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Course Outline

Introduction

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

Overview of CUDA Features and Architecture

  • GPU vs CPU architecture.
  • Understanding SIMT (Single Instruction, Multiple Threads).
  • CUDA programming model.

Setting up the Development Environment

  • Installing CUDA Toolkit and drivers.
  • Installing Python and Numba.
  • Setting up and verifying the environment.

Parallel Programming Fundamentals

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

Working with the Numba Compiler

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

Building a Custom CUDA Kernel

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

Memory Management

  • Types of GPU memory (global, shared, local, constant).
  • Memory transfer between host and device.
  • Optimizing memory usage and avoiding bottlenecks.

Advanced Topics in GPU Acceleration

  • Shared memory and synchronization.
  • Using streams for asynchronous execution.
  • Multi-GPU programming basics.

Converting CPU-based Applications to GPU

  • Profiling CPU code.
  • Identifying parallelizable sections.
  • Porting logic to CUDA kernels.

Troubleshooting

  • Debugging CUDA applications.
  • Common errors and how to resolve them.
  • Tools and techniques for testing and validation.

Summary and Next Steps

  • Review of key concepts.
  • Best practices in GPU programming.
  • Resources for continued learning.

Requirements

  • Experience with Python programming.
  • Familiarity with NumPy (ndarrays, ufuncs, etc.).

Audience

  • Developers.
 14 Hours

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