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56th Technical Symposium on Computer Science Education

作者： Ramesh, Tejas Rush, Alexander Liu, Xu Yin, Binqian Zhou, Keren Jiao, Shuyin George Mason Univ Fairfax VA USA Cornell Univ New York NY USA North Carolina State Univ Raleigh NC USA

ISBN: (纸本)9798400705311

gpu programming is a critical component in AI system courses, which is notoriously difficult to learn and teach, given its unique features such as massive parallelism and data movement across memory hierarchies. This paper presents Triton-Viz, an innovative visualization toolkit that helps students learn gpu programming using Triton, one of the most widely used programming languages to develop AI applications. Triton-Viz offers an intuitive interface with interactive visualizations of gpu operations from multiple perspectives, including parallelism, memory access, and performance metrics. By integrating into educational materials, Triton-Viz enhances hands-on learning and improves comprehension of gpu programming concepts and AI algorithms in real applications, as demonstrated in a study conducted with Computer Science students. The positive feedback from this study highlights the utility of Triton-Viz in educational settings and its potential to bridge the gap between the theoretical algorithm and practical implementations in AI courses.

关键词： Visualization Deep Learning gpu programming Triton

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gpu programming Productivity in Different Abstraction Paradigms: A Randomized Controlled Trial Comparing CUDA and Thrust

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ACM TRANSACTIONS ON COMPUTING EDUCATION 2020年第4期20卷 1–27页

作者： Daleiden, Patrick Stefik, Andreas Uesbeck, Philip Merlin Univ Nevada 4505 S Maryland Pkwy Las Vegas NV 89154 USA

Coprocessor architectures in High Performance Computing are prevalent in today's scientific computing clusters and require specialized knowledge for proper utilization. Various alternative paradigms for parallel and offload computation exist, but little is known about the human factors impacts of using the different paradigms. With computer science student participants from the University of Nevada, Las Vegas with no previous exposure to Graphics Processing Unit programming, our study compared NVIDIA CUDA C/C++ as a control group and the Thrust library. The designers of Thrust claim their higher level of abstraction enhances programmer productivity. The trial was conducted on 91 participants and was administered through our computerized testing platform. Although the study was narrowly focused on the basic steps of an offloaded computation problem and was not intended to be a comprehensive evaluation of the superiority of one approach or the other, we found evidence that although Thrust was designed for ease of use, the abstractions tended to be confusing to students and in several cases diminished productivity. Specifically, abstractions in Thrust for (i) memory allocation through a C++ Standard Template Library-style vector library call, (ii) memory transfers between the host and Graphics Processing Unit coprocessor through an overloaded assignment operator, and (iii) execution of an offloaded routine through a generic transform library call instead of a CUDA kernel routine all performed either equal to or worse than CUDA.

关键词： Computer science education parallel programming threads human factors evidence evaluation empirical studies gpu programming

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Ultra-Fast Digital Tomosynthesis Reconstruction Using General-Purpose gpu programming for Image-Guided Radiation Therapy

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TECHNOLOGY IN CANCER RESEARCH & TREATMENT 2011年第4期10卷 295-306页

作者： Park, Justin C. Park, Sung Ho Kim, Jin Sung Han, Youngyih Cho, Min Kook Kim, Ho Kyung Liu, Zhaowei Jiang, Steve B. Song, Bongyong Song, William Y. Sungkyunkwan Univ Sch Med Samsung Med Ctr Dept Radiat Oncol Seoul South Korea Univ Calif San Diego Dept Radiat Oncol Ctr Adv Radiotherapy Technol La Jolla CA 92093 USA Univ Ulsan Coll Med Asan Med Ctr Dept Radiat Oncol Seoul South Korea Pusan Natl Univ Dept Mech Engn Pusan South Korea Univ Calif San Diego Dept Elect & Comp Engn La Jolla CA 92093 USA

