Java Sorting Algorithms Benchmark & Energy Analysis

This project benchmarks the performance and energy usage of four core sorting algorithms implemented in Java: Bubble Sort, Merge Sort, Quick Sort, and Counting Sort.
The goal was to evaluate both runtime complexity and energy consumption, using a controlled experimental setup and system-level power measurement tools.

This project was completed as part of a university assignment at University College Dublin.

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📌 Algorithms Implemented

✔️ Bubble Sort — O(n²)

Simple but inefficient; used as baseline comparison.

✔️ Merge Sort — O(n log n)

Stable, divide-and-conquer algorithm with consistent performance.

✔️ Quick Sort — O(n log n) average

Fastest algorithm in most scenarios (randomized pivot).

✔️ Counting Sort — O(n + k)

Linear-time sorting for bounded integer ranges.

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🧪 Experiment Setup

• Inputs tested under:
  • Sorted (best case)
  • Reverse sorted (worst case)
  • Random integers
• Input sizes up to 500,000 elements
• Hardware:
  • ASUS Vivobook
  • AMD Ryzen 7 5700U
• Energy measurement tools:
  • HWINFO64 (GPU & CPU wattage sampling)
• Each test repeated between 10–30 times for consistency

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📊 Research Poster

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The poster highlights:

• Time complexity comparison
• Energy usage across all algorithms
• Large-scale performance charts
• Key findings and conclusions

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📂 Project Structure

/src
  BubbleSort.java
  MergeSort.java
  QuickSort.java
  CountingSort.java
  GenerateCSV.java
  Main.java
README.md

Each algorithm is implemented clearly and tested using a CSV-based output system for chart plotting.

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🚀 Running the Project

Clone the repository:

git clone cd

Compile:

javac src/*.java

Run benchmark:

java -cp src Main

This will generate CSV files containing runtime results for analysis and plotting.

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🧠 Key Findings

• Quick Sort was the fastest AND most energy-efficient overall
• Merge Sort performed consistently well across all input types
• Counting Sort was efficient, but energy usage varied noticeably
• Bubble Sort consumed the most time and energy by a large margin
• Strong correlation observed between time complexity and energy consumption

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🎓 Educational Value

This project demonstrates:

• Practical algorithm performance evaluation
• Understanding of asymptotic complexity
• Benchmark design and measurement
• Energy-aware computing concepts
• Clean, modular Java implementation

It is ideal for:

• Interview preparation
• Understanding real-world algorithm behaviour
• Learning how energy usage correlates with computational complexity