Computer Architecture

This section contains comprehensive documentation about computer architecture, organized into clear categories covering CPU, GPU, and ARM architectures.

📁 Directory Structure

🖥️ CPU Architecture

Comprehensive CPU architecture documentation:

Instruction Sets

x86 Instructions: x86 instruction set architecture and implementation
Intel AMX: Advanced Matrix Extensions for AI workloads
Instruction Encoding: Low-level instruction formats and encoding

Pipeline & Performance

Pipeline Design: CPU pipeline architecture and optimization
IBS (Instruction-Based Sampling): AMD performance analysis technology
Performance Monitoring: Hardware performance counters and analysis

Memory Systems

Cache Systems: Multi-level cache hierarchy and optimization
Virtual Memory: Memory management, paging, and address translation
MMU (Memory Management Unit): Hardware memory management
NUMA Architecture: Non-Uniform Memory Access systems

🎮 GPU Computing

GPU architecture and parallel computing:

GPU Architecture: CUDA, SIMT, and parallel processing models
GPU Communication: Inter-GPU communication and memory systems
AI Acceleration: GPU optimization for machine learning workloads
Research Papers: Latest GPU architecture research

🔧 ARM Architecture

ARM processor architecture and programming:

ARM Instructions: ARM instruction set and assembly programming
Inline Assembly: ARM inline assembly programming techniques
Architecture Variants: Different ARM processor families

🚀 Key Topics Covered

CPU Architecture Deep Dive

Instruction Set Architectures: x86, ARM instruction sets and their implementations
Pipeline Design: Superscalar, out-of-order execution, branch prediction
Memory Hierarchy: Cache design, virtual memory, memory consistency models
Performance Analysis: Hardware performance monitoring and optimization

GPU Computing & Parallel Processing

CUDA Programming: GPU programming models and optimization
Memory Systems: GPU memory hierarchy and bandwidth optimization
AI Workloads: GPU acceleration for machine learning and deep learning

ARM Systems

ARM Assembly: Low-level ARM programming and optimization
System Programming: ARM-specific system-level programming techniques

📊 Performance & Optimization

Memory System Optimization

Cache Optimization: Cache-friendly algorithms and data structures
Memory Bandwidth: Optimizing memory access patterns
NUMA Awareness: Optimizing for NUMA architectures

Parallel Computing

GPU Programming: CUDA, OpenCL, and compute shaders
CPU Parallelization: Multi-threading and vectorization
Heterogeneous Computing: CPU-GPU cooperation

🔬 Research & Advanced Topics

Cutting-Edge Research

Value Prediction: Advanced CPU optimization techniques
Memory Prefetching: Hardware and software prefetching strategies
AI Hardware: Specialized hardware for machine learning

Performance Analysis Tools

Hardware Counters: Using PMU for performance analysis
Profiling Tools: perf, Intel VTune, AMD CodeXL
Benchmarking: Performance measurement methodologies

📚 Learning Path

Fundamentals: Start with basic CPU architecture and instruction sets
Memory Systems: Understand cache hierarchy and virtual memory
Performance: Learn performance analysis and optimization techniques
Parallel Computing: Explore GPU computing and parallel algorithms
Advanced Topics: Dive into research papers and cutting-edge techniques

This comprehensive architecture documentation provides both theoretical understanding and practical knowledge for system-level programming, performance optimization, and hardware-aware software development.