![]() This could be the case for example with processors that support Hyper threading. However sometimes we will get much more virtual cores on the output (compared to the number of physical cores). In case of a system with N processors (and M cores in each processor), the total number of physical cores in the graph will be NxM. Core definitionĪs mentioned before, data will be outputted for every core. on Raspberry Pi 3), but instead the main temperature might be available. on Sun systems)! And on some systems the msg.cores array will be empty (e.g. msg.topic is a fixed text ( temperature)ĬAUTION: the temperature values are not available on all systems (e.g.msg.cores is an array of temperatures of all cores.msg.payload is the main (average) temperature.When this option is selected, a single output message will be generated that contains the temperature values in ☌ (with topic 'temperature'). Single output message with core temperature(s) usage is the core usage (as a percentage).name is the name of the (logical) CPU core ( core_xxx).msg.payload is the array with information of all the available cores:.When this option is selected, a single output message will be generated that contains an array of all CPU usages (with topic 'all_cores'). Single output message with array of core usages This is an example of such an output message: msg.topic is the name of the (logical) CPU core ( core_xxx).msg.payload is the core usage (as a percentage).The output message for each core will look like this: Since every core gets it's own topic ( core_xxx), it becomes very easy to display all the cores in a single graph on the dashboard: When this option is selected, an output message will be generated for each core individually. msg.payload is the overall usage percentage ( sum of usage of all cores / number of cores).There will be a single output message, containing the overall data of all cores: This means that the library uses all 4 available cores, but in total only 25% of the 4 cores. ![]() However when looking at the overall CPU usage, it becomes clear that only 25% of the Raspberry Pi CPU resources is being used: For example, from following graph you might conclude (incorrectly!) that video processing (using the OpenCv library) uses nicely all 4 cores of a Raspberry Pi: Simon Hailes adviced me to add this option, since the graphs per core can become too noisy. ![]() The overall CPU usage is calculated as the average usage of all cores: ![]() When this option is selected, a single output message will be generated that contains the overall CPU usage (with topic 'overall'). Single output message for overall cpu usage As a result, it is advised to apply a trigger message every second on the input. The calculated CPU usage is the average usage since the previous calculation, so the calculated value will become more accurate when the period between successive calculations is small. This node will monitor the CPU usage, based on the Node.js OS Library.Ī trigger message should be send to the input, every time the CPU usage should be recalculated. Run the following npm command in your Node-RED user directory (typically ~/.node-red): npm install node-red-contrib-cpu A Node Red node for monitoring CPU usage Install ![]()
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