How to activate Eventrecorder together with RTX5 using Cortex-M0?

Activating the Eventrecorder together with RTX5 on a Cortex-M0 microcontroller provides powerful insights into the real-time behavior of your embedded system. The Eventrecorder module lets you trace context switches, interrupts, scheduler events, and custom instrumentation hooks added to your RTX5-based application. When correlated against your task timing and scheduling requirements, the trace data reveals vital details about the deterministic operation of your system.

Overview of Eventrecorder and RTX5

The Eventrecorder module integrated into Cortex-M0 MCUs gives complete visibility into the processor’s internal state transitions. By tracing time-stamped event records into an on-chip or off-chip memory buffer, you can capture accurate timing of interrupts, context switches, and other system events with minimal intrusion on your application. Common uses of Eventrecorder tracing include:

• Validating task schedulability and runtimes
• Profiling interrupt response latencies
• Analyzing operating system kernel behavior
• Debugging complex timing-related bugs

RTX5 is a deterministic real-time operating system designed specifically for Cortex-M devices. It implements preemptive multitasking with configurable task priorities and efficient scheduling algorithms like round-robin and time slicing. RTX5 also provides inter-task synchronization and communication primitives like mutexes, semaphores, queues, and event flags.

When used together, the Eventrecorder and RTX5 give detailed visibility into the timing interactions and causality chains between application tasks, interrupts, kernel events, and other system activities. You can validate that your task deadlines are met under all conditions and quickly trace unexpected latencies or overruns.

Configuring Eventrecorder

Follow these steps to configure the Eventrecorder module:

1. Enable Eventrecorder clock in RCC register.
2. Configure Eventrecorder control register for circular buffer wrap-around.
3. Set Eventrecorder master enable bit to activate tracing.
4. Optionally enable trace start/stop logic connected to your application.
5. Configure priority filtering to capture relevant events only.
6. Set trace buffer address and size aligned to 4 bytes.

Typical buffer sizes range from 1KB to 32KB for on-chip RAM. Larger trace buffers of 256KB to 1MB can reside in external memory. Use the smallest feasible buffer that satisfies your tracing objectives.

You must allocate trace memory exclusively for the Eventrecorder before activating it. The buffer consumes this statically reserved memory while tracing is enabled.

Instrumenting RTX5 for Event Tracing

To capture task scheduling events, you need to add Eventrecorder instrumentation hooks to the RTX5 kernel. Key points of integration include:

• Record context switches by calling Eventrecorder API on task switch.
• Trace kernel services like task delay, notification, semaphore pend/post.
• Log scheduler time slice events and other core activities.

Minimal changes to a couple RTX5 files integrate the event tracing hooks. The hooks should be called before and after the actual kernel service to record timing and causality. No changes are required to application tasks themselves.

You can extend instrumentation to other system layers, like the hardware abstraction layer or device drivers. Having selective event tracing in lower-level components provides further insight during debug.

Capturing Event Traces

With Eventrecorder and RTX5 configured, you can capture detailed traces during real-time simulation and testing. Frame-based tools integrate the trace reconstruction and visualization:

• Debug probes like SEGGER J-Trace Pro capture Eventrecorder data.
• Frame analyzers format the data into a timeline view for causality analysis.
• Cross-correlation against other system signals validates timing.

For less complex tracing needs, you can log the Eventrecorder buffer to a file and post-process it into human-readable format. The event records contain timestamp, event ID, and optional data fields to log details like task IDs or semaphore counts.

Try to isolate trace capture to the specific test scenarios of interest. Tracing does intrude slightly on system timing, so leave it disabled for normal operation. Targeted tracing during integration testing or diagnosing failures provides the most value.

Analyzing Eventrecorder Traces

Effective trace analysis requires importing the Eventrecorder data into an intelligent visualization tool. A chronological timeline view of runtime events is essential to see ordering, causality, and temporal relationships in the system:

• Identify task scheduling policies andActual task runtimes versus programmed durations.
• Detect missed deadlines or overruns signaling bugs.
• Discover long latency interrupts impacting scheduling.
• Visualize interactions between tasks, interrupts, and kernel.

Drill down on specific events to see details like stack traces or task priority transitions. Filtering the timeline on particular event IDs or task IDs isolates relevant transactions. Statistical profiling can reveal hotspots needing optimization.

Cross-reference trace timeline against execution profile counters or instrumentation variables in tasks and ISRs. Correlating trace events to other measurements validates timing and system behavior.

Iteratively refine tracing setup and analysis to hone in on issues. The Eventrecorder provides a powerful window into dynamic real-time interactions difficult to observe otherwise.

Optimizing System Performance with Traces

The Eventrecorder enables data-driven optimization of real-time system performance:

• Tune task priority assignments to meet deadlines under worst-case loads.
• Adjust kernel time slices for fairer scheduling under overload.
• Reduce interrupt latencies that negatively impact task timing.
• Identify poorly written tasks hogging CPU to remedy.

You can also mine event traces to derive key performance metrics:

• Worst event interrupt latencies.
• Minimum interrupt-to-task latency.
• Ideal task time quanta by profiling scheduler.
• Frequency and duration of task deadline misses.
• Kernel overhead as percentage of execution time.

Traces serve as a benchmark to compare optimization attempts and quantify improvements. The more data you extract, the higher performance your real-time system can achieve.

Conclusion

Activating the Eventrecorder provides invaluable visibility into runtime interactions within an RTX5-based Cortex-M0 system. Careful instrumentation exposes the scheduling and timing behavior missing from static analysis. By integrating trace capture and visualization into your development workflow, you can validate real-time performance and quickly debug complex issues that arise.