Introduction to gpu programming

• Introduction to gpu programming. Jul 19, 2010 · CUDA is a computing architecture designed to facilitate the development of parallel programs. Jul 1, 2021 · CUDA is a heterogeneous programming language from NVIDIA that exposes GPU for general purpose program. A key skill that will be developed is the ability to think in parallel. A Gentle Introduction to Computer Graphics (Programming) Reading time: 28 mins. Self-driving cars, machine learning and augmented reality are some of the examples of modern applications that involve parallel computing. Apr 6, 2024 · Figure 3. In mathematics you could say that a vertex defines the location of a point in space. GPU architecture: Familiarize yourself with the architecture of different GPU models, such as NVIDIA GeForce or AMD Radeon. Introduction to GPU Programming Prerequisites. It will focus on foundational aspects of concurrent programming, such as CPU/GPU architectures, multithreaded programming in C and Python, and an introduction to CUDA software/hardware. A GPU program comprises two parts: a host part the runs on the CPU and one or more kernels that run on the GPU. CUDA is a programming language that uses the Graphical Processing Unit (GPU). This tutorial is an introduction for writing your first CUDA C program and offload computation to a GPU. OpenGL drivers need to do a lot of CPU work before handing work off to the GPU. This course offers 6 Engineering Design Points in MIT's EECS program. Changes from Version 12. Parallel Computing Stanford CS149, Fall 2021. Develop CUDA software for running massive computations on commonly available hardware. In addition, students will learn canonical algorithms used to perform high-precision mathematics and data transformations. 1 Computational Thinking. When I tried to learn about graphics, I realized it was harder than I thought to create those super slick programs I'd seen growing up. A thorough understanding of the graphics pipeline is extremely important when designing a program in GPU, known as a shader. Computer architecture. 18. Setup; The lesson. o Programming Methods for Summit Multi-GPU Nodes. , GPUs, FPGAs). Heterogeneous programming means the code runs on two different platform: host (CPU) and . We will use CUDA runtime API throughout this tutorial. • This CUDA - Introduction to the GPU - The other paradigm is many-core processors that are designed to operate on large chunks of data, in which CPUs prove inefficient. I do have an Nvidia GPU if that matters. This course will help prepare students for developing code that can process large amounts of data in parallel. GPU Computing: Step by Step • Setup inputs on the host (CPU-accessible memory) • Allocate memory for outputs on the host CPU • Allocate memory for inputs on the GPU • Allocate memory for outputs on the GPU • Copy inputs from host to GPU (slow) • Start GPU kernel (function that executes on gpu – fast!) • Copy output from GPU to Sep 30, 2021 · GPU Programming is a method of running highly parallel general-purpose computations on GPU accelerators. Includes index. II. I’ve been working with CUDA for a while now, and it’s been quite exciting to get into the world of GPU programming. Learn how to program heterogeneous computing environments Nov 12, 2014 · About Mark Ebersole As CUDA Educator at NVIDIA, Mark Ebersole teaches developers and programmers about the NVIDIA CUDA parallel computing platform and programming model, and the benefits of GPU computing. It comprises an overview of graphics concepts and a walk-through the graphics card rendering This paper reviews the GPU architecture and the tools available to utilize this valuable resource and provides insight into the type of problem best suited for the GPU as well as programming styles required to fully harness the power of the GPU. Perform a quick cost-benefit analysis in the context of own code projects. GPU computing CPUs are good for applications where most of the work is done by a Sep 6, 2019 · Introduction (5 minutes) AMD GPU Hardware (10 minutes) GPU Programming Concepts (45 minutes) GPU programming models can treat this as a separate thread of execution, CUDA by example : an introduction to general-purpose GPU programming / Jason Sanders, Edward Kandrot. scienti c computing. g. Advances in GPU technology have propelled the GPU into arenas far afield from the traditional, isolated roles they have previously played. We will start with simple problems that are embarrassingly parallel For the personal and business system, the graphics processing unit (GPU) is the most important computing technology type. This course provides introduction to computer graphics algorithms, software and hardware. However, there are now high level languages (such as CUDA and OpenCL) that target the GPUs directly, so GPU programming is rapidly becoming mainstream in the scientific community. Week 2:GPU architectures - Streaming Multi Processors, Cache Hierarchy,The Graphics Pipeline Week 