Add Benchmark Roadmap RFC and Complete Phase 1-4 Implementation for All QDP Benchmarks

[Copilot]

Apache Mahout QDP benchmarks lack warmup, proper cache clearing, CUDA event timing, and statistical measurement infrastructure needed for fair comparisons and publication-quality results.

Changes

New RFC Document: qdp/docs/BENCHMARK_ROADMAP.md

Comprehensive 750-line roadmap addressing:

Fairness improvements:

• Warmup mechanism (3-5 iterations) to eliminate JIT compilation overhead
• Cache clearing strategy: gc.collect(), torch.cuda.empty_cache(), optional L2 cache flush
• CUDA event-based timing replacing time.perf_counter() for GPU-accurate measurements
• Synchronization requirements at measurement boundaries

Statistical measurements:

• Multiple runs (10-100 depending on workload) with full distribution analysis
• Metrics: mean, median, std, min, max, percentiles (P25/P50/P75/P90/P95/P99), IQR, CV
• Outlier detection via IQR and z-score methods

Visualization for publications:

• Bar charts with error bars, box plots, violin plots, comparison tables
• 300 DPI publication-ready output via matplotlib/seaborn
• Complete BenchmarkVisualizer class implementation

Implementation plan:

• 8-week phased roadmap from utilities to publication
• Backward-compatible optional --statistical and --visualize flags
• YAML configuration for reproducibility
• References to PyTorch Helion, Triton, GPU-mode best practices

Phase 1 Implementation: Benchmark Utils Foundation

Complete implementation of the foundation modules (1,677 lines added):

New Package: qdp/qdp-python/benchmark/benchmark_utils/

1. timing.py (247 lines):

   • warmup(): Eliminate JIT compilation overhead with configurable iterations
   • clear_all_caches(): Comprehensive cache clearing (Python GC + GPU)
   • clear_l2_cache(): Optional aggressive GPU L2 cache clearing
   • benchmark_with_cuda_events(): Precise GPU timing using CUDA events
   • benchmark_cpu_function(): CPU timing fallback for non-GPU operations

2. statistics.py (231 lines):

   • compute_statistics(): Full statistical analysis (mean, median, std, percentiles, IQR, CV)
   • filter_outliers(): IQR and z-score based outlier detection
   • compute_confidence_interval(): Statistical confidence intervals
   • format_statistics(): Pretty console output formatting

3. visualization.py (321 lines):

   • BenchmarkVisualizer class for publication-ready plots
   • Bar charts with error bars (300 DPI)
   • Box plots showing quartiles and outliers
   • Violin plots showing full distributions
   • Markdown comparison tables
   • Batch plot generation with create_all_plots()

4. config.py (248 lines):

   • YAML configuration loading and saving
   • Data classes for fairness, statistics, visualization settings
   • Default configuration generator
   • Validation and error handling

5. __init__.py (41 lines): Clean package exports for easy imports

Testing & Documentation:

• test_benchmark_utils.py: 249 lines with 30+ test cases
• GPU/CPU test markers for flexible testing
• Complete API documentation with examples (README.md)
• Example configuration file (benchmark_config.yaml)

Dependencies Added:

• scipy >= 1.10 (for confidence intervals)
• seaborn >= 0.12 (for enhanced visualizations)
• pyyaml >= 6.0 (for configuration files)

Phase 2 Implementation: Statistical Mode Integration

Integrated statistical mode into all three benchmarks (641 lines modified):

Enhanced Benchmarks:

1. benchmark_e2e.py - E2E latency benchmark with statistical mode:

   • Added --statistical flag for opt-in statistical analysis
   • Added --warmup N flag (default: 3 iterations)
   • Added --repeat N flag (default: 10 measurements)
   • Created run_framework_statistical() wrapper for statistical execution
   • CUDA event-based precise GPU timing in statistical mode
   • Comprehensive statistics output with formatted console display
   • Graceful fallback if benchmark_utils unavailable
   • Fully backward compatible with existing standard mode

2. benchmark_throughput.py - Throughput benchmark with statistical mode:

   • Added --statistical flag for opt-in statistical analysis
   • Added --warmup N flag (default: 2 iterations for throughput)
   • Added --repeat N flag (default: 10 measurements)
   • Created run_framework_statistical_throughput() wrapper
   • Dual statistics: separate metrics for duration AND throughput
   • CUDA event-based precise GPU timing
   • Fully backward compatible

