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NVIDIA GPU Programming - Extended培訓

 
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課程大綱
 
  • 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
 
  備案號:備案號:滬ICP備08026168號-1 .(2024年07月24日)....................
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