The purpose of this work is to demonstrate an ultra-fast reconstruction technique for digital tomosynthesis (DTS) imaging based on the algorithm proposed by Feldkamp, Davis, and Kress (FDK) using standard general-purpose graphics processing unit (GPgpu) programming interface. To this end, the FDK-based DTS algorithm was programmed "in-house" with C language with utilization of 1) gpu and 2) central processing unit (CPU) cards. The gpu card consisted of 480 processing cores (2 x 240 dual chip) with 1,242 MHz processing clock speed and 1,792 MB memory space. In terms of CPU hardware, we used 2.68 GHz clock speed, 12.0 GB DDR3 RAM, on a 64-bit OS. The performance of proposed algorithm was tested on twenty-five patient cases (5 lung, 5 liver, 10 prostate, and 5 head-and-neck) scanned either with a full-fan or half-fan mode on our cone-beam computed tomography (CBCT) system. For the full-fan scans, the projections from 157.5 degrees-202.5 degrees (45 degrees-scan) were used to reconstruct coronal DTS slices, whereas for the half-fan scans, the projections from both 157.5 degrees-202.5 degrees and 337.5 degrees-22.5 degrees (2 x 45 degrees-scan) were used to reconstruct larger FOV coronal DTS slices. For this study, we chose 45 degrees-scan angle that contained similar to 80 projections for the full-fan and similar to 160 projections with 2 x 45 degrees-scan angle for the half-fan mode, each with 1024 x 768 pixels with 32-bit precision. Absolute pixel value differences, profiles, and contrast-to-noise ratio (CNR) calculations were performed to compare and evaluate the images reconstructed using gpu- and CPU-based implementations. The time dependence on the reconstruction volume was also tested with (512 x 512) x 16, 32, 64, 128, and 256 slices. In the end, the gpu-based implementation achieved, at most, 1.3 and 2.5 seconds to complete full reconstruction of 512 x 512 x 256 volume, for the full-fan and half-fan modes, respectively. In turn, this meant that our implementati

关键词： Digital tomosynthesis gpu programming FDK on-board imager IGRT

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Low-Level Functional gpu programming for Parallel Algorithms 5

Low-Level Functional GPU Programming for Parallel Algorithms

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5th ACM International Workshop on Functional High-Performance Computing (FHPC)

作者： Dybdal, Martin Elsman, Martin Svensson, Bo Joel Sheeran, Mary Univ Copenhagen Copenhagen Denmark Chalmers Univ Technol Gothenburg Sweden

ISBN: (纸本)9781450344333

We present a Functional Compute Language (FCL) for low-level gpu programming. FCL is functional in style, which allows for easy composition of program fragments and thus easy prototyping and a high degree of code reuse. In contrast with projects such as Futhark, Accelerate, Harlan, Nessie and Delite, the intention is not to develop a language providing fully automatic optimizations, but instead to provide a platform that supports absolute control of the gpu computation and memory hierarchies. The developer is thus required to have an intimate knowledge of the target platform, as is also required when using CUDA/OpenCL directly. FCL is heavily inspired by Obsidian. However, instead of relying on a multi-staged meta-programming approach for kernel generation using Haskell as meta-language, FCL is completely self-contained, and we intend it to be suitable as an intermediate language for data-parallel languages, including data-parallel parts of high-level array languages, such as R, Matlab, and APL. We present a type-system and a dynamic semantics suitable for understanding the performance characteristics of both FCL and Obsidian-style programs. Our aim is that FCL will be useful as a platform for developing new parallel algorithms, as well as a target-language for various code-generators targeting gpu hardware.

关键词： Type systems data-parallel languages gpu programming push arrays pull arrays iteration schemes array-programming hierarchical machine models

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Introduction to gpu programming with GLSL

Introduction to GPU Programming with GLSL

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22nd Brazilian Symposium on Computer Graphics and Image Processing

作者： Marroquim, Ricardo Maximo, Andre CNR Ist Sci & Tecnol Informaz I-56100 Pisa Italy Univ Fed Rio de Janeiro COPPE Lab Computacao Graf Rio de Janeiro Brazil

ISBN: (纸本)9781424449798

One of the challenging advents in Computer Science in recent years was the fast evolution of parallel processors, specially the gpu - graphics processing unit. gpus today play a major role in many computational environments, most notably those regarding real-time graphics applications, such as games. The digital game industry is one of the main driving forces behind gpus,, it persistently elevates the state-of-art in Computer Graphics, pushing outstanding realistic scenes to interactive levels. The evolution of photo realistic scenes consequently demands better graphics cards from the hardware industry. Over the last decade, the hardware has not only become a hundred times more powerful, but has also become increasingly customizable allowing programmers to alter some of previously fixed functionalities. This tutorial is an introduction to gpu programming using the OpenGL Shading Language - GLSL. It comprises an overview of graphics concepts and a walk-through the graphics card rendering pipeline. A thorough understanding of the graphics pipeline is extremely important when designing a program in CPU, known as a shatter. Throughout this tutorial, the exposition of the GLSL language and gpu programming details are followed closely by examples ranging from very simple to more practical applications. It is aimed at an audience with no or little knowledge on the subject.

