Aexus Answers

PTP Performance Benchmarking: Automated Accuracy & Latency Tools

Alex Johnson
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PTP Performance Benchmarking: Automated Accuracy & Latency Tools

In the realm of precision timing, performance benchmarking tools are indispensable for evaluating and optimizing PTP (Precision Time Protocol) implementations. This article delves into the development of automated tools designed to measure, validate, and compare the accuracy, latency, CPU overhead, and resource utilization of PTP implementations, especially those adhering to the IEEE 1588-2019 standard. Such tools are crucial for ensuring compliance, comparing platforms, detecting regressions, guiding optimizations, and conducting competitive analysis.

Feature Description

The core objective is to develop automated performance benchmarking tools that can meticulously measure, validate, and compare various aspects of PTP implementation. This includes accuracy, latency, CPU overhead, and overall resource usage. The focus is on creating a comprehensive suite that can be integrated into continuous integration and continuous delivery (CI/CD) pipelines, providing real-time feedback on performance metrics.

Motivation

The motivation behind developing these tools stems from the critical need for performance validation in several key areas:

  • Compliance Verification: Ensuring that PTP implementations meet the stringent accuracy requirements defined by the IEEE 1588-2019 standard is paramount. Automated benchmarking tools provide a reliable means of verifying this compliance.
  • Platform Comparison: Different hardware and software platforms exhibit varying performance characteristics. These tools enable a fair comparison of PTP performance across different HAL (Hardware Abstraction Layer) implementations.
  • Regression Detection: In a dynamic development environment, performance degradation can occur with each new commit. Automated benchmarking helps detect these regressions early in the development cycle, preventing them from propagating into production.
  • Optimization Guidance: Identifying bottlenecks and optimization opportunities is crucial for maximizing performance. Benchmarking tools provide the data needed to pinpoint areas where improvements can be made.
  • Competitive Analysis: Comparing PTP implementations with alternatives like linuxptp and ptpd is essential for understanding the strengths and weaknesses of each. Automated tools facilitate this comparison, offering insights into relative performance.

Currently, performance testing is primarily manual, lacking an automated benchmarking framework and comprehensive performance data collection. The target is to create a comprehensive benchmarking suite located in tests/benchmarks/ and tools/benchmarking/.

Use Case

The primary use case for these tools is continuous performance monitoring. By automatically running benchmarks on every commit, it becomes possible to detect subtle but significant changes in performance:

  • Timing Accuracy Regression: Detecting any degradation in timing accuracy, such as a >10ns increase in offset.
  • CPU Overhead Increase: Identifying any increase in CPU overhead, such as a >2% rise in CPU usage.
  • Memory Leaks: Spotting memory leaks, indicated by a >1KB increase in memory usage over an hour.
  • Network Packet Loss: Detecting increases in network packet loss, such as a >0.1% increase.

These tools benefit several key stakeholders:

  • Core Library Developers: Optimize PTP library performance, identify performance bottlenecks and areas for improvement.
  • Platform Integrators: Validate HAL implementations, ensure proper performance for target platforms.
  • System Integrators: Select appropriate platforms, make informed decisions about hardware and software based on performance data.
  • End Users: Understand performance characteristics, gain insights into the behavior of PTP implementations in real-world scenarios.

The intended frequency of use is:

  • Every Commit: Automated benchmarks in CI/CD pipelines to catch regressions early.
  • Every Release: Comprehensive performance reports to document the state of the library.
  • On-Demand: Performance troubleshooting to diagnose and resolve issues as they arise.

Proposed Implementation

The proposed implementation involves a structured architecture comprising several key components.

Architecture

The proposed architecture is organized into two main directories:

