C++ Weekly: Exploring Polymorphic Memory Resources (PMR) for Performance Optimization
0:00 Introduction: The episode focuses on using allocators in C++ to improve performance, particularly in game development.
0:23 Allocator Expertise: Jason admits not being an allocator expert, but he shares insights gained from a video by Pablo Halpern and Alisdair Meredith on using allocators transparently.
1:21 C++17 Compatibility: The demonstrated techniques work with C++17 and later, making them widely accessible.
1:36 Polymorphic Memory Resource (PMR): The episode introduces PMR, a powerful tool for managing memory allocation.
1:44 Example with Vector: Jason shows how to create a vector that uses a custom memory resource allocated on the stack.
2:04 Stack-Based Allocation: A memory buffer is created on the stack to store the vector's data.
2:18 Using monotonic_buffer_resource: This PMR class manages the stack-based memory buffer.
3:09 Sharing Memory: Multiple vectors can be created using the same stack-based memory resource, reducing dynamic allocations and improving performance.
4:58 Quick Bench Benchmark: A benchmark comparing a standard list with a list using PMR demonstrates a 3.6x speed improvement for the PMR list.
6:34 Comparison with Vector: A standard vector is significantly faster than the PMR list (7.6x), highlighting vector's general efficiency.
7:01 PMR with Vector: Using PMR with a vector can still provide a performance boost (2.3x faster than a standard vector) by reducing heap allocations.
7:53 Realistic Scenario: The most realistic use case involves reusing the same memory resource for multiple objects, potentially of different types, within a larger system.
8:46 Benchmark with Multiple Objects: The benchmark shows that the performance gains of PMR are still noticeable even when creating multiple lists and vectors.
9:17 Map and Unordered_Map: Jason briefly mentions that standard map and unordered_map can be slow due to dynamic allocations per node.
9:30 Conclusion: The episode encourages viewers to explore PMR and its potential for performance optimization, noting that it offers significant benefits with relatively little programming overhead.