Introduction

Non-Uniform Memory Access (NUMA) is a memory architecture used in modern multi-socket and multi-die server platforms, where memory access latency and bandwidth depend on the location of the memory relative to the executing core. On Intel® Xeon® platforms, aligning workloads with the underlying NUMA topology can deliver significant performance gains—often without code changes, hardware upgrades, or additional investment.

This directory collects best practices, case studies, and performance opportunities for running workloads in NUMA environments. The goal is to help you understand why hardware topology matters, identify configuration mismatches that leave performance on the table, and apply proven, hardware-aware patterns to your deployments.

Running in Kubernetes? The Topology-aware and Balloons policies apply NUMA-aligned placement to every pod automatically. See NRI Resource-Policy Plugins.

Actual improvements will vary depending on a given workload’s characteristics. It is recommended to use a profiling solution such as VTune Profiler to more accurately gauge where an application’s hotpaths are and which optimizations would have the greatest effect.

What’s Inside

• Best Practices: General, workload-agnostic guidance for configuring and tuning applications to align with NUMA topology.
• Performance Opportunities: Common NUMA-related bottlenecks and the optimization patterns that address them.
• Case Studies: Real-world, data-driven examples demonstrating the performance impact of NUMA-aware configuration.