  • tests/benchmarks/: Contains the actual benchmark tests, categorized by the aspect of performance being measured (accuracy, latency, throughput, resources, and comparison).
  • tools/benchmarking/: Contains the tools used to orchestrate benchmark execution, generate reports, visualize data, detect regressions, and integrate with CI/CD pipelines.
tests/benchmarks/
├── accuracy/
│   ├── test_offset_accuracy.cpp        # Measure offset from master accuracy
│   ├── test_path_delay_accuracy.cpp    # Measure path delay accuracy
│   ├── test_bmca_convergence.cpp       # Measure BMCA convergence time
│   └── test_frequency_stability.cpp    # Measure frequency adjustment stability
├── latency/
│   ├── test_sync_latency.cpp           # Sync message processing latency
│   ├── test_delay_req_latency.cpp      # Delay_Req processing latency
│   ├── test_announce_latency.cpp       # Announce processing latency
│   └── test_end_to_end_latency.cpp     # Total synchronization latency
├── throughput/
│   ├── test_message_rate.cpp           # Max sustainable message rate
│   ├── test_packet_loss.cpp            # Packet loss under load
│   └── test_network_saturation.cpp     # Network bandwidth usage
├── resources/
│   ├── test_cpu_usage.cpp              # CPU overhead measurement
│   ├── test_memory_usage.cpp           # Memory footprint and leaks
│   ├── test_context_switches.cpp       # Context switch overhead
│   └── test_interrupt_latency.cpp      # Interrupt service routine latency
└── comparison/
    ├── test_vs_linuxptp.cpp            # Compare with linuxptp (ptp4l)
    ├── test_vs_ptpd.cpp                # Compare with ptpd
    └── test_platform_comparison.cpp    # Compare HAL implementations

tools/benchmarking/
├── benchmark_runner.py                 # Orchestrates benchmark execution
├── benchmark_report_generator.py       # Generate HTML/PDF reports
├── benchmark_visualizer.py              # Plot graphs and charts
├── regression_detector.py              # Detect performance regressions
└── ci_integration/
    ├── github_actions_benchmark.yml    # GitHub Actions workflow
    ├── benchmark_comment_bot.py        # Comment results on PRs
    └── performance_dashboard.html      # Real-time dashboard

reports/benchmarks/
├── accuracy_reports/
├── latency_reports/
├── throughput_reports/
├── resource_reports/
└── comparison_reports/

Key Metrics to Measure

1. Timing Accuracy (Critical)

Timing accuracy is of utmost importance in PTP implementations. The goal is to ensure that the slave clock is accurately synchronized with the master clock. Here's how we'll measure it:

  • Offset from Master: This metric measures the actual time difference between the slave clock and the master clock. The target is to achieve an RMS (Root Mean Square) offset of less than 100ns and a maximum offset of less than 1μs. The test duration will be 1 hour of continuous measurement. This requires precise measurement techniques and stable reference clocks to ensure reliable results. Understanding the sources of error, such as network asymmetry and clock jitter, is crucial for accurate assessment. Statistical analysis of the offset data, including mean, standard deviation, and RMS values, provides a comprehensive view of the synchronization quality. Long-term monitoring helps identify any drift or instability in the clock synchronization.
  • Path Delay Accuracy: This measures the accuracy of the calculated network delay between the master and slave clocks. The target is to achieve an error of less than 50ns. Accurate path delay measurement is essential for compensating for network delays and achieving precise synchronization. The test setup involves measuring the delay between sending a sync message from the master and receiving it at the slave, and vice versa. Advanced techniques, such as using hardware timestamping, can improve the accuracy of path delay measurements. Calibration of the test network is necessary to minimize any systematic errors in the measurements.
  • Frequency Stability: This measures the stability of the frequency adjustment mechanism in the PTP implementation. The target is to achieve a drift of less than 1 ppb (part per billion). Frequency stability is critical for maintaining accurate time synchronization over extended periods. The test involves measuring the frequency drift of the slave clock relative to the master clock over a long duration. Allan deviation calculation is used to quantify the frequency stability. Environmental factors, such as temperature variations, can affect frequency stability, so it's essential to control these factors during testing. Regular monitoring of frequency stability helps ensure that the PTP implementation meets the required synchronization performance.

2. Latency (Important)

Latency refers to the time it takes for PTP messages to be processed and for synchronization to be achieved. Minimizing latency is crucial for real-time applications. Here's how we'll measure it:

