Lecture on CUDA and NVIDIA GPUs

Introduction to CUDA

• Originally, NVIDIA GPUs were used primarily for rendering pixels.
• Ian Buck proposed using GPUs for fluid mechanics, leading to the development of CUDA.
• CUDA aims to unify heterogeneous computing (parallel and serial computing).

Evolution of GPU and CUDA

• GPUs have evolved from mostly fixed-function hardware to predominantly programmable hardware.
• CUDA was developed to make graphics cards usable for general computing tasks beyond rendering.
• Initial CUDA hardware was 90% fixed function; now it's 90% programmable.

Heterogeneous Computing

• Programs have sections suited for GPU (parallel) and CPU (serial) processing.
• CUDA helps distribute tasks between CPU and GPU efficiently.

Applications and Similarities Across Fields

• CUDA applies to fluid mechanics, AI, and other computational fields.
• Similar computational problems are found in graphics, fluid mechanics, and AI.
• AI focuses more on linear algebra, performance tuning, and optimization compared to supercomputing.

CUDA Software Stack

• Written in C, CUDA started as a language and compiler for GPU programming.
• It has grown into a suite with libraries and APIs for various applications (e.g., image processing, AI).
• Allows easy integration with other languages like Python.

Integration and Abstraction

• CUDA integrates CPU and GPU, making them appear as one system.
• Developers can assign tasks to CPU or GPU based on the nature of the task.
• Provides tools to simplify data direction in programs.

Libraries and APIs

• CUDA offers around 900 libraries for AI models, scientific computing, data analysis, etc.
• Developers choose libraries based on their data and application needs.

Backward Compatibility and Security

• Backward compatibility is a priority; old CUDA versions still run on current hardware.
• Security is critical, leading to developments like confidential computing for encrypted communication between CPU and GPU.

CUDA as a Runtime

• Acts like a kernel, managing commands between software and hardware.
• Translates high-level commands into hardware instructions.

Conclusion

• CUDA has maintained compatibility and relevance through hardware evolution.
• NVIDIA invests in ensuring CUDA's presence across chips and versions.
• Confidential computing and security are integral to CUDA's development.

Overall, CUDA's journey from a simple language to a comprehensive computing platform highlights its importance in modern computational tasks across various fields.