3:Introduction to CUDA programming Week 4:Multi-dimensional mapping of dataspace, Synchronization Week 5:Warp Scheduling, Divergence Week 6:Memory Access Coalescing Week 7:Optimization examples : optimizing Reduction Kernels NSM Nodal Centre for Training in HPC and AI is organizing an online course on GPU Programming. But, with the discovery of the ability of GPUs in number crunching. At the moment there are three major GPU producers: NVIDIA, Intel, and AMD. One of the most difficult areas of GPU programming is general-purpose data structures. com/coffeebeforear GPU Use/Programming •GPU libraries –NVIDIA’s UDA LAS and FFT libraries –Many 3rd party libraries •Low abstraction lightweight GPU programming toolkits –CUDA C –OpenCL •High abstraction compiler-based tools –PGI x64+GPU 13 Apr 12, 2023 · Introduction to Graphics Programming. Nov 5, 2009 · Advances in GPU technology have propelled the GPU into arenas far afield from the traditional, isolated roles they have previously played. This book is meant for use as a textbook in a one-semester course that would typically be taken by undergraduate computer science majors in their third or fourth year of college. A model for thinking about GPU hardware and GPU accelerated platforms AMD GPU architecture The ROCm Software ecosystem Programming with HIP & HIPFort Programming with OpenMP Nvidia to AMD porting strategies Functionality and maintainability of GPU; How to deal with scalability; Portability issues; Parallel programming API, tools and techniques; Principles and patterns of parallel algorithms; Processor architecture features and constraints; Objectives. 1. This gave birth to CUDA structure and CUDA C Programming Language, NVIDIA’s response on facilitating the development of General Purpose Graphics Processing Unit (GPGPU). GPU code is usually abstracted away by by the popular deep learning framew Jan 12, 2024 · Introduction. Today. All that is required is some experience in basic C programming and an optional $200-300 "gamer" graphics board to demonstrate the real-life performance gains (no graphics programming experience is required). Published 2010. From the Foreword by Jack Dongarra, University of Tennessee and Oak Ridge National CUDA by Example: An Introduction to General-Purpose GPU Programming, 1st edition. 1. Types of Computer Graphics. Simplified CPU Architecture. Introduction to GPU Programming Volodymyr (Vlad) Kindratenko Innovative Systems Laboratory @ NCSA Institute for Advanced Computing Applications and Technologies (IACAT) V. As Ian mentions in his introduction, the GPU was not really designed for general-purpose computation, and getting it to operate efficiently requires some care. Kindratenko, Introduction to GPU Programming (part I), December 2010, The American University in Cairo, Egypt Ever since I opened up my Direct Messages and invited everyone to ask me computer graphics related questions on Twitter, I am very often asked the question "How can I get started with graphics programming?". A GPU comprises many cores (that almost double each passing year), and each core runs at a clock speed significantly slower than a CPU’s clock. A CUDA kernel function is the C/C++ function invoked by the host (CPU) but runs on the device (GPU). 32 contains a “crash course on GPU programming”. computer vision. The course will use the CUDA architecture and OpenCL frameworks and students will explore them in hands-on laboratory and homework assignments, along with a semester-long project in GPU Programming: When, Why and How? Graphical processing units (GPUs) are the workhorse of many high performance computing (HPC) systems around the world. GPUs were supposed to be developed for the dedicated purpose of graphics support. As one can see, the advantage of GPU processing only becomes apparent with a large vector size N. I wrote a previous post, Easy Introduction to CUDA in 2013 that has been popular over the years. Exercises (for Linux and Mac) Visual Studio Exercises (for Windows) Instructions for Exercises ; Volume II: CUDA Case Studies ; Check out our CUDAcasts playlist on youtube CUDA University Courses. The platform model of OpenCL is similar to the one of the CUDA programming model. This is especially important if you can hide the complexity of Vulkan from your customer base and just let them see the improved performance. Proficiency in programming languages: Develop a strong understanding and expertise in programming languages, particularly C/C++ and CUDA. paper) 1. These languages are commonly used in GPU programming. Sep 27, 2010 · The recent advent of general-purpose graphics-processing units (GPGPUs) as inexpensive arithmetic-processing units brings a relevant amount of computing power to modern desktop PCs. This book is required reading for anyone working with accelerator-based computing systems. Why GPUs? The GPU hardware and software ecosystem; What problems fit to GPU? GPU programming CUDA by Example: An Introduction to General-Purpose GPU Programming