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Course Outline
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
Requirements
- C Programming
- Linux GCC
21 Hours
Custom Corporate Training
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- Customized Content: We adapt the syllabus and practical exercises to the real goals and needs of your project.
- Flexible Schedule: Dates and times adapted to your team's agenda.
- Format: Online (live), In-company (at your offices), or Hybrid.
Price per private group, online live training, starting from 4800 € + VAT*
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