关键词： gpu programming Graphics Hardware GLSL

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Information visualization of multi-dimensional cellular automata using gpu programming

Information visualization of multi-dimensional cellular auto...

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11th International Conference on Information Visualization (IV 2007)

作者： Gobron, Stephane Mestre, Daniel Univ Aix Marseille 2 Federat Res Inst Marey CNRS UMR 6152 Movement & Percept Lab F-13009 Marseille France

ISBN: (纸本)9780769529004

We propose a method for generating all possible rules of multi-dimension Boolean cellular automata (CA). Based on an original encoding method and the programming of graphical processor units (gpu), this method allows us to visualize the CA information flow in real-time so that emerging behaviors can be easily identified. Algorithms of first and von Neumann neighborhood second degrees are detailed with their respective Fragment Shaders programs. As symmetrical CA rules are especially useful in many research fields, we propose an encoding technique to automatically derive their codes;we then apply this technique to identify the 4096 possible cases for surface CA. To show the efficiency of our model a set of converging global behaviors are listed and described. In the last part of the paper we present methods for developing Moore neighborhood in two and in three dimensions. Finally we discus issues concerning computation and the visualization of non-Boolean and higher dimension CA.

关键词： cellular automaton information visualization emerging behavior real-time imaging gpu programming computer graphics

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Geoscience visualization with gpu programming

Geoscience visualization with GPU programming

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Conference on Visualization and Data Analysis 2005

作者： Lin, JCR Landmark Graph Houston TX 77042 USA

ISBN: (纸本)081945642X

In recent years, off-the-shelf graphics cards have provided the ability to program the a aphics processing unit (gpu) as an alternative to using fixed function pipelines. We believe that this capability can enable a new paradigm in geoscience data visualization. In the past, the geoscience data preparation, interpretation, and simulation were all done by the central processing unit (CPU), and then the generated graphics primitives were fed into a gpu for visualization. This approach was dictated by the constraints imposed by the general-purpose graphics application programming interfaces (APIs). With gpu programming, this front-end processing can be done in the gpu and visualized immediately. After passing the geometry data into the gpu, parameters can be used to control these processes inside the gpu. The different algorithms associated with these processes can be applied at run time by loading a new shading program. To prove this concept, we designed and implemented Java-based shader classes, which operate on top of Cg, a high-level language for graphics programming. These shader classes load Cg shaders to provide a new method for visualizing and interacting with geoscience data. The results from this approach show better visual quality for seismic data display and dramatically improved performance for large 3D seismic data sets. For editing geological surfaces, tests demonstrate performance levels 10 times faster than the typical approach. This paper describes the use of these shaders and presents the results of shader application to geoscience data visualization.

关键词： geoscience visualization shader gpu programming data visualization volume visualization

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Understanding the Topics and Challenges of gpu programming by Classifying and Analyzing Stack Overflow Posts 2023

Understanding the Topics and Challenges of GPU Programming b...

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31st ACM Joint Meeting of the European Software Engineering Conference / Symposium on the Foundations-of-Software-Engineering (ESEC/FSE)

作者： Yang, Wenhua Zhang, Chong Pan, Minxue Nanjing Univ Aeronaut & Astronaut Coll Comp Sci & Technol Nanjing Peoples R China Nanjing Univ State Key Lab Novel Software Technol Nanjing Peoples R China

ISBN: (纸本)9798400703270

gpus have cemented their position in computer systems, not restricted to graphics but also extensively used for general-purpose computing. With this comes a rapidly expanding population of developers using gpus for programming. However, programming with gpus is notoriously difficult due to their unique architecture and constant evolution. A large number of developers have encountered problems of one kind or another, and many of them have turned to Q&A sites for help. Unfortunately, there has been no prior work to comprehensively study the topics discussed and challenges encountered by developers in gpu programming. To fill this knowledge gap, we conduct a comprehensive study to understand the topics and challenges of gpu programming using Stack Overflow. We collect 25,269 relevant posts from Stack Overflow, propose a novel approach that combines automatic techniques and manual thematic analysis to extract topics, and build a taxonomy of topics with detailed discussions of the popularity, difficulty, and changing trends of these topics. In addition, we analyzed relevant posts through extensive manual efforts to understand the challenges of each topic and to summarize them for future research.

