Fundamentals of Accelerated Computing with CUDA Python (FACCP)

Voraussetzungen

• Basic Python competency, including familiarity with variable types, loops, conditional statements, functions, and array manipulations
• NumPy competency, including the use of ndarrays and ufuncs
• No previous knowledge of CUDA programming is required

Detaillierter Kursinhalt

Introduction

• Meet the instructor.
• Create an account at https://learn.nvidia.com/join

Introduction to CUDA Python with Numba

• Begin working with the Numba compiler and CUDA programming in Python.
• Use Numba decorators to GPU-accelerate numerical Python functions.
• Optimize host-to-device and device-to-host memory transfers.

Custom CUDA Kernels in Python with Numba

• Learn CUDA’s parallel thread hierarchy and how to extend parallel program possibilities.
• Launch massively parallel custom CUDA kernels on the GPU.
• Utilize CUDA atomic operations to avoid race conditions during parallel execution.

Multidimensional Grids, and Shared Memory for CUDA Python with Numba

• Learn multidimensional grid creation and how to work in parallel on 2D matrices.
• Leverage on-device shared memory to promote memory coalescing while reshaping 2D matrices.

Final Review

• Review key learnings and wrap up questions.
• Complete the assessment to earn a certificate.
• Take the workshop survey.