User Guide


Memory Consumption and Allocations View

Explore the data collected with the Memory Consumption analysis for your native or Python* target and identify the most memory-consuming functions, analyze their allocation stacks and source.
Start with the Summary window that displays a list of top memory-consuming functions.
For example, the
function has the highest Memory Consumption metric value and could be a candidate for optimization:
For further investigation, switch to the
tab and explore the memory consumption distribution over time. Focus on the peak values on the
pane, select a time range of interest, right click and use the
Filter In by Selection
context menu option to filter in the program units (functions, modules, processes, and so on) executed during this range:
Memory Consumption Viewpoint
In the example above, the python
function allocated 915 310 048 bytes of memory in a call tree displayed in the
Call Stack
pane on the right but released only 817 830 048 bytes. 92MB is the maximum Allocation/Deallocation delta value that signals a potential memory leak. Clicking the
function opens the Source view highlighting the code line that allocates the maximum memory. Use this information for deeper code analysis to identify a cause of the memory leaks.

Product and Performance Information


Performance varies by use, configuration and other factors. Learn more at