Beyond CUDA: GPU Accelerated Python on Cross-Vendor Graphics Cards with Vulkan & Kompute
Many advanced data processing paradigms fit incredibly well to the parallel-architecture that GPU computing offers resulting in the continuously growing adoption of graphics card for general purpose computing, and the Python programming language has been at the forefront of this trend. Exciting advancements in the open source Vulkan Project are enabling developers to take advantage of general purpose GPU computing capabilities in cross-vendor mobile and desktop GPUs including AMD, Qualcomm, NVIDIA & friends.
This talk will provide practical insights on high performance GPU computing in Python using the Vulkan Kompute framework. We will cover the trends in GPU processing, the architecture of Vulkan Kompute, we will implement a simple parallel multiplication example, and we will then dive into a machine learning example building a logistic regression model from scratch which will run in the GPU.
In more detail these are the topics of the talk:
• Motivations
• High level overview of the OSS Vulkan initative enabling cross-vendor GPU computing
• The Python Kompute Framework and its architecture which augments Vulkan
• A simple Python Kompute example implementing a parallel array multiplication
• An advanced Python Kompute example implementing a parallel array multiplication
• Further resources & further reading
A more in-depth version of this talk can be found in this blog post:
