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HDF5 Testing Framework

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H. Joe Lee edited this page Sep 2, 2025 · 7 revisions

HDF5 Testing Framework

Objectives

  • Regression Testing: Verify that updates, bug fixes, or enhancements to the HDF5 library maintain compatibility with previous versions and do not alter the expected behavior of existing features, APIs, or data formats.
  • Performance Evaluation: Evaluate the efficiency, scalability, and reliability of HDF5 operations under various workloads.

Key Areas

Regression Testing:

  • Installation and setup
  • API & Functional testing
  • Compatibility testing:
    • Backward compatibility with File Format
    • Backward compatibility with library versions (version functions tested via GitHub)
  • Platforms & Compilers

Performance Evaluation:

  • Read/write throughput
  • Latency
  • Memory usage (Currently not monitored)
  • I/O patterns across different configurations and environments

Environment Setup

Set up a controlled testing environment that reflects the target deployment scenario:

  • Hardware specifications (CPU, RAM, storage type)
  • Operating system and file system details
  • HDF5 library version and configuration
  • Network setup for distributed or parallel I/O testing

Recommended Tools and Utilities

  • HDF5 command-line utilities: h5perf, h5dump, h5stat
  • Custom benchmarking scripts using h5py or C APIs
    • Other benchmarks to be determined later
  • H5Bench suite:
    • Simulates common HDF5 usage patterns
    • Supports parallel I/O
    • Evaluates I/O overhead and observed I/O rate
    • Includes patterns for synchronous/asynchronous operations, caching, logging, and metadata stress
    • GitHub
    • Documentation
  • Profiling tools: Grafana
  • Monitoring tools: CDash (optional)

Testing Metrics

Regression Testing (Lead: Larry Knox)

  • NOTE: It is the responsibility of the test authors to address these metrics, testing only verifies pass or fail on various configurations.
Metric Description
Backward Compatibility Ensure older HDF5 files can still be read and written correctly
API Stability Confirm that public APIs behave consistently across versions
Data Integrity Validate that data stored and retrieved remains unchanged
Performance Consistency Detect any regressions in read/write performance
Cross-Platform Consistency Ensure consistent behavior across supported platforms and compilers
Error Handling Confirm that known error conditions are still handled correctly

Performance Testing (Lead: Joe Lee)

  • NOTE: It is the responsibility of the test authors to address these metrics, testing only verifies pass or fail on various configurations.
Metric Description
Throughput Measurement Assess read/write speeds for different dataset sizes and access patterns
File Size and Layout Compare performance between contiguous and chunked layouts
Chunking Strategies Evaluate impact of chunk sizes and compression methods
Parallel I/O Test performance with MPI-enabled HDF5 and scalability
Metadata Access Measure time to read/write attributes and nested group structures
Dataset Access Patterns Benchmark selection methods and data type performance
Caching Behavior Analyze effects of chunk cache settings and flushing
Additional Considerations Latency, CPU/memory utilization

Test Scenarios

  • Sequential and random read/write operations
  • Chunked and compressed dataset access
  • Parallel I/O using MPI
  • Large-scale dataset handling
  • Metadata access and update performance

Reporting and Analysis

Document test results with:

  • Summary of test configurations (Larry, Pull from CDash)
  • Tabulated performance metrics
  • Observations and anomalies
  • Recommendations for optimization
  • Comparison with baseline or previous versions

Reference

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