General Instructions on Assignment Submission General Instructions on Assignment Submission Sungkil Lee Sungkyunkwan University Table 1: Quick checklist for submission of your assignments. Subject Item Penalty Platform Windows+VS (or Linux+GCC) no credits Program an executable should run without crash no credits no C++ compilation errors no credits no C++ compilation warnings -10 pt OpenGL use […]
PowerPoint Presentation Parallel Computing with GPUs: CUDA Memory Dr Paul Richmond http://paulrichmond.shef.ac.uk/teaching/COM4521/ Previous Lecture and Lab We started developing some CUDA programs We had to move data from the host to the device memory We learnt about mapping problems to grids of thread blocks and how to index data Memory Hierarchy Overview Global Memory Constant
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COMP8551 Optimization COMP 8551 Advanced Games Programming Techniques Software Optimization Borna Noureddin, Ph.D. British Columbia Institute of Technology Overview •Optimization: • Overview • Design techniques •Parallelization: • Partitioning • Profiling • General techniques 2 © B or na N ou re dd in C O M P 85 51 Memory optimization Motivation Hero casts a
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PowerPoint Presentation Images and Displays Computer Graphics Instructor: Sungkil Lee 2 Today • Digital Images • Raster Graphics • Vector Graphics • Displays 3 Images • Image: • (continuous) 2D distribution of intensity or color, defined as a 2-D function 𝑓 𝑥, 𝑦 at spatial coordinates (𝑥, 𝑦) • 𝑓 𝑥, 𝑦 is the intensity
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PowerPoint Presentation Getting Started with OpenGL: Hello Triangle Computer Graphics Instructor: Sungkil Lee 2 Today • Draw a colored triangle on the screen in a window Overview 4 Event-Driven Programming • OpenGL rendering is performed event-driven way • Similar to GUI programming (e.g., Windows API, QT, Web) • After initializations, the program enters an infinite
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MapReduce and Hadoop Lecture 2: MapReduce and Frequent Itemsets Prof. Michael R. Lyu Computer Science & Engineering Dept. The Chinese University of Hong Kong 1 CMSC5741 Big Data Tech. & Apps. 1 Outline Introduction The Hadoop Distributed File System (HDFS) MapReduce Hadoop Hadoop Streaming Problems Suited for MapReduce TensorFlow Frequent Itemsets Conclusion 2 Introduction Much
COMP6714 Project Specification (stage 2) October 4, 2018 1 COMP6714 18s2 Project 2 Stage 2: Modify a baseline model of hyponymy classification 2.1 Deadline and Late Penalty The project deadline is 23:59 26 Oct 2018 (Fri). Late penalty is -10% each day for the first three days, and then -20% each day afterwards. 2.2 Objective
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Course Syllabus: Computer Graphics Fall, 2018, SWE3008 Instructor: Prof. Sungkil Lee (이성길) Computer Graphics Lab. College of Software, Sungkyunkwan University Time and Place: Mon. 10:30–11:45, Wed. 09:00–10:15, 26312 Office Hour: Wed., 10:30–11:30 at my office (27328) Teaching Assistants (TAs): Hyojin Jung (정효진), cglab.skku@gmail.com Course Web page: http://cg.skku.edu/course/cg/ Official language: English Course Summary Computer graphics is
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Parallelization approach 方法1：对每个像素分配一个线程，然后对每个c*c的块进行归一，下为一个c*c的归一过程，在全局内存中操作，不考虑线程块 图1 这样做的缺点是，图中过程1只有1/4线程工作，过程2只有1/16线程工作，以此类推 方法1实现到最后发现有跨块问题，大块mosaic计算出错，且速度慢，没有继续修改。 方法2：分步骤，每次归一4个数 1、 先将数据复制到另外分配的无符号整型数据位置（否则会溢出）cuda_pre函数 2、 每2*2使用1个线程进行求和，放在原始的被2整除的位置，cuda_2函数 3、 每4*4使用1个线程进行求和，放在原始的被4整除的位置，cuda_2函数 4、 ……. 5、 将最终数据平均后，扩散分配输出至各对应位置cuda_after cuda_pre未优化的： __global__ void cuda_pre(unsigned char *ptrOut, unsigned int *ptrTemp, unsigned char *ptrIn, int numrow, int numcol) { unsigned int tidx = threadIdx.x; unsigned int tidy = threadIdx.y; unsigned int x = tidx + blockDim.x*blockIdx.x; unsigned int y =
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PowerPoint Presentation Parallel Computing with GPUs Dr Paul Richmond http://paulrichmond.shef.ac.uk/teaching/COM4521/ Assignment Feedback Last Week We learnt about warp level CUDA How threads are scheduled and executed Impacts of divergence Atomics: Good and bad… Do the warp shuffle! Parallel primitives Scan and Reduction Credits The code and much of the content from this lecture is based
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