GPU History:
The history of GPUs is an incredible tale of technological advancements that have reshaped industries like gaming, AI, and high-performance computing. Here's a look at its key milestones:
1976: Atari 2600 launched with a custom graphics chip, pioneering early hardware for gaming consoles.
1985: Commodore Amiga introduced the advanced Agnus chip for multitasking and smooth 2D graphics.
1995: 3dfx Voodoo GPU debuted, revolutionizing gaming with hardware-accelerated 3D rendering.
1999: NVIDIA launched the GeForce 256, hailed as the first "GPU" with hardware transform and lighting.
2000: ATI (now AMD) released the Radeon DDR, advancing competition with higher memory bandwidth.
2006: NVIDIA introduced CUDA, enabling GPUs to handle general-purpose computing tasks.
2010: AMD released the Radeon HD 5000 series, making DirectX 11 mainstream in gaming.
2016: NVIDIA's Pascal GPUs (e.g., GTX 1080) became the fastest, most efficient gaming GPUs.
2018: NVIDIA launched the RTX series, introducing real-time ray tracing to gaming.
2020: NVIDIA’s A100 GPU redefined AI and HPC performance for massive workloads.
2023: AMD and NVIDIA expanded energy-efficient GPU offerings, crucial for gaming, AI, and cloud computing.
How General GPU architecture looks like ?
1. Core Components
- Streaming Multiprocessors (SMs):
- These are the building blocks of a GPU, housing thousands of smaller processing units (CUDA cores for NVIDIA, or Stream Processors for AMD) to handle parallel tasks.
Shader Units:
Perform calculations for rendering pixels, vertices, and lighting effects.
- Vertex Shaders: Process 3D object vertices.
- Pixel (Fragment) Shaders: Handle coloring, textures, and effects at the pixel level.
- Tensor Cores:
- Found in modern GPUs, these are specialized units for AI and deep learning tasks, accelerating matrix operations.
- Ray Tracing Cores:
- Dedicated hardware (in GPUs like NVIDIA RTX) to compute realistic lighting and shadows through ray tracing.
2. Memory System
- Video Memory (VRAM): High-speed memory (e.g., GDDR6, HBM) used to store textures, frame buffers, and 3D models for quick access by the GPU.
- Memory Interface: The bus connecting the GPU to VRAM, with wider interfaces (e.g., 256-bit or 512-bit) allowing faster data transfer.
3. Graphics Pipeline
The GPU processes data through a pipeline with multiple stages:
- Input Assembly: Collects vertex data from the CPU.
- Geometry Processing: Transforms vertices into 3D shapes.
- Rasterization: Converts 3D objects into 2D pixels.
- Fragment Processing: Adds textures, colors, and effects.
- Output Merger: Combines results to render the final image.
4. Compute Units
- GPUs include computing units (CUs) to handle general-purpose tasks like scientific simulations, machine learning, and cryptocurrency mining.
5. Memory Hierarchy
- Shared Memory: High-speed memory for communication within multiprocessors.
- Global Memory: Accessible by all GPU cores, but slower than shared memory.
- Caches (L1/L2): Speed up data access by reducing latency during computations.
6. Connectivity
- PCIe Interface:Connects the GPU to the motherboard, enabling data exchange between the CPU and GPU.
7. Power Management
- Modern GPUs include sophisticated power and thermal management systems to optimize performance and energy efficiency.