Course Outline

Introduction

  • What is GPU programming?
  • Why use CUDA with Python for government applications?
  • Key concepts: Threads, Blocks, Grids

Overview of CUDA Features and Architecture for Government Use

  • Comparison of GPU and CPU architecture
  • Understanding SIMT (Single Instruction, Multiple Threads)
  • CUDA programming model

Setting up the Development Environment for Government Projects

  • Installing CUDA Toolkit and drivers
  • Installing Python and Numba
  • Configuring and verifying the environment

Fundamentals of Parallel Programming for Government Applications

  • Introduction to parallel execution
  • Understanding threads and thread hierarchies
  • Working with warps and synchronization mechanisms

Utilizing the Numba Compiler for Government Projects

  • Introduction to Numba
  • Writing CUDA kernels using Numba
  • Understanding @cuda.jit decorators

Building a Custom CUDA Kernel for Government Use

  • Developing and launching a basic kernel
  • Using threads for element-wise operations
  • Managing grid and block dimensions effectively

Memory Management in GPU Programming for Government Applications

  • Types of GPU memory (global, shared, local, constant)
  • Data transfer between host and device
  • Optimizing memory usage to enhance performance

Advanced Topics in GPU Acceleration for Government Projects

  • Shared memory and synchronization techniques
  • Using streams for asynchronous execution
  • Basics of multi-GPU programming

Converting CPU-based Applications to GPU for Enhanced Performance in Government

  • Profiling CPU code to identify bottlenecks
  • Identifying sections that can be parallelized
  • Porting logic to CUDA kernels efficiently

Troubleshooting and Debugging for Government Applications

  • Debugging techniques for CUDA applications
  • Common errors and resolution strategies
  • Tools and methodologies for testing and validation

Summary and Next Steps for Government Developers

  • Review of key concepts in GPU programming
  • Best practices for effective GPU programming in government
  • Resources for continued learning and development

Requirements

  • Python programming experience
  • Experience with NumPy (ndarrays, universal functions, etc.)

Audience for government

  • Developers
 14 Hours

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