Basics

Profiling is a process in software development and optimization and refers to the process of analyzing a software application to understand various aspects of its execution, such as the duration of function calls. The aim of profiling is to identify performance bottlenecks, inefficient code and unexpected behavior in order to improve the performance and efficiency of the application.

The TwinCAT 3 PLC Profiler uses the "instrumentation" measurement principle, where specific marks (entry and exit methods) are inserted into the code in order to measure the execution times of individual function blocks or code segments. One of the biggest advantages lies in the detailed insights that the process offers. It enables the accurate measurement of execution times and the frequency of calls to each individual function block within the program. This precision in measurement is essential to understand how the code behaves and where there may be potential for improvement.

Optimization also plays a key role. The insights gained not only allow you to identify performance bottlenecks, but also to address and eliminate them in a targeted manner. This leads to improved efficiency and performance of the application, which is particularly important in time-sensitive and resource-constrained environments. However, there are also important aspects to consider that can influence the results and their interpretation. On the one hand, the measurement marks that are placed in the code lead to an additional load on the system. This can affect the actual execution times and should be taken into account when analyzing the measurement results. On the other hand, this can lead to unwanted weighting: areas of the code that are provided with many measurement marks may appear over-represented in the analysis because the measurement methods themselves take time. This effect must be taken into account when analyzing the data to ensure an accurate interpretation of the code's performance.

In the further application of the TwinCAT 3 PLC Profiler, various challenges resulting from the complexity and dynamics of advanced PLC systems have to be taken into account. One such challenge is the inter-cycle variability of measured values caused by dynamic CPU clocking and other factors. Although measures such as using isolated cores or disabling power management mechanisms can reduce variability, it remains an issue that affects the interpretation of profiling results.

In addition, the implementation of the trace procedure, which measures the entire call tree in each cycle, can lead to an increase in real-time load, which can affect the performance of the system. Another issue is the increase in network utilization due to the transmission of measured values via ADS, especially with high information density. This increase in network traffic must be considered when planning profiling activities.

By taking a careful approach, developers can gain valuable insights into the performance of their applications and make targeted optimizations to improve the efficiency and reliability of their systems.