  • Sync Processing: Measures the time from when a Sync packet is received to when the timestamp is captured. The target is to keep this under 10μs. Reducing Sync processing latency improves the responsiveness of the PTP system. Efficient interrupt handling and optimized timestamp capture mechanisms are key to minimizing this latency. Detailed analysis of the Sync processing path helps identify any bottlenecks or inefficiencies. Real-time operating systems (RTOS) can provide deterministic timing for Sync processing. Optimizing the data structures used in Sync message processing can also reduce latency.
  • Delay_Req Processing: Measures the time from when a trigger occurs to when the Delay_Req packet is transmitted. The target is to keep this under 10μs. Minimizing Delay_Req processing latency ensures timely communication of delay information between master and slave clocks. Efficient queuing mechanisms and prioritized packet transmission can reduce this latency. Understanding the factors that contribute to Delay_Req processing time, such as interrupt handling and packet encapsulation, is essential for optimization. Real-time scheduling can provide deterministic timing for Delay_Req processing.
  • BMCA Convergence: Measures the time it takes for the Best Master Clock Algorithm (BMCA) to converge and select a new master after a network change. The target is to meet the IEEE requirement of less than 2 seconds. Fast BMCA convergence is essential for maintaining synchronization in dynamic network environments. Optimizing the BMCA algorithm and reducing the number of messages exchanged during convergence can improve convergence time. Implementing techniques, such as pre-selection of potential master clocks, can speed up the convergence process. Continuous monitoring of BMCA convergence time helps ensure network resilience.
  • End-to-End Sync: Measures the total time it takes to achieve synchronization accuracy of less than 1μs. The target is to achieve this in less than 30 seconds for a cold start. Reducing end-to-end synchronization time improves the initial synchronization performance of the PTP system. Efficient clock synchronization algorithms and fast clock adjustment mechanisms are key to minimizing this time. Reducing the initial clock offset and frequency difference between master and slave clocks can speed up synchronization. Adaptive clock adjustment techniques can optimize the synchronization process.

3. Throughput (Scalability)

Throughput measures the ability of the PTP implementation to handle a high volume of PTP messages without performance degradation. It's essential for scalability in large networks. Here’s what needs to be considered:

  • Message Rate: Measure the maximum sustainable rate of Sync, Announce, and Delay messages per second. The target is to exceed 16 Sync messages/sec and 1 Announce message/sec (IEEE minimum). Achieving high message rates ensures that the PTP system can handle the traffic demands of a large network. Optimizing message processing and reducing overhead are key to maximizing message rates. Hardware acceleration techniques, such as offloading message processing to a network interface card (NIC), can improve message rates. Load balancing across multiple CPU cores can also enhance message rate performance.
  • Packet Loss: Measure the percentage of PTP packets lost under load. The target is to maintain packet loss below 0.01% at rated load. Minimizing packet loss ensures reliable delivery of PTP messages and accurate time synchronization. Implementing quality of service (QoS) mechanisms and prioritizing PTP traffic can reduce packet loss. Reducing network congestion and optimizing buffer management can also improve packet loss performance. Monitoring packet loss helps identify and address network issues that may affect synchronization quality.
  • Network Bandwidth: Measures the total PTP traffic overhead. The target is to keep this below 100 Kbps for a single slave. Reducing network bandwidth usage minimizes the impact of PTP traffic on other network applications. Optimizing message sizes and reducing the frequency of unnecessary messages can lower bandwidth usage. Using compression techniques to reduce message sizes can also improve bandwidth efficiency. Network traffic analysis helps identify opportunities to optimize bandwidth usage.

4. Resource Usage (Efficiency)

Resource usage refers to the CPU, memory, and other system resources consumed by the PTP implementation. Minimizing resource usage is crucial for embedded systems and resource-constrained environments. Here's what needs to be checked:

  • CPU Overhead: Measures the percentage of CPU used by PTP processing. The target is to keep this below 5% on desktop systems and below 10% on embedded systems. Reducing CPU overhead frees up resources for other applications and improves overall system performance. Optimizing PTP processing algorithms and reducing unnecessary calculations are key to minimizing CPU usage. Offloading PTP processing to dedicated hardware can also reduce CPU overhead. Profiling tools can help identify CPU-intensive operations and guide optimization efforts.
  • Memory Footprint: Measures the RAM usage (static + dynamic). The target is to keep the total memory footprint below 1MB and dynamic memory usage below 500KB. Minimizing memory footprint reduces the demand on system memory and improves the scalability of the PTP implementation. Optimizing data structures and reducing unnecessary memory allocations are key to minimizing memory usage. Using memory pooling techniques and avoiding memory leaks can also improve memory efficiency. Memory analysis tools can help identify memory-intensive operations and guide optimization efforts.
  • Memory Leaks: Measures memory growth over time. The target is to ensure that there are 0 bytes leaked over 24 hours. Preventing memory leaks ensures long-term stability and reliability of the PTP implementation. Thoroughly testing and validating memory management code is essential for preventing leaks. Using memory leak detection tools, such as Valgrind, can help identify and resolve leaks. Regular monitoring of memory usage can help detect leaks early.
  • Interrupt Latency: Measures the Interrupt Service Routine (ISR) execution time. The target is to keep this below 5μs. Reducing interrupt latency improves the responsiveness of the PTP system and ensures timely processing of PTP messages. Optimizing ISR code and reducing the number of instructions executed in the ISR are key to minimizing interrupt latency. Using techniques, such as deferred interrupt processing, can also improve interrupt performance. Interrupt analysis tools can help identify sources of interrupt latency and guide optimization efforts.

