236 lines
7.8 KiB
Markdown
236 lines
7.8 KiB
Markdown
# RTX Benchmark Suite
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A comprehensive benchmarking framework for the RustyTorch++ platform, providing performance analysis across all components of the ML/AI stack.
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## Features
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### Core Infrastructure Benchmarks
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- **Tensor Operations**: Creation, arithmetic, linear algebra, reductions, shape manipulation
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- **Memory Management**: Allocation patterns, pool performance, GPU memory, fragmentation analysis
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- **Autograd System**: Forward/backward passes, computation graphs, gradient computation
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- **Runtime Performance**: Device management, context switching, kernel compilation
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- **GPU Kernels**: CUDA/ROCm kernel execution, compilation time, memory transfers
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### Model Architecture Benchmarks
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- **Transformer Models**: BERT, GPT, T5 with different sizes and configurations
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- **Vision Models**: ResNet, ViT, EfficientNet with various input sizes
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- **Multimodal Models**: CLIP, DALL-E, cross-modal attention mechanisms
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- **Scaling Analysis**: Batch size optimization, sequence length impact, memory usage
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### Distributed Training Benchmarks
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- **Multi-GPU Scaling**: Efficiency analysis across GPU counts
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- **Communication Patterns**: Gradient synchronization, bandwidth utilization
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- **Load Balancing**: Work distribution effectiveness
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- **Fault Tolerance**: Recovery time analysis
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### Production Inference Benchmarks
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- **Latency Analysis**: Single request latency (p50, p95, p99)
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- **Throughput Testing**: Batch processing performance, concurrent requests
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- **Memory Efficiency**: Peak allocation, garbage collection impact
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- **Model Loading**: Initialization and warm-up performance
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### Compression & Optimization Benchmarks
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- **Quantization**: Accuracy vs speed tradeoffs across bit depths
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- **Model Pruning**: Sparsity impact on performance and memory
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- **Knowledge Distillation**: Student model convergence rates
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- **Optimization Techniques**: Various acceleration methods
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### Competitive Analysis Framework
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- **Framework Comparisons**: PyTorch, TensorFlow, ONNX Runtime
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- **MLPerf Benchmarks**: Industry-standard compliance testing
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- **Hardware Optimization**: Platform-specific performance analysis
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- **Statistical Validation**: Rigorous comparison methodology
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## Architecture
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### TDD Implementation
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The benchmark suite follows strict Test-Driven Development:
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- All benchmarks start with failing tests
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- Statistical rigor with confidence intervals
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- Regression detection and alerting
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- Comprehensive validation of measurements
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### Statistical Analysis
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- Coefficient of variation tracking
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- Outlier detection using IQR method
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- Welch's t-test for significance testing
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- Confidence interval calculation
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- Performance regression detection
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### Reporting System
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- **HTML Reports**: Interactive visualizations and detailed analysis
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- **JSON Export**: Machine-readable results for automation
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- **CSV Data**: Raw measurements for external analysis
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- **Regression Alerts**: Automated performance regression detection
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## Usage
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### Command Line Interface
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```bash
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# Run all benchmark suites
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rtx-bench all --gpu --competitive
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# Run specific benchmark categories
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rtx-bench core --devices cpu,cuda:0 --iterations 100
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rtx-bench models --models bert,gpt,resnet --batch-sizes 1,8,16,32
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rtx-bench distributed --num-gpus 4 --backend nccl
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rtx-bench inference --concurrency-levels 1,10,50,100 --batch-processing
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rtx-bench competitive --frameworks pytorch,tensorflow --mlperf
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# Generate reports from existing results
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rtx-bench report --input results/ --regression --historical --formats html,json
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```
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### Programmatic API
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```rust
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use rtx_bench::{BenchmarkConfig, BenchmarkSuite};
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#[tokio::main]
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async fn main() -> anyhow::Result<()> {
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// Configure benchmarks
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let config = BenchmarkConfig::default()
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.with_warmup_iterations(10)
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.with_measurement_iterations(100)
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.with_gpu_enabled(true)
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.with_confidence_level(0.95);
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// Run comprehensive benchmark suite
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let suite = BenchmarkSuite::new(config);
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let results = suite.run_all_benchmarks().await?;
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// Generate detailed report
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let report_config = ReportConfig::default();
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let generator = ReportGenerator::new(report_config.clone());
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let report = generator.generate_report(&results, &report_config).await?;
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println!("Benchmark completed: {}", report.summary.average_execution_time_ms);
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Ok(())
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}
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```
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## Benchmark Categories
