# RTX Benchmark Suite

A comprehensive benchmarking framework for the RustyTorch++ platform, providing performance analysis across all components of the ML/AI stack.

## Features

### Core Infrastructure Benchmarks
- **Tensor Operations**: Creation, arithmetic, linear algebra, reductions, shape manipulation
- **Memory Management**: Allocation patterns, pool performance, GPU memory, fragmentation analysis
- **Autograd System**: Forward/backward passes, computation graphs, gradient computation
- **Runtime Performance**: Device management, context switching, kernel compilation
- **GPU Kernels**: CUDA/ROCm kernel execution, compilation time, memory transfers

### Model Architecture Benchmarks
- **Transformer Models**: BERT, GPT, T5 with different sizes and configurations
- **Vision Models**: ResNet, ViT, EfficientNet with various input sizes
- **Multimodal Models**: CLIP, DALL-E, cross-modal attention mechanisms
- **Scaling Analysis**: Batch size optimization, sequence length impact, memory usage

### Distributed Training Benchmarks
- **Multi-GPU Scaling**: Efficiency analysis across GPU counts
- **Communication Patterns**: Gradient synchronization, bandwidth utilization
- **Load Balancing**: Work distribution effectiveness
- **Fault Tolerance**: Recovery time analysis

### Production Inference Benchmarks
- **Latency Analysis**: Single request latency (p50, p95, p99)
- **Throughput Testing**: Batch processing performance, concurrent requests
- **Memory Efficiency**: Peak allocation, garbage collection impact
- **Model Loading**: Initialization and warm-up performance

### Compression & Optimization Benchmarks
- **Quantization**: Accuracy vs speed tradeoffs across bit depths
- **Model Pruning**: Sparsity impact on performance and memory
- **Knowledge Distillation**: Student model convergence rates
- **Optimization Techniques**: Various acceleration methods

### Competitive Analysis Framework
- **Framework Comparisons**: PyTorch, TensorFlow, ONNX Runtime
- **MLPerf Benchmarks**: Industry-standard compliance testing
- **Hardware Optimization**: Platform-specific performance analysis
- **Statistical Validation**: Rigorous comparison methodology

## Architecture

### TDD Implementation
The benchmark suite follows strict Test-Driven Development:
- All benchmarks start with failing tests
- Statistical rigor with confidence intervals
- Regression detection and alerting
- Comprehensive validation of measurements

### Statistical Analysis
- Coefficient of variation tracking
- Outlier detection using IQR method
- Welch's t-test for significance testing
- Confidence interval calculation
- Performance regression detection

### Reporting System
- **HTML Reports**: Interactive visualizations and detailed analysis
- **JSON Export**: Machine-readable results for automation
- **CSV Data**: Raw measurements for external analysis
- **Regression Alerts**: Automated performance regression detection

## Usage

### Command Line Interface

```bash
# Run all benchmark suites
rtx-bench all --gpu --competitive

# Run specific benchmark categories
rtx-bench core --devices cpu,cuda:0 --iterations 100
rtx-bench models --models bert,gpt,resnet --batch-sizes 1,8,16,32
rtx-bench distributed --num-gpus 4 --backend nccl
rtx-bench inference --concurrency-levels 1,10,50,100 --batch-processing
rtx-bench competitive --frameworks pytorch,tensorflow --mlperf

# Generate reports from existing results
rtx-bench report --input results/ --regression --historical --formats html,json
```

### Programmatic API

```rust
use rtx_bench::{BenchmarkConfig, BenchmarkSuite};

#[tokio::main]
async fn main() -> anyhow::Result<()> {
    // Configure benchmarks
    let config = BenchmarkConfig::default()
        .with_warmup_iterations(10)
        .with_measurement_iterations(100)
        .with_gpu_enabled(true)
        .with_confidence_level(0.95);

    // Run comprehensive benchmark suite
    let suite = BenchmarkSuite::new(config);
    let results = suite.run_all_benchmarks().await?;

    // Generate detailed report
    let report_config = ReportConfig::default();
    let generator = ReportGenerator::new(report_config.clone());
    let report = generator.generate_report(&results, &report_config).await?;

    println!("Benchmark completed: {}", report.summary.average_execution_time_ms);
    Ok(())
}
```

## Benchmark Categories

### 1. Core Infrastructure Performance
- Tensor operations across different sizes and data types
- Memory allocation patterns and pool effectiveness
- GPU memory transfer optimization
- Autograd computation graph efficiency
- Kernel compilation and execution timing

### 2. Model Architecture Analysis
- Forward pass latency and throughput
- Training step performance including backward pass
- Memory usage profiling during training/inference
- Batch size scaling characteristics
- Sequence length impact analysis

### 3. Distributed Training Evaluation
- Multi-GPU communication efficiency
- Gradient synchronization overhead
- Load balancing across devices
- Fault recovery mechanisms
- Scaling efficiency with device count

### 4. Production Inference Metrics
- Single request latency distribution
- Concurrent request handling capability
- Batch processing throughput optimization
- Model loading and initialization time
- Memory efficiency under production load

### 5. Competitive Performance Analysis
- Head-to-head comparisons with leading frameworks
- MLPerf benchmark compliance validation
- Hardware-specific optimization effectiveness
- Industry standard performance verification

## Output and Reporting

### Statistical Validation
All benchmarks include:
- Minimum sample size validation (configurable)
- Outlier detection and filtering
- Coefficient of variation analysis
- Statistical significance testing
- Confidence interval calculation

### Performance Regression Detection
- Automated comparison against historical baselines
- Statistical significance testing for changes
- Severity classification (Low/Medium/High/Critical)
- Root cause analysis suggestions
- Alert generation for significant regressions

### Report Formats
- **Interactive HTML**: Rich visualizations, charts, and drill-down analysis
- **JSON**: Structured data for integration with CI/CD pipelines
- **CSV**: Raw data export for custom analysis tools
- **Regression Alerts**: Automated notifications for performance issues

## Configuration

### Benchmark Configuration
```rust
BenchmarkConfig {
    warmup_iterations: 5,
    measurement_iterations: 50,
    max_duration: Duration::from_secs(300),
    enable_gpu: true,
    enable_distributed: false,
    confidence_level: 0.95,
    min_sample_size: 30,
    collect_memory_stats: true,
    target_devices: vec!["cpu", "cuda:0"],
}
```

### Report Configuration
```rust
ReportConfig {
    include_statistics: true,
    include_regression_analysis: true,
    include_historical_comparison: true,
    generate_html: true,
    generate_json: true,
    generate_csv: false,
    regression_threshold: 5.0, // 5% performance change threshold
}
```

## Integration

### CI/CD Pipeline Integration
```yaml
- name: Performance Benchmarks
  run: |
    rtx-bench all --output-dir results/ --formats json
    rtx-bench report --input results/ --regression --formats html
```

### Automated Regression Detection
The benchmark suite automatically detects:
- Performance regressions above configurable thresholds
- Memory usage increases
- Statistical significance of changes
- High variance indicating unstable performance

## System Requirements

- Rust 1.70.0 or later
- CUDA 12.0+ (for GPU benchmarks)
- 16GB+ RAM recommended for full benchmark suite
- Multi-GPU setup for distributed training benchmarks

## Contributing

The benchmark suite follows strict quality standards:
- All benchmarks must include statistical validation
- Minimum 95% test coverage for new components
- Performance regression tests for all changes
- Documentation with runnable examples

## License

Licensed under either Apache 2.0 or MIT at your option.