Implementation Tasks

The implementation is divided into several phases, each focusing on a specific aspect of the benchmarking framework.

Phase 1: Benchmarking Framework

  • [ ] Google Benchmark integration
    • [ ] CMake configuration
    • [ ] Fixture setup/teardown
    • [ ] Result export (JSON, CSV)
  • [ ] Custom PTP benchmark harness
    • [ ] Mock network interface for controlled testing
    • [ ] Simulated clock for deterministic testing
    • [ ] Virtual PTP network (multiple nodes)
  • [ ] Benchmark utilities
    • [ ] Statistics calculation (mean, stddev, percentiles)
    • [ ] Timestamp collection and analysis
    • [ ] Performance counters (CPU cycles, cache misses)

Phase 2: Accuracy Benchmarks

  • [ ] Offset accuracy measurement
    • [ ] Compare local clock to reference (GPS, OCXO)
    • [ ] Calculate RMS offset over time
    • [ ] Measure maximum/minimum offset
    • [ ] Plot offset vs time
  • [ ] Path delay accuracy
    • [ ] Known-delay test network
    • [ ] Compare measured vs actual delay
    • [ ] Asymmetry detection
  • [ ] Frequency stability
    • [ ] Allan deviation calculation
    • [ ] Long-term drift measurement (24+ hours)

Phase 3: Latency Benchmarks

  • [ ] Packet processing latency
    • [ ] Sync message processing time
    • [ ] Delay_Req/Resp processing time
    • [ ] Announce message processing time
  • [ ] BMCA convergence latency
    • [ ] Master failure detection time
    • [ ] New master selection time
    • [ ] Sync re-establishment time
  • [ ] End-to-end synchronization latency
    • [ ] Cold start to sync time
    • [ ] Warm restart to sync time

Phase 4: Throughput Benchmarks

  • [ ] Message rate testing
    • [ ] Vary Sync rate: 1, 2, 4, 8, 16, 32, 64, 128 messages/sec
    • [ ] Measure packet loss at each rate
    • [ ] Determine maximum sustainable rate
  • [ ] Network saturation testing
    • [ ] Add background network traffic
    • [ ] Measure PTP performance degradation
  • [ ] Multi-slave scalability
    • [ ] 1, 10, 50, 100 slaves on same master
    • [ ] Measure per-slave accuracy

Phase 5: Resource Usage Benchmarks

  • [ ] CPU profiling
    • [ ] Per-function CPU time (gprof, perf)
    • [ ] Hotspot identification
    • [ ] CPU usage under different Sync rates
  • [ ] Memory profiling
    • [ ] Valgrind memcheck for leaks
    • [ ] Heap/stack usage measurement
    • [ ] Memory allocation patterns
  • [ ] System call overhead
    • [ ] strace/ltrace analysis
    • [ ] Context switch measurement

Phase 6: Comparison Benchmarks

  • [ ] Compare with linuxptp (ptp4l)
    • [ ] Same network, same master
    • [ ] Measure accuracy difference
    • [ ] Measure resource difference
  • [ ] Compare with ptpd
    • [ ] Same test conditions
    • [ ] Performance comparison report
  • [ ] Platform comparison
    • [ ] Same library, different HAL implementations
    • [ ] Identify best-performing platforms

Phase 7: Continuous Integration

  • [ ] GitHub Actions benchmark workflow
    • [ ] Run on every commit (fast benchmarks)
    • [ ] Run nightly (comprehensive benchmarks)
  • [ ] Regression detection
    • [ ] Compare current vs baseline
    • [ ] Automatic alerts on regression >10%
  • [ ] Performance dashboard
    • [ ] Real-time results visualization
    • [ ] Historical trend analysis
    • [ ] Per-platform comparison charts

Benchmark Report Format

The benchmark report will provide a comprehensive overview of the PTP implementation's performance, including detailed results for each metric.

