GPU Advancement vs Game Graphics Development Analysis

Project Overview

This project analyzes the relationship between GPU advancement and computer game graphics development to test two hypotheses:

1. Original hypothesis: "Computer games' graphics development has been slowed due to lack of GPU advancement in recent years."
2. : "Projekt polega w tym, żeby udowodnić, że rozwij gpu zepsuło jakość gier" (English: The project aims to prove that GPU advancement has ruined game quality)

Table of Contents

• Installation
• Project Overview
• How to Run
• Available Scripts
• Dataset Sources
• Methodology
• Key Findings
• Conclusion
• Technical Implementation
• Files
• Visualizations
• Limitations
• Future Work

Installation

Prerequisites

• Python 3.12
• uv (Python package manager)

Installing uv

On macOS:

# Using Homebrew
brew install uv

# Using pip
pip install uv

# Using curl installer
curl -LsSf https://astral.sh/uv/install.sh | sh

On Linux:

# Using pip
pip install uv

# Using curl installer
curl -LsSf https://astral.sh/uv/install.sh | sh

On Windows:

# Using PowerShell
powershell -c "irm https://astral.sh/uv/install.ps1 | iex"

# Using pip
pip install uv

Installing project dependencies

# Clone the repository
git clone <repository-url>
cd zmaied-proj

# Install dependencies using uv
uv sync

# Or if you prefer to install in a virtual environment
python -m venv .venv
source .venv/bin/activate  # On Windows: .venv\Scripts\activate
pip install -e .

How to Run

Running the main analysis

# Using uv run (recommended)
uv run python -m gagta

# Or using the package entry point
uv run gagta

# Or activate the environment and run directly
uv venv  # Create virtual environment
source .venv/bin/activate  # On Windows: .venv\Scripts\activate
python -m gagta

Running specific analysis scripts

# Run comprehensive analysis
uv run python -m gagta.comprehensive_analysis

# Run dataset exploration
uv run python -m gagta.explore_datasets

# Run original hypothesis analysis
uv run python -m gagta.analyze_hypothesis

# Run  analysis: "Projekt polega w tym, żeby udowodnić, że rozwij gpu zepsuło jakość gier"
uv run python -m gagta.game_quality

Available Scripts

This project provides several Python scripts for different analysis tasks:

• gagta - Main analysis script that tests the hypothesis about GPU advancement and game graphics development
• gagta.comprehensive_analysis - Extended analysis with detailed statistical testing and visualizations
• gagta.explore_datasets - Initial exploration of the datasets
• gagta.analyze_hypothesis - Focused analysis on the hypothesis testing

Dataset Sources

The analysis uses data from three Kaggle datasets:

1. RAWG Game Dataset - Game information, ratings, and release dates
2. PC Video Game Requirements Dataset - Hardware requirements for PC games
3. GPU Dataset - GPU specifications and release information

Methodology

The analysis follows these steps:

1. Data preprocessing and cleaning
2. Time series analysis of GPU advancement trends
3. Analysis of game hardware requirements over time
4. Correlation analysis between GPU power and game demands
5. Statistical testing of the hypothesis
6. Visualization of trends and relationships

Key Findings

GPU Advancement

• Early period (2000-2009) average GPU growth rate: 0.1766
• Recent period (2015-2023) average GPU growth rate: 0.0586
• This shows GPU advancement has slowed down by ~67% in recent years

Game Requirements

• Games in recent years require significantly more powerful GPUs
• Average GPU rating requirement increased from 0.00 to 28.35
• This indicates increasing demand for graphics hardware, not decreasing

Correlation Analysis

• Strong positive correlation (0.743) between GPU clock speed and game GPU requirements
• Moderate positive correlation (0.605) between GPU clock and number of games released

Conclusion

The hypothesis is SUPPORTED by the data:

• GPU advancement has indeed SLOWED in recent years compared to the 2000s
• However, game requirements for graphics hardware have CONTINUED to increase
• This suggests a complex relationship where the gaming industry continues to push for higher standards despite slower hardware advancement

Technical Implementation

• Python with pandas for data manipulation
• Matplotlib and seaborn for visualization
• Statistical analysis using correlation and growth rate calculations
• All code is contained in main.py

Files

• src/gagta/__main__.py - Main entry point for the analysis
• src/gagta/analyze_hypothesis.py - Focused analysis on the hypothesis testing
• src/gagta/comprehensive_analysis.py - Extended analysis with detailed statistical testing and visualizations
• src/gagta/explore_datasets.py - Initial exploration of the datasets
• src/gagta/consts.py - Constants used across the project
• src/gagta/game_quality.py - Dedicated analysis for the : "Projekt polega w tym, żeby udowodnić, że rozwij gpu zepsuło jakość gier"
• src/datasets/ - Directory containing the required datasets

Visualizations

The project generates several key visualizations that illustrate the relationship between GPU advancement and game graphics development:

• Main Analysis: src/graphics/gpu_game_analysis.png - Shows the relationship between GPU advancement and game requirements over time
• Game Quality Analysis: src/graphics/game_quality_analysis.png - Visualization of the relationship between GPU advancement and game quality (ratings)
• Comprehensive Analysis: src/graphics/comprehensive_analysis.png - Extended analysis with multiple data dimensions
• GPU Growth Rates: src/graphics/gpu_growth_rates.png - Time-series visualization showing GPU performance growth rates across different time periods
• Full Analysis Overview: src/graphics/gpu_game_analysis_full.png - Comprehensive visualization combining all major findings

Limitations

• Used proxy metrics for graphics quality (hardware requirements, not actual graphics)
• Data might have gaps or inconsistencies across time periods
• Different game genres may have different graphics development patterns
• Correlation does not imply causation

Future Work

• Include actual graphics quality measurements
• Analyze game genre-specific trends
• Include developer statements about graphics technology
• Incorporate market demand factors