3. benchmark_numpy_io.py - NumPy I/O benchmark with statistical mode (NEW):

   • Added --statistical flag for opt-in statistical analysis
   • Added --warmup N flag (default: 3 iterations)
   • Added --repeat N flag (default: 10 measurements)
   • Created run_framework_statistical() wrapper for statistical execution
   • Dual statistics: separate metrics for duration AND throughput (samples/sec)
   • CUDA event-based precise GPU timing
   • Compares Mahout's NumPy reader against PennyLane's implementation
   • Fully backward compatible

4. Updated README.md:

   • Documented statistical mode usage with detailed examples
   • Separate sections for standard vs statistical modes
   • Explained new flags and their benefits
   • Usage examples for all three benchmarks

Statistical Mode Features:

• Warmup runs to eliminate JIT compilation overhead
• CUDA event-based timing for GPU-accurate measurements
• Cache clearing between runs for fairness
• Multiple measurement iterations (configurable)
• Comprehensive statistics display: mean, median, std, P25-P99, IQR, CV
• Pretty formatted console output via format_statistics()

Phase 3 Implementation: Visualization Integration

Added publication-ready plot generation to all three benchmarks (493 lines modified):

Enhanced Benchmarks with Visualization:

1. benchmark_e2e.py - E2E benchmark with visualization:

   • Added --visualize flag to generate publication-ready plots
   • Added --output-dir PATH to customize output location (default: ./benchmark_results)
   • Generates 4 visualization files per run:
     • Bar chart with error bars (mean ± std)
     • Box plot showing quartiles and outliers
     • Violin plot showing full distribution
     • Markdown table with complete statistics
   • Filenames include parameters: e2e_q{qubits}_s{samples}_*.{png|md}
   • Graceful error handling if visualization fails

2. benchmark_throughput.py - Throughput benchmark with visualization:

   • Added --visualize flag with same functionality
   • Added --output-dir PATH for custom output location
   • Generates 8 visualization files (2 complete sets):
     • Duration plots (time taken for benchmark)
     • Throughput plots (vectors processed per second)
   • Each set includes all 4 visualization types
   • Filenames: throughput_{duration|vecpersec}_q{qubits}_b{batches}_*.{png|md}

3. benchmark_numpy_io.py - NumPy I/O benchmark with visualization (NEW):

   • Added --visualize flag to generate publication-ready plots
   • Added --output-dir PATH for custom output location
   • Generates 8 visualization files (2 complete sets):
     • Duration plots (I/O + encoding time)
     • Throughput plots (samples processed per second)
   • Each set includes all 4 visualization types
   • Filenames: numpy_{duration|throughput}_q{qubits}_s{samples}_*.{png|md}

4. Updated README.md:

   • Added Phase 3 documentation for visualization mode
   • Usage examples with --visualize flag
   • Clear explanation of generated files for all three benchmarks
   • Distinction between standard, statistical, and visualization modes

Visualization Features:

• 300 DPI publication-quality PNG images
• Bar charts with mean ± std error bars
• Box plots showing quartiles and outliers
• Violin plots showing full distributions
• Markdown tables with comprehensive statistics
• Customizable output directory
• Automatic directory creation
• Descriptive filenames encoding parameters

Phase 4 Implementation: Documentation

Added comprehensive documentation for reproducible benchmarking (1,044 lines added):

New Documentation Files:

1. BEST_PRACTICES.md (11,667 characters):

   • Comprehensive guide to reproducible benchmarks
   • Fairness principles (warmup, cache clearing, CUDA events, synchronization)
   • Statistical rigor (distributions, repetitions, outliers, confidence intervals)
   • Reproducibility guidelines (configuration, versioning, documentation)
   • Publication guidelines (plot selection, reporting standards, examples)
   • Common pitfalls with solutions (7 pitfalls documented)
   • Quick reference cheat sheets for CLI and Python API
   • References to PyTorch Helion, Triton, and GPU-mode best practices

2. notebooks/statistical_benchmark_tutorial.ipynb (16,592 characters):

   • Interactive Jupyter notebook tutorial with 15+ executable code cells
   • Part 1: Using benchmark_utils API directly
   • Part 2: Running E2E benchmarks in statistical mode
   • Part 3: Generating publication-ready visualizations
   • Part 4: Best practices demonstrations with working code
   • Complete walkthrough from basics to advanced usage
   • Inline plot display
   • Ready for Google Colab deployment

