Understanding memory page sizes in Arm64 and why they matter
In Arm64 architectures, memory pages can come in different sizes, and choosing the right size directly impacts the performance of databases, AI workloads, and intensive I/O operations. Large pages reduce TLB overhead and improve performance in sequential accesses and applications that handle large data sets, while small pages offer better granularity and less memory waste in workloads with many small allocations.
When to use 64K pages in Arm64
64K pages are usually beneficial for high-performance databases, in-memory analytics engines, artificial intelligence models with large embeddings, and throughput-intensive I/O workloads. They also help in cloud environments where virtual machines or containers move large memory regions, reducing TLB misses and improving latency in repeated accesses.
Considerations and trade-offs
Using large pages can increase memory waste due to internal fragmentation, penalize workloads that use many small allocations, and complicate debugging. Additionally, some libraries and drivers expect the usual page size and may require adjustment. It is important to measure with real profiles before deploying changes in production.
How to configure Linux to take advantage of large pages in Arm64
Some practical recommendations for Linux Arm64 environments
1 Compile the kernel with support for large page sizes if the platform requires it, for example by enabling the CONFIG_ARM64_64K_PAGES option when the SoC supports it
2 Configure HugeTLB and hugetlbfs for explicit large page memory reservations when necessary
3 Use transparent hugepages with caution and measure behavior, or apply madvise for critical processes that can benefit from huge pages
4 Adjust kernel and bootloader parameters and review the device tree to ensure the system recognizes the desired page size
5 For containers, ensure that the host and runtime allow the use of hugetlb and that memory limits do not prevent allocation
Measurement and tools
Perf, pagemap, /sys/kernel/mm/hugepages, and specific profiling tools help check TLB misses, memory usage, and fragmentation. Before changing global sizes, validate with real database workloads, AI tests, and I/O workloads.
How Q2BSTUDIO can help
At Q2BSTUDIO, we are a custom software and application development company specialized in optimizing platforms on Arm64 and in the cloud. We offer custom software services, artificial intelligence integration, and cybersecurity consulting to ensure that changes in memory management do not affect security or stability. We also provide AWS and Azure cloud services to design infrastructures that take advantage of appropriate page sizes according to the workload.
Our services include performance analysis for databases and AI systems, implementation of AI agents to automate tasks, business intelligence services solutions, and dashboards with Power BI to visualize impact and key metrics. If your company is looking for AI for businesses or AI agents to optimize processes, Q2BSTUDIO develops and integrates these solutions as custom software and custom applications.
Summary and recommendations
Choosing the page size in Arm64 is a technical decision that depends on the workload profile. Databases, large artificial intelligence models, and intensive I/O workloads can benefit from 64K pages. However, it is essential to measure, test, and adjust kernel and cloud environment parameters. Q2BSTUDIO offers complete support from performance testing to deployment on AWS and Azure cloud services, with a focus on cybersecurity, artificial intelligence, and business intelligence services to ensure robust and scalable solutions.
Contact Q2BSTUDIO to design a personalized strategy that combines custom software, custom applications, artificial intelligence, cybersecurity, and cloud optimization to get the maximum performance out of your Arm64 infrastructure.
