rustytorch/crates/tooling/rtx-bench

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

# 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

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

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

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

- 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.