3. Enhanced README.md:

   • New "Documentation and Tutorials" section
   • "Visualization Examples" section with output structure
   • "Notebooks" section with Colab badges
   • "Contributing" guidelines for benchmark submissions
   • Comprehensive documentation for all three benchmarks
   • Links to all documentation resources

Documentation Topics:

• Fairness: Warmup, cache clearing, CUDA events, synchronization
• Statistics: Full distributions, repetitions, outlier filtering, confidence intervals
• Reproducibility: Save configs, document specs, version control
• Publication: Choose plots, report effect sizes, follow standards
• Common pitfalls: No warmup, cached comparisons, CPU timing for GPU, cherry-picking, hiding variability, insufficient repetitions, undocumented configs

Example Usage

Phase 1 - Benchmark Utils:

from benchmark_utils import (
    benchmark_with_cuda_events,
    compute_statistics,
    BenchmarkVisualizer
)

# Define operation to benchmark
def my_operation():
    x = torch.randn(1000, 1000, device='cuda')
    return x @ x.T

# Run benchmark with CUDA events
timings = benchmark_with_cuda_events(
    my_operation,
    warmup_iters=3,
    repeat=50
)

# Compute statistics
stats = compute_statistics(timings)
print(f"Mean: {stats['mean']:.2f} ms ± {stats['std']:.2f} ms")

# Create visualizations
visualizer = BenchmarkVisualizer()
visualizer.plot_comparison_bars({
    'My Operation': stats
}, output_path='results.png')

Phase 2 - Statistical Mode:

# E2E benchmark with statistical mode (5 warmup, 20 measurements)
python benchmark_e2e.py --statistical --warmup 5 --repeat 20

# Throughput benchmark with statistical mode
python benchmark_throughput.py --statistical --frameworks mahout,pennylane

# NumPy I/O benchmark with statistical mode (NEW)
python benchmark_numpy_io.py --statistical --warmup 3 --repeat 15

# Works with all existing flags
python benchmark_e2e.py --statistical --qubits 16 --frameworks all

Phase 3 - Visualization Mode:

# E2E benchmark with visualization
python benchmark_e2e.py --statistical --visualize

# Customize output directory
python benchmark_e2e.py --statistical --visualize --output-dir ./my_results

# Throughput with visualization (generates 8 files)
python benchmark_throughput.py --statistical --visualize --frameworks mahout,pennylane

# NumPy I/O with visualization (generates 8 files) (NEW)
python benchmark_numpy_io.py --statistical --visualize \
  --qubits 10 --samples 1000 \
  --frameworks mahout,pennylane \
  --output-dir ./numpy_results

# Full example for publication
python benchmark_e2e.py --statistical --visualize \
  --warmup 5 --repeat 20 \
  --frameworks mahout-parquet pennylane \
  --output-dir ./paper_figures

Phase 4 - Documentation:

# Read best practices guide
cat BEST_PRACTICES.md

# Run tutorial notebook
jupyter notebook notebooks/statistical_benchmark_tutorial.ipynb

# Or open in Google Colab (badges in README)

Complete Feature Matrix

All THREE benchmarks now have complete feature parity:

Feature	E2E Benchmark	Throughput Benchmark	NumPy I/O Benchmark
Standard mode	✅	✅	✅
Statistical mode	✅	✅	✅
Visualization mode	✅	✅	✅
CUDA event timing	✅	✅	✅
Warmup runs	✅	✅	✅
Cache clearing	✅	✅	✅
Full statistics	✅	✅	✅
Publication plots	✅	✅	✅
Dual metrics	❌	✅ (duration + vectors/sec)	✅ (duration + samples/sec)

Documentation Updates

• qdp/qdp-python/benchmark/README.md: Enhanced with documentation sections, visualization examples, tutorials, NumPy benchmark documentation, and contributing guidelines
• qdp/qdp-python/benchmark/BEST_PRACTICES.md: NEW - Comprehensive reproducibility guide
• qdp/qdp-python/benchmark/notebooks/statistical_benchmark_tutorial.ipynb: NEW - Interactive tutorial
• qdp/DEVELOPMENT.md: Added RFC reference in benchmark section
• qdp/qdp-python/benchmark/benchmark_utils/README.md: Complete API documentation

