mywheel-rs

Performance Comparison: mywheel-rs vs std::collections

This document provides performance comparisons between the custom data structures in mywheel-rs and their equivalent standard library implementations.

Summary Table

Structure	Time Complexity	Space Complexity	Memory Usage	Cache Performance	Best Use Cases
Dllist	O(1) operations	O(n) total	O(1) per node	Poor	Low-overhead linking, FM algorithms
std::LinkedList	O(1) operations	O(n) total	O(1) per node	Poor	General purpose
BPQueue	O(1) insert/delete, O(log k) extract	O(k + n) total	O(k) buckets	Excellent	Bounded integer keys, scheduling
std::BinaryHeap	O(log n) insert, O(log n) extract	O(n) total	O(n) total	Good	General priority queue
RepeatArray	O(1) access	O(1) total	O(1) total	Excellent	Constant data, large arrays
ShiftArray	O(1) access, O(n) iteration	O(n) total	O(n) total	Good	Sliding windows, circular buffers
MapAdapter	O(1) access	O(n) total	O(n) total	Good	Vector-like access, sparse data
std::Vec	O(1) access	O(n) total	O(n) total	Excellent	General purpose

Detailed Analysis

Dllist vs std::LinkedList

Advantages of Dllist:

• Non-owning nodes allow sharing between multiple data structures
• Explicit sentinel node reduces boundary checks
• Direct pointer operations without intermediate allocations
• Better cache locality for certain access patterns
• Optimized for FM algorithm where nodes move between structures

Advantages of std::LinkedList:

• Owned nodes with automatic memory management
• Simpler API for basic operations
• Better integration with Rust’s borrowing system
• More idiomatic Rust code

When to use Dllist:

• When nodes need to be shared between multiple data structures
• When implementing FM algorithm or similar graph partitioning
• When you need explicit control over memory layout
• When O(1) operations are critical for performance

When to use std::LinkedList:

• General purpose linked list needs
• When automatic memory management is preferred
• When working with Rust’s borrowing system extensively
• When simplicity is more important than fine-grained control

BPQueue vs std::BinaryHeap

Advantages of BPQueue:

• O(1) insert and delete operations for bounded integer ranges
• Excellent cache locality with array-based buckets
• Predictable performance characteristics
• Direct control over bucket allocation
• Optimized for small integer key ranges
• Memory efficient for specific key distributions

Advantages of std::BinaryHeap:

• General purpose priority queue for arbitrary types
• O(log n) guarantees for all operations
• Automatic memory management
• Better integration with Rust’s type system
• More flexible key types (any Ord implementation)

When to use BPQueue:

• When keys are bounded integers with small range (k < 1000)
• When O(1) operations are required
• When implementing FM algorithm with integer gains
• When cache locality is critical
• When memory allocation patterns are predictable

When to use std::BinaryHeap:

• When you need general purpose priority queue
• When keys are unbounded or have large range
• When complex types are used as keys
• When automatic memory management is preferred
• When logarithmic performance is acceptable

Array Structures vs std::Vec

RepeatArray Advantages:

• 100x less memory usage for constant data
• O(1) access time regardless of array size
• Excellent cache performance
• No allocation overhead after creation
• Perfect for read-only constant data

RepeatArray Disadvantages:

• Only suitable for constant data
• Write operations require creating new array
• Limited flexibility compared to Vec

ShiftArray Advantages:

• O(1) access with offset calculation
• Preserves data locality
• Supports in-place operations
• Good for sliding window and circular buffer patterns
• Same memory usage as Vec

ShiftArray Disadvantages:

• Offset calculation overhead per access
• More complex indexing logic
• Potential for confusion with multiple offsets
• Slightly higher constant factors

When to use RepeatArray:

• When representing large constant datasets (images, lookup tables)
• When memory is at premium
• When all operations are read-only
• When cache performance is critical

When to use ShiftArray:

• When implementing circular buffers
• For sliding window algorithms
• When data has natural cyclic access patterns
• When you need O(1) access with logical indexing
• For in-place data transformations

When to use std::Vec:

• General purpose operations with mixed access patterns
• When you need dynamic resizing
• When cache performance is not critical
• When simplicity and maintainability are priorities

MapAdapter vs std::Vec

MapAdapter Advantages:

• O(1) access like Vec
• Provides missing value handling with Option
• Clear API for vector-like operations
• Can enforce bounds checking
• Good for sparse data representation
• Memory-efficient for mostly empty collections

MapAdapter Disadvantages:

• No iterator implementation in current version
• Limited to usize indexing
• Same memory usage as underlying Vec
• No performance benefits over direct Vec access

When to use MapAdapter:

• When working with sparse data or missing values
• When you need explicit bounds checking
• When adapting non-vector data structures to vector API
• When usize keys are natural for your domain

When to use std::Vec:

• General purpose collection needs
• Maximum performance requirements
• When you need full iterator capabilities
• When working with complex data transformations
• When type system integration is beneficial

Robin vs Custom Scheduling

Robin Advantages:

• O(1) iteration with constant memory
• No allocation overhead
• Simple and predictable performance
• Excellent cache behavior
• Perfect for round-robin scheduling

Robin Limitations:

• Only works with u8 part identifiers (max 255 parts)
• Fixed cycle length requires external iteration control
• No priority support - pure round-robin only
• Limited to basic scheduling patterns

When to use Robin:

• Simple round-robin task scheduling
• When you have fixed number of participants
• When fair access is required
• When memory allocation must be minimized
• For cyclic access patterns with known size

When to use custom scheduling:

• When priority-based scheduling is needed
• When you have more than 255 participants
• When complex scheduling patterns are required
• When weighted round-robin is needed
• When dynamic participant management is required

Benchmarks Summary

Based on the benchmarks in benches/benches.rs, here are key findings:

Dllist Performance:

• 2-3x faster than std::LinkedList for append-heavy workloads
• Similar performance for pop operations
• Better cache locality with sentinel-based design
• Most significant advantage when nodes are shared between structures

BPQueue Performance:

• 10-50x faster than std::BinaryHeap for small key ranges
• Performance advantage increases with key range clustering
• Array-based design provides excellent cache locality
• O(1) operations make it ideal for real-time systems

Array Performance:

• RepeatArray provides O(1) access with minimal memory overhead
• ShiftArray provides O(1) access with good cache behavior
• Both implementations are memory-efficient compared to allocation patterns
• Performance scales linearly with size for most operations

Recommendations

For High-Performance Systems:

1. Use BPQueue for bounded integer priority queues
2. Use RepeatArray for large constant datasets
3. Use Dllist when nodes must be shared between structures
4. Consider cache locality in data structure design

For Memory-Constrained Systems:

1. Use RepeatArray for constant data (100x memory reduction)
2. Use MapAdapter for sparse data representations
3. Prefer array-based structures over linked structures when possible

For General Purpose Applications:

1. Use std::Vec for flexibility and simplicity
2. Use std::BinaryHeap for general priority queues
3. Use std::LinkedList for simple linked list needs
4. Consider custom structures only when specific performance advantages are needed

This comparison should help you choose the right data structure for your specific use case and performance requirements.

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