Explore how the CPU and GPU collaborate during gaming workloads, focusing on identifying and analyzing bottlenecks. This project serves as a comprehensive toolkit for analyzing CPU-GPU interaction patterns in gaming workloads through simulation, real-time monitoring, and performance analysis.

1. Overview

2. Features

   • Gaming Workload Simulation
   • Real-Time Performance Monitoring
   • Automated Profiling
   • Performance Analysis

3. Requirements

4. Installation

5. Usage

   • Running the Workload Simulation
   • Starting Performance Monitoring
     • Initial Setup
     • Idle Monitoring
     • Active Game Monitoring

6. Simulation Results

   • GPU-Accelerated Implementation Results
   • CPU-Only Implementation Results

7. Real Game Performance Analysis: Rocket League

   • Resource Utilization Patterns
   • Game Session Statistics
   • Performance Metrics
   • GPU Performance
   • System Performance
   • Comparison with Phase 1 Simulation

8. Final Thoughts

9. Future Studies and Improvements

   • Simulation Expansion
   • Extended Profiling
   • Tool Enhancement

10. Project Structure

11. Performance Metrics

12. Contributing

13. Acknowledgments

This project provides tools to:

• Simulate realistic gaming workloads on both CPU and GPU
• Monitor real-time performance metrics during gameplay
• Automate performance profiling
• Analyze CPU-GPU parallelism and bottlenecks
• Generate detailed performance reports and visualizations

• CPU-based simulation with multi-threading support
• GPU-accelerated simulation using CUDA
• Configurable parameters:
  • Resolution (720p to 1440p)
  • CPU thread count (1-8)
  • Workload intensity (light/medium/heavy)
• Simulates:
  • Graphics rendering (matrix operations)
  • Physics calculations
  • AI pathfinding

• Live CPU and GPU metrics tracking
• FrameView integration for FPS monitoring
• Metrics logged:
  • FPS and frame times
  • CPU/GPU utilization
  • Memory usage
  • Temperature and power draw
  • Thread utilization

• Tmux-based monitoring interface
• Automated benchmark execution
• CSV logging of performance metrics
• Support for multiple game configurations

• Bottleneck identification
• CPU-GPU parallelism efficiency analysis
• Resource utilization patterns
• Performance visualization
• Optimization recommendations

• Python 3.8+
• NVIDIA GPU with CUDA support
• Required Python packages:

  numpy cupy pandas matplotlib psutil pynvml 

• NVIDIA drivers and CUDA toolkit
• Tmux (for monitoring interface)

1. Clone the repository:

git clone https://github.com/samisrana/Gaming-Workload-Profiling-and-CPU-GPU-Parallelism-Analysis.git cd Gaming-Workload-Profiling-and-CPU-GPU-Parallelism-Analysis

2. Install dependencies:

pip install numpy cupy pandas matplotlib psutil pynvml

3. Ensure NVIDIA drivers and CUDA toolkit are installed:

nvidia-smi # Verify NVIDIA drivers nvcc --version # Verify CUDA toolkit

1. CPU-only simulation:

python Workloadsim/test_gaming_workload.py

2. GPU-accelerated simulation:

python Workloadsim/test_gaming_workload_gpu.py

The real-time monitoring interface provides comprehensive system metrics visualization:

1. Monitor a specific game:

python realtime/main.py [game_name]

2. Launch the monitoring interface:

./setup_profiling.sh

The monitoring tool starts with a simple interface for selecting the game to monitor. It supports various games and provides case-insensitive search.

When no game is running, the interface shows baseline system metrics:

• Real-time CPU and GPU usage graphs
• Memory utilization
• Temperature and power consumption
• System resource baselines

During active gameplay (Terraria shown):

• Split-screen view of metrics and game
• Live performance tracking
• CSV logging for post-session analysis
• Complete system resource utilization

The workload simulation provides comparative performance analysis between CPU-only and GPU-accelerated implementations.

Key findings from GPU implementation:

• Higher FPS at lower resolutions (60+ FPS at 720p)
• Increased GPU utilization with resolution (30% at 720p to 75% at 1440p)
• Linear scaling of GPU memory usage with resolution
• CPU usage remains moderate even at higher resolutions

Key findings from CPU implementation:

• Lower overall performance (max 22 FPS at 720p)
• More consistent CPU utilization across resolutions
• Significant performance drop at higher resolutions
• Better power efficiency but lower absolute performance

These results demonstrate the efficiency of GPU acceleration for graphics-intensive workloads, while highlighting the importance of CPU-GPU balance in gaming applications.