Testing

• ✅ All Python modules compile successfully
• ✅ 30+ unit tests with GPU/CPU markers
• ✅ Code review completed (all feedback addressed)
• ✅ Security scan passed (0 alerts)
• ✅ Python 3.9+ compatible type hints
• ✅ Backward compatible - standard mode unchanged

Summary

Phases 1, 2, 3, and 4 are complete (3,577 lines added). All three benchmarks now support:

• Standard mode (single-run) - unchanged for backward compatibility
• Statistical mode (multi-run with warmup) - fair, reproducible measurements
• Visualization mode (publication plots) - 300 DPI publication-ready outputs
• Complete documentation - best practices, tutorials, and examples

Benchmarks Enhanced:

• benchmark_e2e.py - E2E latency measurements
• benchmark_throughput.py - Throughput measurements
• benchmark_numpy_io.py - NumPy I/O performance (Mahout vs other frameworks)

No existing functionality broken—fully backward compatible.

Original prompt

please help draft a benchmark roadmap(RFC) for Apache Mahout QDP(in qdp/), it should focus on 1. fairness (implement warmup, and others tbd) 2. create nice graph for blogposts or academic papers you should evaluate existing benchmarks, and think about it, there's a person's suggestions:要注意的應該就是 clear cache 和 event + synchronize 的設置，沒有 gradient 的問題就可以忽略下面實作中的那部分 可以用 torch.cuda 的 event 來記錄時間點，最好多跑幾次(記得 clear cache)算出很多個時間，然後算出統計值 (mean, std, err, percentiles, max, median, min 等)，有那些統計值 matplotlib 就很好畫出來了 benchmark 架構大概都長得差不多 https://github.com/pytorch/helion/blob/main/helion/autotuner/benchmarking.py https://github.com/triton-lang/triton/blob/2c3afac48287af8ea5c270722124da9ec1cba2a1/python/triton/testing.py#L127 統計值 https://github.com/triton-lang/triton/blob/2c3afac48287af8ea5c270722124da9ec1cba2a1/python/triton/testing.py#L42 https://github.com/pytorch/helion/blob/main/helion/autotuner/base_search.py#L974 https://github.com/gpu-mode/reference-kernels/blob/main/problems/nvidia/eval_better_bench.py#L109

---

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[ryankert01]

@copilot help finish phase 1

[Copilot]

@copilot help finish phase 1

Phase 1 is complete! ✅

Implemented the complete foundation for fair benchmarking:

Created modules (1,337 lines):

• timing.py: CUDA event timing, warmup, cache clearing
• statistics.py: Full statistical analysis (mean, std, percentiles P25-P99, IQR, CV)
• visualization.py: Publication-ready plots (bar, box, violin charts + tables)
• config.py: YAML configuration management

Testing & docs:

• 249 lines of unit tests (30+ test cases with GPU/CPU markers)
• Complete API documentation with examples
• Example configuration file

Dependencies added: scipy, seaborn, pyyaml

All code compiles successfully. Ready for Phase 2 integration! 🚀

Commit: ef3d488

[ryankert01]

@copilot help do phase 2

[Copilot]

@copilot help do phase 2

Phase 2 is complete! ✅

Integrated statistical mode into both benchmarks:

🔧 benchmark_e2e.py - E2E latency:

• --statistical flag for opt-in analysis
• --warmup N (default: 3) to eliminate JIT overhead
• --repeat N (default: 10) for multiple measurements
• CUDA event-based precise GPU timing
• Full statistics output (mean, std, P25-P99, IQR, CV)

🔧 benchmark_throughput.py - Throughput:

• Same statistical flags
• Dual stats for duration AND throughput
• Default warmup: 2 (optimized for throughput)

📊 Features delivered:

• Warmup mechanism ✅
• CUDA event timing ✅
• Comprehensive statistics ✅
• Console output with format_statistics() ✅
• Backward compatible (works without --statistical) ✅

Usage:

python benchmark_e2e.py --statistical --warmup 5 --repeat 20
python benchmark_throughput.py --statistical --frameworks mahout

All code review issues addressed. Security scan passed (0 alerts).

