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As a Linux Kernel Developer at Ampere Computing, I focus on optimizing the scheduler for high-density ARM64 systems. My work culminates in a patch merged for the Linux 7.0 release that refines avg_idle tracking a critical metric the scheduler uses to decide how long to search for an idle CPU before giving up. In my session "The Hidden Cost of Sleep", I will break down the try_to_wake_up() path to show how even minor inaccuracies in idle-time accounting lead to poor CPU selection and increased cache misses. I’ll explain how my Linux 7.0 optimizations [commit 36ae1c45b2cede] specifically reduce the 'search cost' during wakeups, directly improving the responsiveness of AI inference workloads. By sharing raw performance data and trace analysis, I’ll demonstrate why getting the wakeup path right is the only way to achieve the deterministic performance needed for autonomous AI agents and scalable trust infrastructure.
