Course Outline
Introduction
Grasping the Fundamentals of Heterogeneous Computing Methodology
Rationale for Parallel Computing: Understanding the Need
Multi-Core Processors: Architecture and Design
Introduction to Threads, Thread Basics, and Core Concepts of Parallel Programming
Comprehending 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
Leveraging GPUs for Parallel Computing
GPU Programming Model
Hands-on Demonstration of Various Programs on GPU
SDK, Toolkit, and Environment Installation 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
Efficient Global Memory Access in CUDA: Global Memory Optimization
Optimizing Data Transfers in CUDA Using CUDA Streams
Utilizing 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
- Proficiency in C Programming
- Experience with Linux GCC
Testimonials (1)
Trainers energy and humor.