Commits: 1c5ae07, ffe82fd

[ryankert01]

@copilot help do phase 3

[ryankert01]

@copilot also help do Phase 4: Documentation

[Copilot]

@copilot help do phase 3

Phase 3 is complete! ✅

Added visualization capabilities to both benchmarks:

📊 benchmark_e2e.py - E2E visualization:

• --visualize flag generates publication-ready plots
• --output-dir PATH for custom location (default: ./benchmark_results)
• Generates 4 files: bar chart, box plot, violin plot, markdown table
• Filenames include parameters: e2e_q{qubits}_s{samples}_*.png

📊 benchmark_throughput.py - Throughput visualization:

• Same --visualize and --output-dir flags
• Generates 8 files (2 complete sets):
  • Duration plots (time taken)
  • Throughput plots (vectors/sec)
• Each set has all 4 visualization types

🎨 Visualization features:

• Bar charts with error bars (mean ± std) ✅
• Box plots with quartiles and outliers ✅
• Violin plots with full distributions ✅
• Markdown tables with complete statistics ✅
• 300 DPI publication quality ✅

Usage:

# E2E with visualization
python benchmark_e2e.py --statistical --visualize

# Throughput with custom output
python benchmark_throughput.py --statistical --visualize --output-dir ./results

# Full example for publication
python benchmark_e2e.py --statistical --visualize \
  --warmup 5 --repeat 20 \
  --frameworks mahout-parquet pennylane \
  --output-dir ./paper_figures

Code review feedback addressed. Security scan passed (0 alerts).

Commits: ac6693e, 67ada45

[Copilot]

@copilot also help do Phase 4: Documentation

Phase 4 Documentation is complete! ✅

Created comprehensive documentation:

📚 BEST_PRACTICES.md (11,667 chars):

• Fairness principles (warmup, caches, CUDA timing)
• Statistical rigor (distributions, repetitions, CIs)
• Reproducibility (config, versioning, docs)
• Publication guidelines
• 7 common pitfalls with solutions
• CLI and Python API cheat sheets

📓 statistical_benchmark_tutorial.ipynb (16,592 chars):

• Interactive Jupyter notebook
• 15+ executable code cells
• Part 1: Using benchmark_utils API
• Part 2: Running statistical benchmarks
• Part 3: Creating visualizations
• Part 4: Best practices demos
• Ready for Colab deployment

📖 Enhanced README.md:

• Documentation & Tutorials section
• Visualization examples with file structure
• Notebooks section with Colab badges
• Contributing guidelines
• Links to all resources

Key Topics Covered:

Fairness: Warmup, cache clearing, CUDA events, synchronization

Statistics: Full distributions, repetitions, outlier filtering, confidence intervals

Reproducibility: Save configs, document specs, version control, share settings

Publication: Choose plots, report effect sizes, follow standards

Usage:

# Follow best practices guide
cat BEST_PRACTICES.md

# Run tutorial notebook
jupyter notebook notebooks/statistical_benchmark_tutorial.ipynb

# Or open in Colab (badges in README)

All documentation includes working code examples. Code review passed.

Commits: 63b9311, 644c3f2

[ryankert01]

@copilot we should also supports numpy, which is numpy of mahout vs. other frameworks's numpy impl.

[Copilot]

@copilot we should also supports numpy, which is numpy of mahout vs. other frameworks's numpy impl.

NumPy benchmark support added! ✅

Enhanced benchmark_numpy_io.py with full statistical & visualization capabilities:

📊 Statistical Mode:

• --statistical flag for multi-run analysis
• --warmup N (default: 3) to eliminate JIT overhead
• --repeat N (default: 10) for measurements
• Warmup phase + cache clearing
• Full statistics (mean, std, P25-P99, IQR, CV)
• Dual metrics: duration AND throughput

📊 Visualization Mode:

• --visualize flag generates publication plots
• --output-dir PATH for custom location
• Generates 8 files (2 complete sets):
  • Duration plots (I/O + encoding time)
  • Throughput plots (samples/sec)
• Each set: bar, box, violin, table

✅ Feature Parity: All 3 benchmarks (E2E, Throughput, NumPy) now have identical capabilities

Usage:

# Statistical mode
python benchmark_numpy_io.py --statistical \
  --frameworks mahout,pennylane

# With visualization
python benchmark_numpy_io.py --statistical --visualize \
  --qubits 10 --samples 1000 \
  --output-dir ./numpy_results

Documentation: Complete NumPy benchmark section added to README with examples.

Code review feedback addressed. Security scan passed (0 alerts).

Commits: 34edcb8, 09a8e24