When Linux schedules a thread on a CPU core, there is no guarantee which memories the thread will access. If the workload is lucky, the thread will use data that is already in CPU caches or in a memory that is close to the CPU core. But if not, millions of memory operations need to travel a longer way to reach the physical memory. Yet this may sound too low-level to be controllable and make a difference, you can easily help the scheduler running Kubernetes workloads, and make a big difference! Antti and Sasha show how to get a lot more computing power out of your CPUs by adding CRI Resource Manager (CRI-RM) on your Kubernetes nodes. CRI-RM affects process scheduling and memory locality by dynamically managing CPU and memory pinning of all Kubernetes containers on the node. In case studies CRI-RM has given major improvements in database and AI training performances without any workload-specific configurations or changes to upstream Kubernetes components.
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