Overview
This lecture discusses the history, architecture, and evolution of CUDA, Nvidia's parallel computing platform, and how it supports heterogeneous computing across CPUs and GPUs for applications like graphics, AI, and scientific computing.
Origins of CUDA
- Nvidia GPUs originally focused on rendering graphics and pixels.
- Ian Buck proposed using GPUs for general computation (fluid mechanics) and later led the creation of CUDA at Nvidia.
- Early CUDA had limited programmability; now it enables broader parallel computing.
Heterogeneous Computing
- Programs have serial (CPU) and parallel (GPU) components; CUDA unifies these.
- CPUs handle tasks like file I/O and web requests; GPUs handle parallel tasks like image processing.
- This mix is called heterogeneous computing.
Evolution of GPU Hardware
- Early GPUs: 90% fixed-function hardware, 10% programmable.
- Modern GPUs: ~90% programmable, enabling complex tasks beyond graphics.
- Graphics, AI, and fluid mechanics computation share similar underlying algorithms.
CUDA Platform and Ecosystem
- CUDA started as a C-based language and compiler for GPU programming.
- Now, CUDA includes APIs, libraries (AI, image processing), frameworks, and SDKs—around 900 libraries/models.
- Supports interoperability with languages like Python via high-level libraries.
- CUDA abstracts the differences between many types of Nvidia hardware.
Compatibility and Security
- CUDA maintains backward compatibility; code from version 1.0 still runs today.
- Ensures new hardware supports legacy CUDA applications.
- Security is enforced via "confidential computing," enabling encrypted data transfer between CPU and GPU.
Software and Hardware Integration
- CUDA acts like a runtime: interprets high-level commands, manages hardware control, and dispatches tasks.
- Sits between diverse software applications and varying GPU hardware models.
- Functions similarly to a kernel by managing instructions between software and hardware.
Key Terms & Definitions
- GPU (Graphics Processing Unit) — Specialized processor for rendering graphics and parallel computing tasks.
- CUDA (Compute Unified Device Architecture) — Nvidia's software platform for general-purpose GPU programming.
- Heterogeneous Computing — Combining CPUs (serial tasks) and GPUs (parallel tasks) in one program.
- Confidential Computing — Secured, encrypted data exchange between CPU and GPU to protect sensitive information.
- Runtime — The software layer that manages execution of code and communication between applications and hardware.
Action Items / Next Steps
- Review CUDA programming basics, focusing on launching tasks on CPU vs GPU.
- Explore examples of CUDA usage in Python and C/C++.
- Read about confidential computing and security practices in heterogeneous computing systems.