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### 1. Core Infrastructure Performance
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- Tensor operations across different sizes and data types
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- Memory allocation patterns and pool effectiveness
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- GPU memory transfer optimization
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- Autograd computation graph efficiency
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- Kernel compilation and execution timing
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### 2. Model Architecture Analysis
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- Forward pass latency and throughput
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- Training step performance including backward pass
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- Memory usage profiling during training/inference
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- Batch size scaling characteristics
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- Sequence length impact analysis
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### 3. Distributed Training Evaluation
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- Multi-GPU communication efficiency
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- Gradient synchronization overhead
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- Load balancing across devices
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- Fault recovery mechanisms
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- Scaling efficiency with device count
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### 4. Production Inference Metrics
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- Single request latency distribution
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- Concurrent request handling capability
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- Batch processing throughput optimization
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- Model loading and initialization time
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- Memory efficiency under production load
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### 5. Competitive Performance Analysis
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- Head-to-head comparisons with leading frameworks
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- MLPerf benchmark compliance validation
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- Hardware-specific optimization effectiveness
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- Industry standard performance verification
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## Output and Reporting
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### Statistical Validation
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All benchmarks include:
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- Minimum sample size validation (configurable)
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- Outlier detection and filtering
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- Coefficient of variation analysis
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- Statistical significance testing
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- Confidence interval calculation
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### Performance Regression Detection
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- Automated comparison against historical baselines
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- Statistical significance testing for changes
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- Severity classification (Low/Medium/High/Critical)
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- Root cause analysis suggestions
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- Alert generation for significant regressions
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### Report Formats
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- **Interactive HTML**: Rich visualizations, charts, and drill-down analysis
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- **JSON**: Structured data for integration with CI/CD pipelines
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- **CSV**: Raw data export for custom analysis tools
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- **Regression Alerts**: Automated notifications for performance issues
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## Configuration
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### Benchmark Configuration
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```rust
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BenchmarkConfig {
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warmup_iterations: 5,
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measurement_iterations: 50,
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max_duration: Duration::from_secs(300),
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enable_gpu: true,
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enable_distributed: false,
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confidence_level: 0.95,
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min_sample_size: 30,
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collect_memory_stats: true,
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target_devices: vec!["cpu", "cuda:0"],
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}
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```
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### Report Configuration
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```rust
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ReportConfig {
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include_statistics: true,
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include_regression_analysis: true,
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include_historical_comparison: true,
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generate_html: true,
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generate_json: true,
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generate_csv: false,
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regression_threshold: 5.0, // 5% performance change threshold
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}
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```
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## Integration
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### CI/CD Pipeline Integration
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```yaml
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- name: Performance Benchmarks
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run: |
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rtx-bench all --output-dir results/ --formats json
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rtx-bench report --input results/ --regression --formats html
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```
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### Automated Regression Detection
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The benchmark suite automatically detects:
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- Performance regressions above configurable thresholds
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- Memory usage increases
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- Statistical significance of changes
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- High variance indicating unstable performance
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## System Requirements
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- Rust 1.70.0 or later
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- CUDA 12.0+ (for GPU benchmarks)
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- 16GB+ RAM recommended for full benchmark suite
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- Multi-GPU setup for distributed training benchmarks
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## Contributing
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The benchmark suite follows strict quality standards:
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- All benchmarks must include statistical validation
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- Minimum 95% test coverage for new components
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- Performance regression tests for all changes
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- Documentation with runnable examples
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## License
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Licensed under either Apache 2.0 or MIT at your option. |