What Is Non-Uniform Memory Access (NUMA)?
Non-uniform memory access (NUMA) is a computer memory design used in multiprocessor systems, where memory access time varies depending on the memory location relative to the processor. In a NUMA architecture, each processor has its own local memory. While it can also access memory attached to other processors, that access is slower due to the need to traverse interconnects and potentially other processors, which introduces additional latency and bandwidth contention.
NUMA was developed to address the scalability limitations of traditional symmetric multiprocessing (SMP) systems, where all processors share a single memory pool. By grouping memory closer to each processor, NUMA improves memory access efficiency and overall performance, especially in systems with a large number of central processing units (CPUs) or cores.
Modern operating systems and applications optimized for NUMA can allocate memory in a way that keeps data local to the processor performing the computation, minimizing latency and increasing throughput. NUMA is commonly used in servers, high-performance computing (HPC) systems, and enterprise workloads that require extensive memory and compute resources.
How Does NUMA Work?
In a NUMA system, the architecture is divided into multiple nodes. Each node contains one or more processors (or CPU sockets) along with its own dedicated memory. These nodes are interconnected, allowing processors to access both local and remote memory.
Access to local memory (memory that’s physically attached to the same node) is faster and has lower latency. When a processor accesses remote memory (memory that’s located on a different node) it incurs additional latency due to the interconnect path between nodes. This latency difference is what defines the “non-uniform” aspect of NUMA.
Modern NUMA systems rely on the system firmware, operating system, and applications to be NUMA-aware. NUMA-aware software attempts to allocate memory and schedule processes in a way that keeps tasks and their memory close together on the same node. This reduces cross-node memory traffic and optimizes performance.
NUMA is typically implemented in systems with two or more CPU sockets, such as servers and high-performance computing platforms. Each socket has its own memory controller and local dynamic random-access memory (DRAM), with the system interconnect, such as Intel® Ultra Path Interconnect (UPI) or AMD Infinity Fabric, linking the sockets and enabling access to remote memory when needed.
NUMA and System Architecture Design
As multi-socket systems become more common in modernized data centers as well as enterprise infrastructure, understanding NUMA's influence on system architecture is critical. Hardware vendors design server motherboards and CPU interconnects with NUMA topologies in mind, ensuring optimal data paths between processors and memory banks. This has a direct impact on application performance, especially for workloads that scale horizontally across multiple cores and sockets.
Design considerations also include memory population rules, BIOS-level NUMA configuration settings, and the physical layout of components. System architects must account for how applications interact with memory and compute resources to ensure that hardware capabilities are fully utilized. Poorly optimized hardware configurations can lead to suboptimal NUMA behavior, including excessive remote memory access and increased latency.
Software Optimization for NUMA Systems
For NUMA to deliver its full performance potential, software must be designed, or at least configured, to be aware of the underlying memory topology. Operating systems such as Linux and Windows Server include kernel-level support for NUMA, enabling features such as processor affinity, memory binding, and NUMA-aware scheduling. These mechanisms help ensure that threads are scheduled on CPUs with fast access to the memory regions they use most frequently.
Application developers and system administrators can further improve performance by leveraging NUMA-specific APIs and tuning practices. Parallel programming frameworks, such as OpenMP and MPI, provide options to control thread placement and memory locality. For enterprise and HPC workloads, this level of optimization can significantly reduce memory latency and increase throughput, particularly when running on multi-socket systems with large memory footprints.
NUMA Use Cases and Benefits
NUMA provides performance advantages for workloads that demand high parallelism and large memory footprints. By reducing cross-node memory access and making better use of available bandwidth, NUMA enhances scalability and helps eliminate bottlenecks in multi-socket architectures.
High-Performance Computing (HPC)
In High Performance Computing, scientific simulations, modeling, and large-scale data analysis workloads benefit from NUMA by reducing memory access latency across multiple CPU sockets.
Virtualization Platforms
Hypervisors and virtual machines can be optimized to use NUMA-aware scheduling, ensuring that virtual CPUs and their memory remain on the same physical node.
In-Memory Databases
NUMA helps maintain low-latency memory access in database systems that store large volumes of data directly in RAM.
Large-Scale Enterprise Applications
NUMA improves responsiveness and throughput for enterprise software, such as enterprise resource planning (ERP) systems, that require access to large memory spaces across multiple cores.
FAQs
- What’s NUMA clustering?
NUMA clustering refers to the organization of processors and memory into nodes or clusters within a NUMA-enabled system. Each cluster has its own local memory, and processors within a cluster can access that memory with lower latency. This arrangement improves performance and scalability by reducing contention for shared memory resources. - What is NUMA topology?
NUMA topology describes how CPUs, memory, and interconnects are physically and logically arranged in a system. It defines which processors are connected to which memory regions, and how access occurs between nodes. Understanding the system's topology helps optimize memory allocation and application performance. - Is NUMA important for virtualization?
Yes, NUMA plays a critical role in virtualization by helping ensure optimal memory and CPU placement for virtual machines. When a hypervisor is NUMA-aware, it can keep virtual CPUs and their associated memory within the same physical node, reducing latency and avoiding performance penalties. - How do you enable NUMA on a system?
NUMA support is usually enabled by default on modern server platforms. In some cases, it may need to be activated in the system BIOS or UEFI settings. Once enabled, the system exposes the NUMA topology to the operating system for optimized scheduling and memory allocation. - What happens if software is not NUMA-aware?
If software is not NUMA-aware, it may access memory inefficiently across nodes, resulting in higher latency and lower performance. This can become a bottleneck in multi-socket systems with large memory pools, especially under high workloads.