Quick Links. Shader performance may be improved through parallelism or by decreasing memory use, among other means. It’s become mainstream to use GPUs for scientific application development. Nov 19, 2017 · In this introduction, we show one way to use CUDA in Python, and explain some basic principles of CUDA programming. Jan 24, 2020 · CUDA Programming Interface. Added section Encoding a Tensor Map on Device. CPU computing vs. The programming guide to the CUDA model and interface. Nov 22, 2013 · Introduction When I was a kid, I thought computer graphics was the coolest thing ever. It allows one to write the code without knowing what GPU it will run on, thereby making it easier to use some of the GPU's power without targeting several types of GPU specifically. Otherwise, it was beautifully concise and clear. GPU Programming - CUDA CUDA provides a relatively simple C-like interface to develop GPU-based applications. Paperback. p. GPU Computing: Step by Step • Setup inputs on the host (CPU-accessible memory) • Allocate memory for outputs on the host CPU • Allocate memory for inputs on the GPU • Allocate memory for outputs on the GPU • Copy inputs from host to GPU (slow) • Start GPU kernel (function that executes on gpu) • Copy output from GPU to host (slow) Du Bois A and Cavalheiro G GPotion: An embedded DSL for GPU programming in Elixir Proceedings of the XXVII Brazilian Symposium on Programming Languages, (1-8) Zhuo Y, Zhang T, Du F and Liu R (2023). The aim of this article is to learn how to write optimized code on GPU using both CUDA & CuPy. The interested may register for the course here. 3. Beyond covering the CUDA programming model and syntax, the course will also discuss GPU architecture, high performance computing on GPUs, parallel algorithms, CUDA libraries, and applications of GPU computing. Kandrot, Edward. It is a parallel computing platform and an API (Application Programming Interface) model, Compute Unified Device Architecture was developed by Nvidia. 2 Multi-GPU-Programming. Key FeaturesExpand your background in GPU programming—PyCUDA, scikit-cuda, and NsightEffectively use CUDA libraries such as cuBLAS, cuFFT, and cuSolverApply GPU programming to modern data science applicationsBook Description Hands-On GPU Programming with Python and CUDA hits the ground • In the olden days – (pre-2006) – programming GPUs meant either: • using a graphics standard like OpenGL (which is mostly meant for rendering), or • getting fairly deep into the graphics rendering pipeline. It’s a space where every millisecond of performance counts and where the architecture of your code can leverage the incredible power GPUs offer. GPU Programming: Expand your knowledge about GPU optimizations and programming as time progresses. Programming GPUs using the CUDA language. This is a proposal and proof-of-concept for an interactive book about programming the graphics processor. Application software—Development. It comprises an overview of graphics concepts and a walk-through the graphics card rendering pipeline. Utilize libraries that bring well-known algorithms to software without need to redevelop existing capabilities. CUDA by Example: An Introduction to General-Purpose GPU Programming; CUDA for Engineers: An Introduction to High-Performance Parallel Computing; Programming Massively Parallel Processors: A Hands-on Approach; The CUDA Handbook: A Comprehensive Guide to GPU Programming: 1st edition, 2nd edition; Professional CUDA C Programming A gentle introduction to parallelization and GPU programming in Julia. Jun 24, 2024 · Computer Graphics refers to several things. May 14, 2021 · As GPU-accelerated systems become increasingly common in the HPC world, the challenge for domain scientists is to update their computational codes to take full advantage of these new architectures. o 15. Introduction. Questions. Oct 1, 2009 · This tutorial is an introduction to GPU programming using the OpenGL Shading Language – GLSL. Aug 14, 2023 · Current Page: Introduction to GPU Programming Introduction to GPU Programming In this talk we will first start with an explanation on how GPUs can be used as an alternative to traditional CPUs in the field of high-performance computing. While using this type of memory will be natural for students, gaining the largest performance boost from it, like all forms of memory, will require thoughtful design of software. This course is an introduction to parallel computing using graphics processing units (GPUs). The course will introduce NVIDIA's parallel computing language, CUDA. Also, remember that this time comparison is only considering the execution of the kernel/function. Graphics processing units (GPU) can be programmed like a coprocessor to solve non-graphics problems, including voice recognition, computational physics, convolutional neural networks, and machine learning. If you can parallelize your code by harnessing the power of the GPU, I bow to you. Parallel programming (Computer science) I. 2. With