A performance-focused C++ project exploring how implementation choices affect computational cost when processing large numbers of tasks.

This project compares two task-processing pipelines:

• Naive implementation using dynamic allocations and pointer indirection
• Optimized implementation using contiguous storage and cache-friendly data layout

The goal is to demonstrate how memory layout and allocation strategy influence throughput and processing efficiency.

Many systems that process large workloads — schedulers, pipelines, simulators, and runtime engines — depend heavily on how data is structured in memory.

Even when the logical workload is identical, different implementation strategies can result in substantial performance differences.

This project provides a simple experimental framework for measuring those effects.

• Allocation overhead vs contiguous storage
• Pointer indirection vs direct memory access
• Cache locality effects
• Throughput differences from implementation strategy
• Benchmark discipline and reproducible measurement

• Heap allocation per task
• Pointer-based storage
• Less cache-friendly memory access

• Contiguous task storage
• Fewer allocations
• Cache-friendly iteration
• Reduced indirection overhead

Benchmarks process multiple task counts:

• 100,000 tasks
• 500,000 tasks
• 1,000,000 tasks

Each test runs multiple iterations to improve timing stability.

Metrics recorded:

• Total processing time
• Relative speedup

Speedups of ~3–5x observed depending on workload size.

This illustrates how implementation mechanics can significantly affect system performance.

cpp-task-processing-engine/
├── include/
│ └── task_engine.hpp
├── src/
│ ├── main.cpp
│ └── task_engine.cpp
├── scripts/
│ └── plot_results.py
├── results/
│ ├── benchmark_results.csv
│ ├── processing_time_comparison.png
│ └── speedup_by_task_count.png
└── README.md

g++ -O2 -std=c++17 src/*.cpp -Iinclude -o engine_benchmark

./engine_benchmark

python scripts/plot_results.py

./scripts/run_benchmarks.sh

In performance-sensitive systems such as:

• Schedulers
• Simulators
• Data pipelines
• High-throughput services

Implementation choices can significantly influence computational cost.

Understanding these tradeoffs is essential for building efficient systems.

Possible next steps:

• Multithreaded processing
• Custom allocator experiments
• Cache-line alignment tests
• Lock-free data structures
• Profiling and flamegraph analysis

MIT