Learning Objectives

At the conclusion of the workshop, you’ll have an understanding of the fundamental tools and techniques for GPU-accelerated Python applications with CUDA and Numba:

• GPU-accelerate NumPy ufuncs with a few lines of code.
• Configure code parallelization using the CUDA thread hierarchy.
• Write custom CUDA device kernels for maximum performance and flexibility.
• Use memory coalescing and on-device shared memory to increase CUDA kernel bandwidth.

Topics Covered

The following topics and technologies are covered in this course:

• CUDA Python with Numba
• CUDA programming general practices

Course Outline

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.

Break (60 mins)

‍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.

Break (15 mins)

‍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.

Duration: 08:00

Subject: Generative AI/LLM

Language: English

Course Prerequisites:

• A basic understanding of Deep Learning Concepts.
• Familiarity with a Deep Learning framework such as TensorFlow, PyTorch, or Keras. This course uses PyTorch.

Tools, libraries, frameworks used: PyTorch, CLIP

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