Expert-Sliced GPU Scheduling for Mixture of Experts

A research-grade implementation of dynamic GPU resource allocation for Mixture of Experts models, achieving 2.3-2.4× throughput improvement and 35-45% energy efficiency gains.

🚀 Key Features

Core Optimizations

• 🔥 Triton Kernels: Fused expert computation with minimal memory traffic
• 📊 CUDA Graphs: Pre-batched expert execution for reduced kernel launch overhead
• ⚡ Dynamic GPU Slicing: Runtime allocation of GPU resources based on expert utilization
• 🔀 Stream-Based Parallelism: Concurrent expert execution on dedicated CUDA streams
• 🎯 MIG Support: Integration with NVIDIA Multi-Instance GPU technology
• 📈 Energy Monitoring: Real-time power consumption and efficiency tracking via NVML

Performance Highlights

• 2.3-2.4× throughput improvement over baseline PyTorch MoE
• 35-45% energy efficiency gains (tokens per joule)
• 73% GPU utilization (vs. 28% baseline)
• Zero accuracy loss - bit-exact results

📋 Requirements

Hardware

• NVIDIA GPU with Compute Capability 8.0+ (A100, H100, RTX 3090+)
• CUDA 12.0 or later
• 16GB+ GPU memory recommended

Software

• Python 3.10+
• PyTorch 2.0+
• Triton 2.0+
• CUDA Toolkit 12.0+

🔧 Installation

Quick Start

# Clone the repository
git clone https://github.com/Esmail-ibraheem/Nexus.git
cd moe-gpu-scheduling

# Create virtual environment
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# Install dependencies
pip install -r requirements.txt

# Verify installation
python -c "import torch; print(f'PyTorch: {torch.__version__}, CUDA: {torch.cuda.is_available()}')"

Development Installation

# Install in editable mode with dev dependencies
pip install -e .
pip install pytest black isort mypy

# Run tests
pytest tests/

🎯 Quick Start

1. Run Benchmark

Compare baseline vs. optimized MoE implementations:

python examples/run_benchmark.py

Expected output:

================================================================================
MoE Benchmark Results
================================================================================

Configuration:
  Input dim: 512
  Expert dim: 512
  Hidden dim: 1024
  Num experts: 8
  Top-k: 2
  Device: cuda

Batch Size   Baseline (ms)   Optimized (ms)  Speedup    Improvement    
--------------------------------------------------------------------------------
64           2.76            1.22            2.26×      126.23%
128          4.89            2.11            2.32×      131.75%
256          8.34            3.61            2.31×      130.75%
================================================================================

2. Train Advanced MoE Model

Train with all optimizations enabled:

python examples/train_advanced_moe.py

Features demonstrated:

• Dynamic GPU slice allocation
• CUDA graph optimization
• Triton kernel acceleration
• Energy monitoring
• Real-time performance statistics

3. Visualize Results

Generate publication-quality plots:

python scripts/visualize_results.py --results benchmark_results.json --output plots/

Generated plots:

• Throughput comparison
• GPU utilization
• Energy efficiency
• Expert utilization heatmap
• Ablation study
• Scaling analysis

📁 Project Structure

moe-gpu-scheduling/
├── moe_gpu/                      # Core implementation
│   ├── __init__.py              # Package exports
│   ├── model.py                 # MoE layers (baseline & advanced)
│   ├── triton_kernels.py        # Optimized Triton kernels
│   ├── cuda_graph_manager.py    # CUDA graph & stream management
│   ├── gpu_slice_manager.py     # Dynamic GPU slicing with MIG support
│   ├── profiler.py              # Expert profiling & optimization
│   ├── energy_monitor.py        # Power & energy tracking
│   └── benchmark.py             # Comprehensive benchmarking suite
│
├── examples/                     # Example scripts
│   ├── train_moe.py             # Basic training (legacy)
│   ├── train_advanced_moe.py    # Advanced training with all features
│   └── run_benchmark.py         # Interactive benchmark runner
│
├── scripts/                      # Utility scripts
│   └── visualize_results.py     # Generate plots and visualizations
│
├── paper/                        # Research paper
│   └── research_paper.md        # Full paper with methodology & results
│
├── tests/                        # Unit tests
│   └── test_*.py                # Test files
│
├── requirements.txt              # Python dependencies
└── README.md                     # This file

🔬 How It Works

Architecture Overview

Input Tokens → Router (Triton) → Expert Profiler → GPU Slice Manager
                                        ↓
                                 Slice Optimizer
                                        ↓
                            CUDA Graph Manager ← Stream Manager
                                        ↓
                            Expert Execution (Parallel)
                                        ↓
                            Output Aggregation + Energy Monitor

Key Components

1. Triton Kernels (triton_kernels.py)

Fused operations that minimize memory traffic:

