the Navier-Stokes equations. Performance results of the ported code show a speed-up of up to 3.1 on multi-gpu for a polynomial order N > 11. (C) 2019 Elsevier Inc. All rights reserved.

关键词： Nek5000 OpenACC gpu programming Spectral element method High performance computing

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SkelCL: a high-level extension of OpenCL for multi-gpu systems

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JOURNAL OF SUPERCOMPUTING 2014年第1期69卷 25-33页

作者： Steuwer, Michel Gorlatch, Sergei Univ Munster D-48149 Munster Germany

Application development for modern high-performance systems with graphics processing units (gpus) currently relies on low-level programming approaches like CUDA and OpenCL, which leads to complex, lengthy and error-prone programs. We present SkelCL-a high-level programming approach for systems with multiple gpus and its implementation as a library on top of OpenCL. SkelCL makes three main enhancements to the OpenCL standard: (1) memory management is simplified using parallel container data types (vectors and matrices);(2) an automatic data (re)distribution mechanism allows for implicit data movements between gpus and ensures scalability when using multiple gpus;(3) computations are conveniently expressed using parallel algorithmic patterns (skeletons). We demonstrate how SkelCL is used to implement parallel applications, and we report experimental evaluation of our approach in terms of programming effort and performance.

关键词： Parallel programming gpu programming OpenCL Algorithmic skeletons SkelCL Many-cores

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Real-time parallel image processing applications on multicore CPUs with OpenMP and GPgpu with CUDA

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JOURNAL OF SUPERCOMPUTING 2018年第6期74卷 2255-2275页

作者： Aydin, Semra Samet, Refik Bay, Omer Faruk Gazi Univ Ankara Turkey Ankara Univ Ankara Turkey

This paper presents real-time image processing applications using multicore and multiprocessing technologies. To this end, parallel image segmentation was performed on many images covering the entire surface of the same metallic and cylindrical moving objects. Experimental results on multicore CPU with OpenMP platform showed that by increasing the chunk size, the execution time decreases approximately four times in comparison with serial computing. The same experiments were implemented on GPgpu using four techniques: (1) Single image transmission with single pixel processing;(2) Single image transmission with multiple pixel processing;(3) Multiple image transmission with single pixel processing;and (4) Multiple image transmission with multiple pixel processing. All techniques were implemented on GeForce, Tesla K20 and Tesla K40. Experimental results of gpu with CUDA platform showed that by increasing the core number speedup is increased. Tesla K40 gave the best results of 35 and 12 (for the first technique), 36 and 13 (for the second technique), 54 and 16 (for the third technique), 71 and 17 (for the fourth technique) times improvement without and with data transmission time in comparison with serial computing. As a result, users are suggested to use Tesla K40 gpu and Multiple image transmission with multiple pixel processing to get the maximum performance.

关键词： Parallel computing Real-time image processing Image segmentation Thresholding Multicore programming gpu programming

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gpu-accelerated adjoint algorithmic differentiation

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COMPUTER PHYSICS COMMUNICATIONS 2016年 200卷 300-311页

作者： Gremse, Felix Hoefter, Andreas Razik, Lukas Kiessling, Fabian Naumann, Uwe Rhein Westfal TH Aachen Expt Mol Imaging Aachen Germany Rhein Westfal TH Aachen Software & Tools Computat Engn Aachen Germany

Many scientific problems such as classifier training or medical image reconstruction can be expressed as minimization of differentiable real-valued cost functions and solved with iterative gradient-based methods. Adjoint algorithmic differentiation (AAD) enables automated computation of gradients of such cost functions implemented as computer programs. To backpropagate adjoint derivatives, excessive memory is potentially required to store the intermediate partial derivatives on a dedicated data structure, referred to as the "tape". Parallelization is difficult because threads need to synchronize their accesses during taping and backpropagation. This situation is aggravated for many-core architectures, such as Graphics Processing Units (gpus), because of the large number of light-weight threads and the limited memory size in general as well as per thread. We show how these limitations can be mediated if the cost function is expressed using gpu-accelerated vector and matrix operations which are recognized as intrinsic functions by our AAD software. We compare this approach with naive and vectorized implementations for CPUs. We use four increasingly complex cost functions to evaluate the performance with respect to memory consumption and gradient computation times. Using vectorization, CPU and gpu memory consumption could be substantially reduced compared to the naive reference implementation, in some cases even by an order of complexity. The vectorization allowed usage of optimized parallel libraries during forward and reverse passes which resulted in high speedups for the vectorized CPU version compared to the naive reference implementation. The gpu version achieved an additional speedup of 7.5 +/- 4.4, showing that the processing power of gpus can be utilized for AAD using this concept. Furthermore, we show how this software can be systematically extended for more complex problems such as nonlinear absorption reconstruction for fluorescence-mediated tomography. Prog

关键词： Adjoint algorithmic differentiation gpu programming

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