A real-world performance analysis of Rocket League demonstrates the practical application of the monitoring tools:

The graph above shows resource utilization across different game states, demonstrating key patterns in CPU-GPU interaction:

• Launch Phase: Initial low resource usage as the game starts up
• Menu Phase:
  • Increased CPU activity for game setup and UI rendering
  • Gradual increase in GPU power consumption (11.45W to 43.46W)
  • Initial memory loading occurs
• Gameplay Phase:
  • Peak GPU usage (~31%) with stable temperatures
  • Decreased CPU usage as GPU takes over rendering workload
  • Memory usage stabilizes around 43%, indicating efficient resource management
  • Consistent FPS throughout match duration
• End Phase: Gradual resource reduction

The analysis reveals efficient CPU-GPU load balancing with no significant bottlenecks, as evidenced by stable frame times and optimized resource distribution.

• Session Duration: 1 hour 6 minutes
• Game Active Time: 98.1% of session

• Average FPS: 143.5
• Average Frame Time: 6.94ms
• 1% Low FPS: 140.6
• 0.1% Low FPS: 138.6

• Average Utilization: 30.2%
• Peak Utilization: 92.0%
• Average Memory Used: 2176MB
• Peak Memory Used: 2398MB
• Maximum Temperature: 47.0°C
• Average Power Draw: 30.3W
• Power Scaling: 11.45W (idle) to 43.46W (peak gameplay)

• Average CPU Usage: 9.6%
• Peak CPU Usage: 100.0%
• Average Memory Usage: 44.2%

simulation results showed:

• CPU Version:
  • Light workload (720p, 2 threads): ~22 FPS
  • Medium workload (1080p, 4 threads): ~10 FPS
  • Heavy workload (1440p, 8 threads): ~5 FPS
  • CPU usage scales with workload: 10% → 11% → 14%
• GPU Version:
  • Light workload (720p, 2 threads): ~62 FPS
  • Medium workload (1080p, 4 threads): ~30 FPS
  • Heavy workload (1440p, 8 threads): ~20 FPS
  • GPU utilization scales with resolution: 30% → 50% → 75%
  • GPU memory scales linearly: 1500MB → 2000MB → 3000MB
  • CPU usage increases with resolution: 25% → 35% → 50%

These simulation results contrast with the real game performance, demonstrating the optimization potential in professional game engines compared to simulation models.

Research and development have yielded several significant achievements:

• Successfully developed a foundational gaming workload simulation that accurately models CPU-GPU interaction patterns seen in production games
• Validated simulation insights against real-world data from Rocket League, confirming the model's ability to predict resource utilization patterns
• Established a comprehensive profiling methodology combining simulation baselines with real-time monitoring
• Demonstrated the effectiveness of GPU acceleration, achieving up to 7x performance improvement over CPU-only implementation
• Identified efficient resource management patterns in commercial games, particularly in areas of power scaling and memory utilization
• Developed tools for analyzing CPU-GPU load balancing and bottleneck detection

• Expand testing to cover more games across different genres
• Implement additional workload types beyond gaming
• Develop more sophisticated physics and AI simulation modules

• Add memory access pattern analysis to better understand data flow between CPU and GPU
• Develop automated bottleneck detection using machine learning to provide real-time optimization suggestions
• Implement predictive performance modeling based on collected metrics

• Create automated reporting and analysis features
• Develop real-time visualization improvements for better monitoring
• Implement cross-platform support for various gaming engines

├── setup_profiling.sh # Monitoring setup script ├── monitors/ │ ├── plotcpu.py # CPU monitoring visualization │ └── plotgpu.py # GPU monitoring visualization ├── realtime/ │ ├── main.py # Main monitoring program │ ├── metrics_collector.py # Performance metrics collection │ ├── data_logger.py # Data logging functionality │ ├── display_manager.py # Real-time display management │ ├── game_performance_logs # csv file logs │ └── game_performance_analysis # Graphs, logs, and reports of average data per session └── Workloadsim/ ├── gaming_workload_simulation.py # CPU simulation workload ├── gaming_workload_simulation_gpu.py # GPU simulation workload ├── test_gaming_workload.py # CPU simulation tests ├── test_gaming_workload_gpu.py # GPU simulation tests └── benchmark_results # test results 

The toolkit collects and analyzes the following metrics:

• Frame rate (FPS) and frame times
• CPU utilization (overall and per-thread)
• GPU utilization and memory usage
• Temperature and power consumption
• Memory bandwidth and usage patterns
• Thread scaling efficiency

Contributions are welcome! Please feel free to submit a Pull Request.

• NVIDIA for CUDA and NVML toolkits
• Python scientific computing community
• Gaming performance analysis community