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Hello everyone, happy Sunday!!
I continue new experiments on @ionet , this time my target is Apple Mac devices. The code snippet you see on the screen uses the Ray library to perform distributed calculations. Ray is a library designed to execute Python code in a parallel and distributed manner. The code is designed to handle heavy workloads continuously in a loop 🧙♂️
Each heavy workload is defined in a function called heavy_task. This function performs intensive calculations on large matrices. Each workload creates a randomly large matrix and then performs a series of matrix multiplications on these matrices. These matrix multiplications tax the CPU more and create a more intensive workload. ⚡️🧮
The loop is designed to handle these heavy workloads continuously. At each step, a workload is sent to the processors in the cluster via the Ray library and executed in parallel. This constantly keeps the CPUs busy and allows them to do more processing.💻
However, constantly running such intensive workloads can excessively consume system resources and negatively impact performance.
SO BE CAREFUL IF YOU ARE GOING TO USE CODE ON YOUR PERSONAL MAC DEVICE. ‼️
In the video you can see exactly how I overloaded the system.Give me all the resources, I love dealing with them 😁 🔥💻👀
Don't believe what anyone says, come and experience it yourself. Onwards!!
@shadid_io @0xHushky @hanzthehuman @ionet @GauravTdhinait @billwan28848539 @iot_is_life @okansariirmak @eli5defi @HouseofChimera @noble1noble1 @_d3f4ult


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