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開(kāi)課地址：【上海】同濟(jì)大學(xué)(滬西)/新城金郡商務(wù)樓(11號(hào)線白銀路站)【深圳分部】：電影大廈(地鐵一號(hào)線大劇院站) 【武漢分部】：佳源大廈【成都分部】：領(lǐng)館區(qū)1號(hào)【沈陽(yáng)分部】：沈陽(yáng)理工大學(xué)【鄭州分部】：錦華大廈【石家莊分部】：瑞景大廈【北京分部】：北京中山學(xué)院 【南京分部】：金港大廈
最新開(kāi)班 (連續(xù)班 、周末班、晚班）：2020年3月16日 |
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課程大綱 |
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- Introduction
- Understanding the Fundamentals of Heterogeneous Computing Methodology
- Why Parallel Computing? Understanding the Need for Parallel Computing
- Multi-Core Processors - Architecture and Design
- Introduction to Threads, Thread Basics and Basic Concepts of Parallel Programming
- Understanding the Fundamentals of GPU Software Optimization Processes
- OpenMP - A Standard for Directive-Based Parallel Programming
- Hands on / Demonstration of Various Programs on Multicore Machines
- Introduction to GPU Computing
- GPUs for Parallel Computing
- GPUs Programming Model
- Hands on / Demonstration of Various Programs on GPU
- SDK, Toolkit and Installation of Environment for GPU
- Working with Various Libraries
- Demonstration of GPU and Tools with Sample Programs and OpenACC
- Understanding the CUDA Programming Model
- Learning the CUDA Architecture
- Exploring and Setting Up the CUDA Development Environments
- Working with the CUDA Runtime API
- Understanding the CUDA Memory Model
- Exploring Additional CUDA API Features
- Accessing Global Memory Efficiently in CUDA: Global Memory Optimization
- Optimizing Data Transfers in CUDA Using CUDA Streams
- Using Shared Memory in CUDA
- Understanding and Using Atomic Operations and Instructions in CUDA
- Case Study: Basic Digital Image Processing with CUDA
- Working with Multi-GPU Programming
- Advanced Hardware Profiling and Sampling on NVIDIA / CUDA
- Using CUDA Dynamic Parallelism API for Dynamic Kernel Launch
- Summary and Conclusion
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