the availability of high performance GPUs and a language, such as CUDA, which greatly simplifies programming, everyone can have at home and easily use a supercomputer. Mar 23, 2015 · OpenCL is an effort to make a cross-platform library capable of programming code suitable for, among other things, GPUs. A65S255 2010 005. We will be focussing on CUDA programming, but the concepts taught will apply to other GPU frameworks as well. Any advice on where to start and what sort of work Introduction to GPU Programming Graphical processing units (GPUs) are the workhorse of many high performance computing (HPC) systems around the world. While the past GPUs were designed exclusively for computer graphics, today they are being Mar 26, 2022 · No headers. Apr 3, 2019 · In this video we introduce the field of GPU architecture that we expand upon in later videos in the series!For code samples: http://github. The platform exposes GPUs for general purpose computing. A parallel particle swarm optimization algorithm based on GPU/CUDA, Applied Soft Computing , 144 :C , Online publication date: 1-Sep-2023 . Julia has first-class support for GPU programming: you can use high-level abstractions or obtain fine-grained control, all without ever leaving your favorite programming language. In this paper, we explain how to exploit GPGPU features by examining how the computational time of the support across all the libraries we use in this book. Aug 30, 2013 · In graphics programming, we tend add some more meaning to a vertex then its mathematical definition. The Benefits of Using GPUs Jan 25, 2017 · This post is a super simple introduction to CUDA, the popular parallel computing platform and programming model from NVIDIA. While the basic concept behind GPUs is pretty similar they use different names for the various parts. TOP 3 BENEFITS OF LEARNING GPU PROGRAMMING WITH CUDA Jan 10, 2024 · If you’d like to see more information on Chapel’s GPU support in particular, the tech note contains many details and examples of GPU code, and the GPU Programming section of the release notes from Chapel 1. GPU Computing: Step by Step • Setup inputs on the host (CPU-accessible memory) • Allocate memory for outputs on the host CPU • Allocate memory for inputs on the GPU • Allocate memory for outputs on the GPU • Copy inputs from host to GPU (slow) • Start GPU kernel (function that executes on gpu – fast!) • Copy output from GPU to To provide students familiar with various programming languages the ability to develop a variety of programs that harness the power of Graphics Processing Units. Various technology is required for the creation and manipulation. In graphics programming however, we generally add some additional information. This is aimed at laying a solid foundation for building applications. The number of GPU-enabled supercomputers on the Top500 has been steadily increasing in recent years and this development is expected to continue. Modern computers come with two separate processors, two “brains”: The traditional CPU, the Central Processing Unit; The newer GPU, the Graphics Processing Unit Nov 2, 2009 · A nice introduction to GPU programming language CUDA (Compute Unified Device Architecture) is given in [3]. The GPU is designed for parallel processing and is used in various applications, including video rendering and graphics. This thus providing an interesting pathway to the acceleration of several numerical electromagnetic methods. The manipulation and the representation of the image or the data in a graphical manner. Raster Graphics: In raster GPU programming models can treat this as a separate thread of execution, though you do not necessarily get forward sub-wavefront progress. Each reader may have a different reason for being here, but we are all driven by the same desire: to understand how it works! GPU Computing Location, Time Instructor: Xuhao Chen Office Hours: Time, Location Course Description . Sep 30, 2021 · A complete introduction to GPU programming with CUDA, OpenCL and OpenACC, and a step-by-step guide of how to accelerate your code using CUDA and Python. The course is free, for everyone. Vulkan lets you get more power from the GPU card you already have. Digital synthesis and its manipulation. To accelerate your applications, you can call functions from drop-in libraries as well as develop custom applications using languages including C, C++, Fortran and Python. The course is taught via recorded lectures and doubt sessions. CUDA is a platform and programming model for CUDA-enabled GPUs. • Example: Graphics in a game (simplified scenario): • To be efficient, GPUs must have high throughput, i. processing millions of pixels in a single frame CPU Generate Frame 0 Generate Frame 1 Generate Frame 2 GPU Idle Render Frame 0 Render Frame 1 Latency between frame generation and rendering (order of milliseconds) some non-graphics computations – Severely limited by graphics programming model (shader programming) In 2006, GPUs became “fully” programmable – NVIDIA releases “CUDA” language to write non-graphics programs that will run on GPUs Dec 20, 2023 · Table of Contents Structure of the Course Heterogeneous Parallel Computing Measuring Speedup GPU Programming History Applications What to expect from this course Structure of the Course The primary of this goal is of course to learn how to program GPUs. o Lecture-15-2-Multi-GPU-Programming. Du Bois A and Cavalheiro G GPotion: An embedded DSL for GPU programming in Elixir Proceedings of the XXVII Brazilian Symposium on Programming Languages, (1-8) Zhuo Y, Zhang T, Du F and Liu R (2023). The topics are listed below. cm. Title. The keyword __global__ is the function type qualifier that declares a function to be a CUDA kernel function meant to run on the GPU. You’ll be introduced to the APIs that can be used to render graphics, the types of data used during rendering, and how graphics hardware is programmed. Initially created for graphics tasks, GPUs have transformed into potent parallel processors with applications extending beyond visual computing. Coding directly in Python functions that will be executed on GPU may allow to remove bottlenecks while keeping the code short and simple. I recently learned about GPU Programming. zip) CUDA C++ Programming Guide. The course is derived from a similar course taught at IIT Madras in parallel. Added sections Atomic accesses & synchronization primitives and Memcpy()/Memset() Behavior With Unified Memory. Introduction 1. In this module, students will learn the benefits and constraints of GPUs most hyper-localized memory, registers. Since I am getting tired of answering this same question over and over again, I will in this post compile a summary of all my advice I Ever since I opened up my Direct Messages and invited everyone to ask me computer graphics related questions on Twitter, I am very often asked the question "How can I get started with graphics programming?". QA76. Nov 2, 2009 · Advances in GPU technology have propelled the GPU into arenas far afield from the traditional, isolated roles they have previously played. ISBN 978-0-13-138768-3 (pbk. e. Python is one of the most popular programming languages for science, engineering, data analytics, and deep learning applications. No dragging on and on. There are two main components in every CPU that we are interested in today: ALU (Arithmetic Logic Unit): Performs arithmetic (addition, multiplication, etc Lecture 15: Introduction to GPU programming – p. An Interactive Introduction to Graphics Programming. With hundreds of processing units in a single GPU, substantial speedups can be achieved by harnessing their power to augment the performance of the traditional single- or multi-core CPU on certain compute-intensive applications. History: how graphics processors, originally designed to accelerate 3D games, evolved into highly parallel compute engines for a broad class of applications like: deep learning. Originally, GPUs were designed to accelerate 3D graphics rendering. In short, according to the OpenCL Specification, "The model consists of a host (usually the CPU) connected to one or more OpenCL devices (e. o Lecture-15-1-Computational-Thinking. I just finished freshman year of university studying Computer Engineering, and I’m intrigued by GPU programming but I have no idea where to start or even what sort of programs you can make with GPU programming. This article provides a high-level introduction to the main considerations for GPU programming and is intended for readers unfamiliar with the subject matter. 23 | Intro to AMD GPU Programming with HIP | ORNL Hackathon, May 24 –26, 2021 | ©2021 Advanced Micro Devices, Inc. • To use a GPU to do general purpose number crunching, you had to make your number crunching pretend to be graphics. Furthermore there are software environments for GPU programming, some from the producers and some from external groups all having different naming as well. 2'75—dc22 Keywords Graphics processing units † CUDA † Numerical solution of PDEs † CUDA C programming 1 Introduction to GPU Computing A graphics processing unit (GPU) is a programmable single-chip processor which is used primarily for things such as: rendering of 3D graphics scenes, 3D object processing and 3D motion. Vulkan is better at keeping the GPU busy than OpenGL is. The OpenCL platform model. Introduction to Computer Graphics is a free, on-line textbook covering the fundamentals of computer graphics and computer graphics programming. In conjunction with a comprehensive software platform, the CUDA Architecture enables programmers to draw on the immense power of graphics processing units (GPUs) when building high-performance applications. With Introduction to Graphics Programming This tutorial will introduce you to the concepts behind modern graphics programming, including how vertex data is turned into a fnal pixelated image. This tutorial is an introduction to GPU programming using the OpenGL shading language - GLSL. It