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:

1. Input Assembly: Collects vertex data from the CPU.
2. Geometry Processing: Transforms vertices into 3D shapes.
3. Rasterization: Converts 3D objects into 2D pixels.
4. Fragment Processing: Adds textures, colors, and effects.
5. 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.