Lecture Notes: Evolution and Impact of CUDA by Nvidia
Introduction to CUDA
Around 20 years ago, GPUs by Nvidia were primarily used for rendering pixels.
Ian Buck, during his PhD, envisioned using GPUs for fluid mechanics and normal computing, leading to the creation of CUDA.
CUDA was developed to turn graphics processing into general-purpose computing.
Development of CUDA
Initial stages of CUDA were barely programmable, evolved over time with increased programmability.
Originally, GPUs were 90% fixed function hardware and 10% programmable; now it's the reverse.
CUDA allows for heterogeneous computing, efficiently utilizing both CPUs and GPUs.
Application Areas
Graphics: Despite its general-purpose use, CUDA retains strong graphics capabilities (e.g., ray tracing, procedural textures).
AI and Supercomputing: Similar computational needs in graphics, AI, and fluid mechanics. AI focuses more on linear algebra and performance optimization.
Evolution of Algorithms: Algorithms for AI, supercomputing, and other fields often overlap.
CUDA's Language and Tools
Written in C, CUDA started as a language and compiler but has grown into a comprehensive suite of tools and libraries.
Provides libraries for image processing, AI, and interface with other programming languages like Python.
Offers abstraction, enabling easy usage of GPUs from high-level languages.
Integration and Compatibility
CUDA sees CPU and GPU as one integrated unit for seamless program execution.
Maintains backward compatibility; any version of CUDA runs on current hardware.
Nvidia ensures all hardware developments consider CUDA's compatibility and functionality.
Software and Hardware Interaction
CUDA acts as a runtime environment, similar to a kernel, mediating between high-level software and low-level hardware operations.
Supports diverse hardware ranging from GeForce cards to mobile computing devices.
Handles the complexity of operating system differences and varied hardware configurations.
Security and Confidential Computing
Nvidia emphasizes security through features like confidential computing for encrypted communication between CPU and GPU.
Important for protecting valuable AI models and data integrity.
Conclusion
CUDA has grown from simple beginnings into a comprehensive platform that supports a wide array of applications and devices.
Nvidia is committed to CUDA’s continuous evolution and integration, ensuring its relevance and utility across all computing platforms.