When does class start/end?
Classes begin promptly at 9:00 am, and typically end at 5:00 pm.
Fundamentals of Accelerated Computing with CUDA Python
This workshop teaches you the fundamental tools and techniques for running GPU-accelerated Python applications using CUDA® GPUs and the Numba compiler. You’ll work though dozens of hands-on coding...
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$500 USD
Course Code
NV-ACC-CUDA-PYTH
Duration
1 day
Available Formats
Classroom
This workshop teaches you the fundamental tools and techniques for running GPU-accelerated Python applications using CUDA® GPUs and the Numba compiler. You’ll work though dozens of hands-on coding exercises and, at the end of the training, implement a new workflow to accelerate a fully functional linear algebra program originally designed for CPUs, observing impressive performance gains. After the workshop ends, you’ll have additional resources to help you create new GPU-accelerated applications on your own.
Skills Gained
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.
Prerequisites
- 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
Course Details
Workshop Outline
Introduction
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
