Core Management - How to Properly Manage Your Processing Power

At first glance, it seems that all cores in a processor are identical and that the system “already knows” how to use them. But when it comes to Inference Optimization, these small differences are what separate a slow system from a truly efficient one.

What is “Core Management”?

When a model runs, it doesn’t run “in the air” - it runs on threads (sub-processes). The operating system decides which cores to run each thread on and sometimes moves them between cores based on load - a process called context switching.

This sounds smart, but there’s a catch: Every time a thread “migrates” from one core to another, it loses the cache content stored in the previous core.

Why Does This Happen?

Each core has its own cache - a small, fast memory where data and intermediate computation results are stored. As long as the thread runs on the same core, it benefits from quick access to this data - without accessing the much slower main memory.

But when the operating system moves the thread to another core:

• The new core doesn’t recognize the data stored by the previous one.
• The processor has to reload all the data from main memory.
• The cache resets, and the system “starts from scratch” in terms of speed.

This creates a situation where the processor wastes time reloading data instead of performing actual computations - directly resulting in higher latency and lower throughput.

How to Manage This Properly

Instead of letting the system “decide on its own,” it’s better to determine:

• Which processes run on which cores.
• Which threads don’t compete for the same resources.

This is called Thread Affinity - binding a thread to a specific core. This way, each thread “stays home,” retains its cache, and the processor spends time on real computations rather than unnecessary transfers.

How Does This Impact Inference?

In inference systems, especially those serving many requests simultaneously, proper core management can:

• Significantly reduce latency.
• Increase throughput (number of predictions per second).
• Maintain stability - ensuring each request gets consistent processing time without sudden spikes or delays.

Bottom Line

A core isn’t just “more computing power” - it’s a workspace with its own memory. If we maintain continuity between a thread and its core, we gain speed, efficiency, and stability. Proper core management isn’t a marginal optimization - it’s the difference between a system that struggles and one that performs inference in near-zero time.