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Go Runtime Stack Optimization

2 posts · Apr 2026 – May 2026

What this trail covers

Uber has optimized the performance of its Go services by fine-tuning the Go runtime's stack allocation mechanisms. This effort focuses on reducing CPU overhead by disabling adaptive stack expansion and shrinking, and instead pre-allocating stack sizes. A profile-guided approach automates the tuning process by analyzing CPU profiles to determine optimal static stack sizes for different services, leading to significant CPU savings.

All Uber trails
  1. Zero-Growth Stack, Real Gains: How Stack Allocation Can Save 10% CPU in GoThis post details the implementation of a profile-guided approach to optimize Go stack allocation. It describes disabling adaptive stack expansion and shrinking, and instead pre-allocating stack sizes based on CPU profile analysis. The contribution includes a workflow for analyzing pprof profiles, extracting stack usage from binaries, and automating the tuning process to achieve significant CPU savings.
  2. Hybrid Core Allocation: From Overallocation to Reliable SharingThis post introduces a significant evolution in Uber's CPU resource allocation strategy within its Odin container orchestration system. It details the transition from dedicated CPU allocation to a hybrid model that incorporates shared cores alongside dedicated ones. Key contributions include the introduction of shared core pooling, over-allocation control, fair contention handling via `cpu.shares`, and an upgraded vertical scaler. The post also discusses NUMA considerations for optimal CPU and memory affinity, the implementation of in-place vertical scaling for stateful workloads, and how Odin bridges Kubernetes gaps for hybrid core allocation. The core innovation is enabling more intelligent handling of bursty CPU usage patterns, leading to improved performance, reliability, and cost-effectiveness for colocated workloads.