Project 15: Performance-Optimized Data Structures

High-performance data structures (hash table, arena allocator, ring buffer) with cache-conscious design and benchmarking.

Quick Reference

Attribute Value
Primary Language C
Alternative Languages None
Difficulty Level 5 - Master
Time Estimate See main guide
Knowledge Area Data Structures, Performance
Tooling GCC, perf, Valgrind (cachegrind)
Prerequisites See main guide

What You Will Build

High-performance data structures (hash table, arena allocator, ring buffer) with cache-conscious design and benchmarking.

Why It Matters

This project builds core skills that appear repeatedly in real-world systems and tooling.

Core Challenges

  • Cache-conscious design → Maps to data layout for cache efficiency
  • Memory allocation strategies → Maps to arena and pool allocators
  • Benchmarking accuracy → Maps to measuring real performance

Key Concepts

  • Map the project to core concepts before you code.

Real-World Outcome

# 1. Hash table benchmark
$ ./bench hash_table
Hash Table Benchmark (1M operations):
Implementation      | Insert   | Lookup   | Delete   | Memory
--------------------|----------|----------|----------|--------
Naive chaining      | 245 ms   | 189 ms   | 201 ms   | 48 MB
Open addressing     | 112 ms   | 87 ms    | 95 ms    | 32 MB
Robin Hood          | 98 ms    | 65 ms    | 78 ms    | 32 MB

Cache statistics (Robin Hood):
  L1 cache misses: 12,345 (vs 89,012 for naive)
  L3 cache misses: 1,234 (vs 15,678 for naive)

# 2. Arena allocator
$ ./bench arena
Arena vs malloc (100K small allocations):
                    | Time     | Fragmentation | Free Time
--------------------|----------|---------------|----------
malloc/free         | 15 ms    | 23%          | 12 ms
Arena allocator     | 2 ms     | 0%           | 0.1 ms (bulk)

# 3. Ring buffer throughput
$ ./bench ring_buffer
SPSC Ring Buffer (producer-consumer):
  Message size: 64 bytes
  Buffer size: 4096 entries
  Throughput: 25M messages/second
  Latency (p99): 150 ns

Implementation Guide

  1. Reproduce the simplest happy-path scenario.
  2. Build the smallest working version of the core feature.
  3. Add input validation and error handling.
  4. Add instrumentation/logging to confirm behavior.
  5. Refactor into clean modules with tests.

Milestones

  • Milestone 1: Minimal working program that runs end-to-end.
  • Milestone 2: Correct outputs for typical inputs.
  • Milestone 3: Robust handling of edge cases.
  • Milestone 4: Clean structure and documented usage.

Validation Checklist

  • Output matches the real-world outcome example
  • Handles invalid inputs safely
  • Provides clear errors and exit codes
  • Repeatable results across runs

References

  • Main guide: PROFESSIONAL_C_PROGRAMMING_MASTERY.md
  • Mastering Algorithms with C by Kyle Loudon