Why Scaling to 64 Cores Makes Your Code Cry (NUMA Explained)

You upgraded to a multi-core CPU server, but your program didn’t get faster?
Welcome to the world of NUMA – Non-Uniform Memory Access, where not all memory is equal, and core-to-RAM distance matters more than you think.

In this episode of "What Every Programmer Should Know About Memory", we’ll uncover how modern CPUs and motherboards manage memory access across cores — and why NUMA-awareness can make or break your performance in high-load or parallel applications.

🔍 What You’ll Learn:
✅ What is NUMA and why it exists in modern systems
✅ How NUMA differs from traditional memory architectures
✅ The performance impact of remote memory access
✅ What causes NUMA-related latency spikes
✅ How to write NUMA-aware code
✅ How operating systems schedule threads in NUMA environments

💡 If you work with multithreading, game engines, databases, machine learning, or high-performance computing — you can’t afford to ignore NUMA.

🔔 Subscribe so you don’t miss future deep dives into memory and performance!

🔧 Resources & Tools:
📊 Tool: lscpu, numactl, and numastat
📚 Best Books for Low-Level Programming Optimization → [https://people.freebsd.org/~lstewart/...]

💬 Discussion Time!
Have you ever run into unexpected performance issues on a server or workstation with many cores?
💬 Let me know in the comments — I’d love to hear your stories or questions!

📌 Like 👍 | Comment 💬 | Subscribe 🔔 to join the performance-focused programming community.

🧠 Hashtags for SEO:
#NUMA #MulticorePerformance #MemoryManagement #ParallelProgramming #HPC #LowLevelProgramming #CPUTopology #SoftwareOptimization #WhatEveryProgrammerShouldKnow