Inside the GPU: Architecture and Parallelism

Unpacking the specialized design that makes GPUs excel at massive parallel computation, from their core units to memory systems.

The GPU's Inner Workings

While we've explored how GPUs handle tasks differently from CPUs, understanding their true power requires a look under the hood. A GPU isn't just one big processor; it's a complex system of many specialized components working in concert to achieve unparalleled parallel processing capabilities.

GPU Block Diagram

graph TD A[Input Data] --> B(GPU Die); B --> C(Streaming Multiprocessors); C --> D(CUDA Cores / Stream Processors); D --> E(Registers); C --> F(Shared Memory); B --> G(L1/L2 Caches); B --> H(Global Memory / VRAM); H --> B; D --> I(Texture Units); D --> J(ROPs); J --> K[Output: Rendered Frame / Computed Result]; style A fill:#f0f9ff,stroke:#0ea5e9,stroke-width:2px style K fill:#f0f9ff,stroke:#0ea5e9,stroke-width:2px style B fill:#e0f2fe,stroke:#38bdf8,stroke-width:2px style C fill:#e0f2fe,stroke:#38bdf8,stroke-width:2px style D fill:#e0f2fe,stroke:#38bdf8,stroke-width:2px style E fill:#e0f2fe,stroke:#38bdf8,stroke-width:2px style F fill:#e0f2fe,stroke:#38bdf8,stroke-width:2px style G fill:#e0f2fe,stroke:#38bdf8,stroke-width:2px style H fill:#e0f2fe,stroke:#38bdf8,stroke-width:2px style I fill:#e0f2fe,stroke:#38bdf8,stroke-width:2px style J fill:#e0f2fe,stroke:#38bdf8,stroke-width:2px

Streaming Multiprocessors (SMs)

At the heart of a GPU are its Streaming Multiprocessors (SMs), or Compute Units (CUs) in AMD's terminology. Think of each SM as a mini-processor, capable of handling a large number of threads simultaneously. A modern GPU can have dozens to over a hundred SMs, each operating independently.

Interactive: Inside an SM

Each SM contains many individual processing cores. Click "Run Simulation" to see how tasks are distributed and processed in parallel within a single SM.

Streaming Multiprocessor (SM)

Input Data
Output

GPU Memory Hierarchy

Efficient data access is crucial for parallel processing. GPUs employ a sophisticated memory hierarchy to ensure that the thousands of cores have fast access to the data they need.

Memory Speed vs. Size

Registers Fastest, Smallest (per thread) Shared Memory Very Fast, Small (per SM) L1/L2 Cache Fast, Medium (per SM/GPU) Global Memory (VRAM) Slower, Largest (GPU-wide)

Arrows indicate data flow from faster, smaller memory to slower, larger memory.

Workload Management: Warps and Schedulers

To keep thousands of cores busy, GPUs don't manage individual threads. Instead, they group threads into warps (NVIDIA) or wavefronts (AMD). All threads within a warp execute the same instruction simultaneously, but on different data. This is known as SIMT (Single Instruction, Multiple Thread).

Interactive: Warp Execution

Observe how a "warp" of threads moves through an SM, executing instructions in lockstep.

Streaming Multiprocessor (SM) Instruction Fetch Scheduler Execution Units Memory Access Write Back Warp (32 Threads)

The Symphony of Parallelism

The GPU's internal architecture is a masterclass in parallel design. By combining many simpler processing units, a hierarchical memory system, and efficient workload management, GPUs can tackle problems that are inherently parallel with incredible speed. This design has not only revolutionized graphics but also unlocked new possibilities in AI, scientific computing, and beyond.