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坚持小班授课，为保证培训效果，增加互动环节，每期人数限3到5人。 |
| 上课时间和地点 |
开课地址：【上海】同济大学(沪西)/新城金郡商务楼(11号线白银路站)【深圳分部】：电影大厦(地铁一号线大剧院站) 【武汉分部】：佳源大厦【成都分部】：领馆区1号【沈阳分部】：沈阳理工大学【郑州分部】：锦华大厦【石家庄分部】：瑞景大厦【北京分部】：北京中山学院 【南京分部】：金港大厦
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| 实验设备 |
☆资深工程师授课
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| 质量保障 |
1、培训过程中，如有部分内容理解不透或消化不好，可免费在以后培训班中重听；
2、课程完成后,授课老师留给学员手机和Email,保障培训效果,免费提供课后答疑。
3、培训合格学员可享受免费推荐就业机会。 |
课程大纲 |
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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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