====================================================================
IEEE 1588-2019 PTP Library - Performance Benchmark Report
====================================================================
Date: 2025-11-12
Version: v1.0.0-MVP
Platform: Linux x86_64 (Intel i7-8700K @ 3.7 GHz)
Network: 1 Gbps Ethernet (hardware timestamping enabled)
Compiler: GCC 11.4.0 (-O3 -march=native)
====================================================================

[TIMING ACCURACY]
------------------------------------------------------------------
Offset from Master (1 hour test):
  Mean Offset:          48.2 ns
  Std Deviation:        12.5 ns
  RMS Offset:           49.8 ns       ✓ PASS (<100ns target)
  Max Offset:           287 ns        ✓ PASS (<1μs target)
  Min Offset:           -302 ns

Path Delay Accuracy:
  Measured Delay:       5,432 ns
  Actual Delay:         5,425 ns
  Error:                7 ns           ✓ PASS (<50ns target)

Frequency Stability (24 hour test):
  Initial Frequency:    +15.2 ppb
  Final Frequency:      +14.8 ppb
  Drift:                0.4 ppb        ✓ PASS (<1ppb target)

[LATENCY]
------------------------------------------------------------------
Sync Processing:        6.2 μs         ✓ PASS (<10μs target)
Delay_Req Processing:   5.8 μs         ✓ PASS (<10μs target)
Announce Processing:    12.3 μs        ⚠ WARNING (>10μs)
BMCA Convergence:       1.2 sec        ✓ PASS (<2sec target)
End-to-End Sync:        18.5 sec       ✓ PASS (<30sec target)

[THROUGHPUT]
------------------------------------------------------------------
Max Sync Message Rate:  128 msg/sec    ✓ PASS (>16 target)
Packet Loss @ 16 Hz:    0.001%         ✓ PASS (<0.01% target)
Packet Loss @ 128 Hz:   0.035%         ⚠ WARNING (>0.01%)
Network Bandwidth:      87 Kbps        ✓ PASS (<100Kbps target)

[RESOURCE USAGE]
------------------------------------------------------------------
CPU Usage (16 Hz Sync): 2.3%           ✓ PASS (<5% target)
Memory (Static):        245 KB
Memory (Dynamic):       178 KB
Total Memory:           423 KB         ✓ PASS (<1MB target)
Memory Leaks (24h):     0 bytes        ✓ PASS (0 target)
Interrupt Latency:      3.1 μs         ✓ PASS (<5μs target)

[COMPARISON WITH LINUXPTP]
------------------------------------------------------------------
                        This Library    linuxptp    Difference
Offset RMS:             49.8 ns        52.3 ns     -4.8% (better)
CPU Usage:              2.3%           3.1%        -25.8% (better)
Memory Usage:           423 KB         892 KB      -52.6% (better)
BMCA Convergence:       1.2 sec        1.1 sec     +9.1% (worse)

[OVERALL ASSESSMENT]
------------------------------------------------------------------
✓ PASS: 14 metrics
⚠ WARNING: 2 metrics (Announce latency, packet loss @ 128Hz)
✗ FAIL: 0 metrics

Overall Performance: EXCELLENT
IEEE 1588-2019 Compliance: YES
====================================================================

Files to Create

Accuracy Tests:

tests/benchmarks/accuracy/test_offset_accuracy.cpp
tests/benchmarks/accuracy/test_path_delay_accuracy.cpp
tests/benchmarks/accuracy/test_frequency_stability.cpp

Latency Tests:

tests/benchmarks/latency/test_sync_latency.cpp
tests/benchmarks/latency/test_bmca_convergence.cpp

Resource Tests:

tests/benchmarks/resources/test_cpu_usage.cpp
tests/benchmarks/resources/test_memory_usage.cpp

Tools:

tools/benchmarking/benchmark_runner.py
tools/benchmarking/benchmark_report_generator.py
tools/benchmarking/regression_detector.py

Example Benchmark Code

// tests/benchmarks/accuracy/test_offset_accuracy.cpp
#include <benchmark/benchmark.h>
#include "ptp_clock.h"
#include "ptp_messages.h"

// Measure clock offset accuracy over time
static void BM_ClockOffsetAccuracy(benchmark::State& state) {
    // Setup: Initialize PTP clock
    ptp_clock_t clock;
    ptp_clock_init(&clock);
    
    // Reference time (simulated GPS or OCXO)
    uint64_t reference_time_ns = get_reference_time();
    
    std::vector<int64_t> offsets;
    
    for (auto _ : state) {
        // Get current PTP clock time
        uint64_t ptp_time_ns = ptp_clock_get_time(&clock);
        
        // Calculate offset
        int64_t offset_ns = (int64_t)ptp_time_ns - (int64_t)reference_time_ns;
        offsets.push_back(offset_ns);
        
        // Wait 100ms between samples
        std::this_thread::sleep_for(std::chrono::milliseconds(100));
    }
    
