Microsoft Word – COMP8551 OpenCL Lab.docx COMP 8551 OpenCL Lab Exercises 1. Review the HelloWorld example provided. 2. Write a simple application that keeps track of the grades for students in a class. The application consists of two main tasks: a. Obtain a class average by averaging the final grades of all students. b. Compute […]
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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
Matrix Multiplication in CUDA with Shared Memory Paul Richmond This document provides explanation as to how to adapt the Lab06 starting code (link) to implement a shared memory Matrix Multiplication using CUDA. Explanation for the Starting Code Figure 1 – Naive CUDA Matrix Multiply Exercise one asks you to modify an implementation of a naive
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PowerPoint Presentation Parallel Computing with GPUs Dr Paul Richmond http://paulrichmond.shef.ac.uk/teaching/COM4521/ Context and Hardware Trends Supercomputing Software and Parallel Computing Course Outline Context of course 0.0 TFlops 1.0 TFlops 2.0 TFlops 3.0 TFlops 4.0 TFlops 5.0 TFlops 6.0 TFlops 7.0 TFlops 8.0 TFlops 9.0 TFlops 10.0 TFlops 1 CPU Core GPU (4992 cores) 8.74 TeraFLOPS ~40
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Com 4521 Parallel Computing with GPUs: Lab 07 Spring Semester 2018 Dr Paul Richmond Lab Assistants: John Charlton and Robert Chisholm Department of Computer Science, University of Sheffield Learning Outcomes Understanding the limitations of atomic operations on the Kepler architecture. Understanding how to use recursive kernel calls to implement a parallel reduce type
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Com4521/Com6521: Parallel Computing with GPUs Assignment: Part 2 Deadline: Tuesday 15th May 2018 17:00 (week 12) Last Edited: 09/03/2018 Marking Assignment 2 (of 2) is worth 70% of the total assignment mark. The total assignment mark (both parts 1 and 2) is worth 80% of the total module mark. Assignment 2 marks will be weighted
程序代写代做代考 Excel GPU compiler cache cuda Com4521/Com6521: Parallel Computing with GPUs Read More »
PowerPoint Presentation Parallel Computing with GPUs: Introduction to CUDA Dr Paul Richmond http://paulrichmond.shef.ac.uk/teaching/COM4521/ This lecture CUDA Programming Model CUDA Device Code CUDA Host Code and Memory Management CUDA Compilation and execution in Visual Studio Programming a GPU with CUDA Application Code GPU CPU Use GPU to Parallelize Compute-Intensive Functions Rest of Sequential CPU Code GPU
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cse3431-lecture1.key CSE 3431— Fall 2018 Introduction to 3D Computer Graphics Instructor: Petros Faloutsos Teaching Assistant: Irfa Nisar Applications of Computer Graphics Entertainment • Computer games • Films • Virtual reality Scientific visualization • Medical visualization • Flight simulation • Architecture • Information visualization Education Movies To reality and beyond ! Movies Special Effects Movies Compositing
并行实现（Parallelisation 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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Lab2 The game loop and animations In this lab is divided in three parts. · In the first part we will explore the software design for implementing a character in your game. · In the second part we will go trough the process of animating objects using the update method. · In the third part