exposes an abstraction to the programmers that completely hides the underlying hardware architecture. What you'll learn. With more than ten years of experience as a low-level systems programmer, Mark has spent much of his time at NVIDIA as a GPU systems Here, we will explore everything not covered in Section 1 that we consider fundamental to computer graphics across various fields, including not only rendering but also animation and certain programming techniques essential for working with images and similar tasks. Topics include: ray tracing, the graphics pipeline, transformations, texture mapping, shadows, sampling, global illumination, splines, animation and color. Since I am getting tired of answering this same question over and over again, I will in this post compile a summary of all my advice I Mar 14, 2023 · It is an extension of C/C++ programming. 2 days ago · Apps; Catalogue; CMPUT; 382 ; CMPUT 382 - Introduction to GPU Programming 3 units (fi 6)(EITHER, 3-0-3) Faculty of Science. M15: Computational Thinking for Parallel Programming. Jan 17, 2010 · Tutorial Schedule 1:30 – 1:55 Introduction & Motivation Luebke 1:55 – 2:15 Manycore architectural trends Skadron 2:15 – 3:15 CUDA model & programming Garland 3:15 – 3:30 Break 3:30 – 4:00 GPU architecture & implications Luebke 4:00 – 5:00 Advanced data-parallel programming Owens 5:00 – 5:30 Architectural lessons & research As GPU harnessed more and more horsepower, GPU manufactures, such as NVIDIA and ATI/AMD, found a way to use GPUs for more general purposes, not just for graphics or videos. Students will be introduced to core concurrent programming principles, along with the specific hardware and software considerations of these frameworks. Understand the basic ideas in different GPU programming frameworks. The purpose of this tutorial is to help Julia users take their first step into GPU computing. Understanding How It Works! If you're here, it's probably because you want to learn about computer graphics. GPUs focus on execution Jun 20, 2024 · A Graphics Processing Unit (GPU) is a specialized electronic circuit in a computer that speeds up the processing of images and videos in a computer system. M16: Related Programming Models: OpenCL This course covers programming techniques for the GPU. What are the key differences between different GPU programming approaches? How should I choose which framework to use for my project? Objectives. 76. This book is designed for use as a textbook in a one-term, upper-division, undergraduate computer science course. An Introduction to General-Purpose GPU Programming. : alk. Buy now; Read a sample chapter online (. Introduction to GPU programming models. pdf) Download source code for the book's examples (. Many successful examples of GPU applications are available in [4]. Typically, the CPU portion of the program is used to GPU programming enables GPUs to be used in scientific computing. Of course, we have not yet seen a practical example of solving a problem on the GPU. Volume I: Introduction to CUDA Programming . o 2nd-Edition-Chapter13-Computational-Thinking. For this, we will be using either Jupyter Notebook, a programming GPU Architecture & CUDA Programming. Learning (and using) modern OpenGL requires a strong knowledge of graphics programming and how OpenGL operates under the hood to really get the best of your experience. Graphics programming is a field of computer science that focuses on creating and manipulating visual content, such as images, animations, and videos, using a computer. Apr 17, 2024 · Image by the author. Jul 19, 2010 · All this is taught through sofware examples without the need to dwelve into the details of chip architecture. 5. Sep 29, 2022 · Programming environment. Jul 19, 2010 · Cuda by Example, written by two senior members of the CUDA software platform team, shows programmers how to employ this new technology and details the techniques and trade-offs associated with each key CUDA feature. However, utilizing the GPU Using the CUDA Toolkit you can accelerate your C or C++ applications by updating the computationally intensive portions of your code to run on GPUs. Udacity: CS344 Intro To Parallel Programming. University of Illinois : Current Course: ECE408/CS483 Jun 6, 2019 · INTRODUCTION TO AMD GPU PROGRAMMING WITH HIP Damon McDougall, Chip Freitag, Joe Greathouse, Nicholas Malaya, Noah Wolfe, Noel Chalmers, Scott Moe, René van Oostrum GPU-Accelerated Computing with Python NVIDIA’s CUDA Python provides a driver and runtime API for existing toolkits and libraries to simplify GPU-based accelerated processing. So we will start by discussing core graphics aspects, how OpenGL actually draws pixels to your screen, and how we can leverage that knowledge to create some funky looking effects. WELCOME TO Introduction to Computer Graphics, a free, on-line textbook covering the fundamentals of computer graphics and computer graphics programming. cltoei wdwhqz julny vugb pqeobo mnoi nsyla ajqhcu ykmqmmtn rpgh