Memory Management Patterns in High-Performance C++

Objective: Master advanced memory management techniques used in high-performance systems like llama.cpp, focusing on RAII, custom allocators, memory mapping, and efficient resource management.

Overview

This exercise explores memory management patterns used in production systems like llama.cpp, which handles multi-gigabyte AI models efficiently. You'll implement a simplified version of a memory allocator that demonstrates key concepts from real-world C++ systems.

Learning Goals

• Master RAII (Resource Acquisition Is Initialization) principles
• Understand modern C++ memory management patterns
• Implement custom allocators and memory mappers
• Apply smart pointers and move semantics effectively
• Handle large data structures efficiently

Background: llama.cpp Memory Management

llama.cpp uses sophisticated memory management to handle large language models efficiently:

1. Memory Mapping (mmap): Maps model files directly into memory
2. Custom Allocators: Optimizes memory allocation patterns
3. RAII Wrappers: Ensures proper resource cleanup
4. Reference Counting: Manages shared tensor data

Let's implement a simplified version of these patterns!

Exercise 1: RAII Memory Mapper

Successfully mapped 1048576 bytes
Moved mapper size: 1048576

Exercise 2: Smart Pointer Tensor System

Created tensor with 6 elements
Created tensor with 4 elements
Tensor1 sum: 21
Tensor2 sum: 10
Moved tensor
Added tensor to manager, total: 1
Moved tensor
Added tensor to manager, total: 2
Total memory: 40 bytes
Retrieved tensor sum: 21
Freeing tensor data
Freeing tensor data

Exercise 3: Memory Pool Allocator

Created memory pool of 1024 bytes
Allocated 4 bytes at offset 0
TestData(42) constructed
Allocated 4 bytes at offset 8
TestData(42) constructed
Data1: 100
Data2: 200
Pool usage: 1.5625%
Moved data: 100
TestData(200) destroyed
TestData(100) destroyed
After destruction, pool usage: 1.5625%
Reset memory pool
After reset, pool usage: 0%

Mental Model Development

Key Concepts to Internalize

1. RAII Ownership: Resources are acquired in constructors and released in destructors
2. Move Semantics: Prefer moving expensive objects over copying
3. Smart Pointers: Use unique_ptr for exclusive ownership, shared_ptr for shared ownership
4. Memory Mapping: Direct file-to-memory mapping for efficient large data access
5. Custom Allocators: Pool allocation for performance-critical code

Design Patterns from llama.cpp

1. Resource Manager Pattern: Centralized resource management
2. Factory Pattern: Controlled object creation
3. Strategy Pattern: Different backends (CPU, GPU, Metal)
4. RAII Wrapper Pattern: Safe resource handling

Performance Considerations

1. Memory Locality: Keep related data close in memory
2. Alignment: Proper alignment for SIMD operations
3. Pool Allocation: Reduce fragmentation and allocation overhead
4. Copy Avoidance: Use move semantics and references

Advanced Challenge

Combine all concepts to create a ModelLoader class that:

• Uses memory mapping for model files
• Implements a tensor pool allocator
• Manages tensors with smart pointers
• Provides efficient loading and unloading

This exercise demonstrates the sophisticated memory management techniques used in production C++ systems like llama.cpp, preparing you for high-performance C++ development.