• Routing kernel: Softmax + top-k + counting in one pass
• Expert MLP kernel: Multi-layer computation with register-resident intermediates
• Batched expert kernel: Tiled matrix multiplication for token batches

2. CUDA Graph Manager (cuda_graph_manager.py)

Captures and replays expert execution patterns:

• Reduces kernel launch overhead from ~5μs to ~1μs
• Automatically captures frequently-used experts
• Supports dynamic input shapes

3. GPU Slice Manager (gpu_slice_manager.py)

Dynamic resource allocation with multiple policies:

• Static: Fixed allocation (baseline)
• Dynamic: Based on recent utilization
• Proportional: Weighted by expert load
• Adaptive: ML-based prediction (future work)

4. Energy Monitor (energy_monitor.py)

Real-time power and efficiency tracking:

• Per-expert energy profiling
• Tokens per joule calculation
• GPU utilization monitoring via NVML

📊 Performance Results

Throughput Comparison (A100 GPU)

Batch Size	Baseline	Ours	Speedup
64	23,120	52,340	2.26×
128	41,230	95,670	2.32×
256	68,450	158,230	2.31×
512	102,340	245,670	2.40×

GPU Utilization

Baseline:     ████░░░░░░░░░░░░  28% avg
Ours:         ████████████████  73% avg  (+161%)

Energy Efficiency

Configuration	Baseline	Ours	Improvement
Small MoE	2.45 J	1.42 J	42.0%
Medium MoE	4.12 J	2.51 J	39.1%
Large MoE	7.89 J	5.14 J	34.9%

Ablation Study

Configuration	Throughput	Speedup
Baseline	68,450	1.00×
+ Triton Kernels	98,230	1.43×
+ CUDA Graphs	124,560	1.82×
+ Dynamic Slicing	145,670	2.13×
+ Stream Parallelism	158,230	2.31×

🎓 Research Paper

A comprehensive research paper is included in paper/research_paper.md covering:

• Detailed methodology
• Experimental setup
• Complete results and analysis
• Ablation studies
• Comparison with related work

Key contributions:

1. Novel dynamic GPU slicing algorithm
2. Triton-optimized expert kernels
3. CUDA graph integration for MoE
4. Comprehensive energy efficiency analysis

🔧 Advanced Usage

Custom MoE Configuration

from moe_gpu import AdvancedMoELayer, SliceAllocationPolicy

model = AdvancedMoELayer(
    input_dim=512,
    expert_dim=512,
    hidden_dim=2048,
    num_experts=16,
    top_k=2,
    total_slices=8,
    use_triton=True,
    use_cuda_graphs=True,
    enable_energy_monitoring=True,
    allocation_policy=SliceAllocationPolicy.DYNAMIC
)

# Forward pass returns output and statistics
output, stats = model(input_tensor)

# Get comprehensive performance metrics
perf_stats = model.get_performance_stats()
print(f"GPU Utilization: {perf_stats['slice_stats']['avg_utilization']:.2f}")
print(f"Energy per token: {perf_stats['energy_stats']['efficiency_metrics']['tokens_per_joule']:.2f}")

Custom Benchmarking

from moe_gpu.benchmark import MoEBenchmark

benchmark = MoEBenchmark(
    input_dim=1024,
    expert_dim=1024,
    hidden_dim=4096,
    num_experts=32,
    top_k=4,
    batch_sizes=[128, 256, 512, 1024],
    num_iterations=100
)

results = benchmark.run_comparison()
benchmark.print_results()
benchmark.save_results('my_benchmark.json')

🐛 Troubleshooting

Common Issues

1. CUDA Out of Memory

# Reduce batch size or number of experts
python examples/train_advanced_moe.py --batch_size 64 --num_experts 8

2. Triton Not Available

# Install Triton
pip install triton>=2.0.0

# Or disable Triton kernels
model = AdvancedMoELayer(..., use_triton=False)

3. NVML Initialization Failed

# Energy monitoring requires proper NVIDIA drivers
# Disable if not needed
model = AdvancedMoELayer(..., enable_energy_monitoring=False)

🤝 Contributing

We welcome contributions! Please see our contributing guidelines:

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

Development Guidelines

• Follow PEP 8 style guide
• Add unit tests for new features
• Update documentation
• Run black and isort before committing

📝 Citation

If you use this work in your research, please cite:

@article{expert_sliced_gpu_2025,
  title={Expert-Sliced GPU Scheduling: Dynamic Resource Allocation for Mixture of Experts Models},
  author={Esmail Gumaan},
  year={2025}
}

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

🙏 Acknowledgments

• NVIDIA for CUDA, Triton
• PyTorch team for the deep learning framework
• Research community for MoE innovations

Issues: GitHub Issues

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