    // Calculate statistics
    double mean = calculate_mean(offsets);
    double stddev = calculate_stddev(offsets);
    double rms = calculate_rms(offsets);
    
    // Report custom metrics
    state.counters["MeanOffset_ns"] = mean;
    state.counters["StdDev_ns"] = stddev;
    state.counters["RMS_ns"] = rms;
    state.counters["MaxOffset_ns"] = *std::max_element(offsets.begin(), offsets.end());
    state.counters["MinOffset_ns"] = *std::min_element(offsets.begin(), offsets.end());
    
    // Validate against IEEE requirements
    if (rms > 100.0) {
        state.SkipWithError("RMS offset exceeds 100ns IEEE target");
    }
}
BENCHMARK(BM_ClockOffsetAccuracy)->Iterations(3600);  // 1 hour @ 1 sample/sec

// Measure Sync message processing latency
static void BM_SyncProcessingLatency(benchmark::State& state) {
    ptp_message_t sync_msg;
    create_sync_message(&sync_msg);
    
    for (auto _ : state) {
        // Start timing
        auto start = std::chrono::high_resolution_clock::now();
        
        // Process Sync message
        ptp_process_sync_message(&sync_msg);
        
        // End timing
        auto end = std::chrono::high_resolution_clock::now();
        
        auto latency_ns = std::chrono::duration_cast<std::chrono::nanoseconds>(end - start).count();
        state.counters["Latency_ns"] = latency_ns;
    }
    
    // Report mean latency
    state.SetLabel("Sync Processing");
}
BENCHMARK(BM_SyncProcessingLatency)->Iterations(10000);

BENCHMARK_MAIN()

Impact Assessment

The development of these benchmarking tools is estimated to be a medium-sized effort, requiring approximately 2-3 months.

Effort Estimate: Medium (2-3 months)

Breakdown:

  • Framework setup: 2 weeks
  • Accuracy benchmarks: 3 weeks
  • Latency benchmarks: 2 weeks
  • Throughput benchmarks: 2 weeks
  • Resource benchmarks: 2 weeks
  • Comparison benchmarks: 2 weeks
  • CI integration: 2 weeks
  • Documentation: 1 week

Breaking Changes: None

  • Benchmarks are in separate test suite
  • Does not affect library code

Performance Impact: N/A (testing only)

Dependencies

Required:

  • v1.0.0-MVP: IEEE 1588-2019 PTP core
  • Google Benchmark library (for C++ benchmarks)
  • Python 3.8+ (for reporting tools)

Optional:

  • Valgrind (for memory profiling)
  • perf/gprof (for CPU profiling)
  • gnuplot/matplotlib (for visualization)
  • Hardware timestamping capable NIC (for accurate measurements)

Related Work

Existing Benchmarking Tools:

  • ptp4l test suite: Part of linuxptp, basic tests
  • ptpd benchmark scripts: Limited automated testing
  • Google Benchmark: C++ microbenchmarking framework

Related GitHub Issues:

  • #6: Additional HAL Implementations (need benchmarking per platform)
  • All other issues (performance validation needed)

Testing Requirements

  • [ ] Benchmark suite runs successfully on CI
  • [ ] All benchmarks complete without errors
  • [ ] Regression detection works correctly
  • [ ] Reports generated correctly (HTML, PDF, JSON)
  • [ ] Dashboard updates automatically
  • [ ] Comparison with linuxptp works
  • [ ] Platform comparison works

Documentation Requirements

  • [ ] Benchmarking Guide: How to run benchmarks, interpret results
  • [ ] Performance Report: Current library performance characteristics
  • [ ] Optimization Guide: How to optimize for different use cases
  • [ ] Platform Performance Comparison: Results across all HAL implementations
  • [ ] Troubleshooting: Common performance issues and solutions

Milestones

  • v1.1.0: Basic benchmarking framework + accuracy/latency tests (Q1 2026)
  • v1.2.0: Throughput + resource tests, CI integration (Q2 2026)
  • v1.3.0: Comparison tests, performance dashboard (Q3 2026)
  • v2.0.0: Comprehensive platform comparison (Q4 2026)

Willing to Contribute?

This feature benefits from:

  • Performance testing expertise
  • Profiling and optimization skills
  • Python scripting (for reporting tools)
  • Data visualization experience

See CONTRIBUTING.md for how to get involved.

Further Reading: For more in-depth information on PTP and performance benchmarking, visit the IEEE 1588 standards page. This resource provides detailed specifications and updates on the Precision Time Protocol.

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