HDF5 Testing Framework

HDF5 Testing Framework? Guidelines?

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

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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)

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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 |

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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