🚀 Advanced Guide to GPUs and Parallel Computing

💻 Hardware Architecture

Processing Units Comparison Matrix

Feature	CPU	GPU	TPU	FPGA
Purpose	General	Graphics/Parallel	AI/ML	Configurable
Clock Speed	⚡ High	🔸 Medium	🔸 Medium	🔸 Medium
Cores	🔸 Few	⚡ Many	⚡ Many	📊 Variable
Cache	⚡ High	🔸 Low	🔸 Medium	🔸 Low
Latency	⚡ Low	🔸 High	🔸 Medium	⚡ Very Low
Throughput	🔸 Low	⚡ High	⚡ High	⚡ Very High
Power Usage	🔸 Medium	🔸 High	🔸 Medium	⚠️ Very High

🎮 NVIDIA Evolution

From Gaming to AI Revolution

Timeline

graph LR
    A[1990s] --> B[GeForce]
    B --> C[CUDA]
    C --> D[Tesla]
    D --> E[Modern GPUs]

⚡ Deep Learning Performance

Why GPUs Excel in Deep Learning?

graph TD
    A[Parallel Processing] --> B[Matrix Operations]
    B --> C[High Throughput]
    C --> D[Faster Training]
    A --> E[Multiple Cores]
    E --> F[Concurrent Execution]

🔧 CUDA Programming Flow

sequenceDiagram
    participant CPU
    participant GPU
    CPU->>CPU: Allocate Memory
    CPU->>GPU: Copy Data
    GPU->>GPU: Execute Kernel
    GPU->>CPU: Return Results

📘 Key Terminology

Essential Concepts

Kernel: GPU-specific functions
Thread/Block/Grid: Execution hierarchy
GEMM: Matrix multiplication operations
Host/Device: CPU/GPU terminology

Memory Hierarchy

graph TD
    A[Global Memory]
    A1[Shared Memory]
    A2[L2 Cache]
    A1a[Registers]
    A1b[L1 Cache]
    B[Host Memory]

    A --> A1
    A --> A2
    A1 --> A1a
    A1 --> A1b
    B --> A