关键词： gpu programming Stack Overflow topic taxonomy

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Multiple oil and gas volumetric data visualization with gpu programming

Multiple oil and gas volumetric data visualization with GPU ...

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Conference on Visualization and Data Analysis 2007

作者： Lin, Jim Ching-Rong Hall, Cass Landmark Graph 2101 CityWest Blvd Houston TX 77042 USA

ISBN: (纸本)9780819466082

In recent years, multi-volume visualization has become an industry standard for analyzing and interpreting large surveys of seismic data. Advances made in computer hardware and software have moved visualization from large, expensive visualization centers to the desktop. Two of the greatest factors in achieving this have been the rapid performance enhancements to computer processing power and increasing memory capacities. In fact, computer and graphics capabilities have tended to more than double each year. At the same time, the sizes of seismic datasets have grown dramatically. Geoscientists regularly interpret projects that exceed several gigabytes. They need to interpret prospects quickly and efficiently and expect their desktop workstations and software applications to be as performant as possible. Interactive, multi-volume visualization is important to rapid prospect generation. Consequently, the ability to visualize and interpret multiple seismic and attribute volumes enhances and accelerates the interpretation process by allowing geoscientists to gain a better understanding of the structural framework, reservoir characteristics, and subtle details of their data. Therefore, we analyzed seismic volume visualization and defined four levels of intermixing: data, voxel, pixel, and image intermixing. Then, we designed and implemented a framework to accomplish these four levels of intermixing. To take advantage of recent advancements in programmable graphics processing units (gpus), all levels of intermixing have been moved from the CPU into the gpu, with the exception of data intermixing. We developed a prototype of this framework to prove our concept. This paper describes the four levels intermixing, framework, and prototype;it also presents a summary of our results and comments made by geoscientists and developers who evaluated our endeavor.

关键词： multi-volume visualization seismic data visualization volume visualization gpu programming

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A Fast Spatial Clustering Method for Sparse LiDAR Point Clouds Using gpu programming

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SENSORS 2020年第8期20卷 2309-2309页

作者： Tian, Yifei Song, Wei Chen, Long Sung, Yunsick Kwak, Jeonghoon Sun, Su North China Univ Technol Sch Informat Sci & Technol Beijing 100144 Peoples R China Univ Macau Dept Comp & Informat Sci Macau 999078 Peoples R China Beijing Key Lab Urban Intelligent Traff Control T Beijing 100144 Peoples R China Dongguk Univ Dept Multimedia Engn Seoul 04620 South Korea Purdue Univ Dept Comp & Informat Technol W Lafayette IN 47907 USA

Fast and accurate obstacle detection is essential for accurate perception of mobile vehicles' environment. Because point clouds sensed by light detection and ranging (LiDAR) sensors are sparse and unstructured, traditional obstacle clustering on raw point clouds are inaccurate and time consuming. Thus, to achieve fast obstacle clustering in an unknown terrain, this paper proposes an elevation-reference connected component labeling (ER-CCL) algorithm using graphic processing unit (gpu) programing. LiDAR points are first projected onto a rasterized x-z plane so that sparse points are mapped into a series of regularly arranged small cells. Based on the height distribution of the LiDAR point, the ground cells are filtered out and a flag map is generated. Next, the ER-CCL algorithm is implemented on the label map generated from the flag map to mark individual clusters with unique labels. Finally, obstacle labeling results are inverse transformed from the x-z plane to 3D points to provide clustering results. For real-time 3D point cloud clustering, ER-CCL is accelerated by running it in parallel with the aid of gpu programming technology.

关键词： 3D spatial clustering connected component labeling LiDAR gpu programming

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