What Is NUMA (Non-uniform Memory Access)?

April 19, 2024

NUMA (Non-uniform Memory Access) is a computer memory design optimized for multi-processor systems. It enhances both performance and scalability by efficiently managing how memory is accessed by various processors.

What Is NUMA (Non-uniform Memory Access)?

NUMA stands for Non-uniform Memory Access, a system architecture designed to optimize the efficiency of multi-processor computer systems. In contrast to single-processor or uniform memory access (UMA) systems, where every processor shares equal access to a single memory pool, NUMA configures a computer system with multiple memory nodes connected to one or more processors.

This configuration allows for decreased latency in memory access by ensuring that processors primarily access memory located physically closer to them, thus avoiding the bottleneck experienced in UMA systems as they scale up.

NUMA Architecture

NUMA’s architecture adds more processors or memory to a system, enhancing how these resources are organized and managed to tackle advanced computational tasks more effectively. The configuration of nodes, the strategic allocation of memory, the robust inter-node communication systems, and sophisticated memory controllers all converge to form an architecture tailored to the demands of modern high-performance computing.

Here is a detailed breakdown of NUMA Architecture:

Node Configuration

In a NUMA system, the entire architecture is divided into multiple nodes. Each node comprises one or more CPUs (or cores) and their directly associated memory. This arrangement allows each node to function almost as an autonomous unit, yet it remains an integral part of the larger system framework.

This division enhances performance by localizing data access and simplifies scaling, as each node can be added or configured independently based on system requirements. The nodes act as the fundamental units of computation and memory within the NUMA architecture, each serving a specific subset of processing and memory tasks, reducing latency, and improving data handling efficiency.

Memory Allocation

In NUMA systems, memory is not a shared, uniform resource but is segmented and allocated to specific nodes. Each node’s memory serves as the primary storage for the processors within that node. By localizing memory resources, the system reduces the operational overhead associated with a single, extensive, monolithic memory system.

This setup minimizes the contention typically seen in non-NUMA architectures where multiple processors vie for the same memory resources. The localized memory model significantly enhances the speed and efficiency with which processors access and manipulate data, leading directly to performance improvements in multithreading and parallel processing tasks.

Inter-node Communication

NUMA systems employ high-speed interconnects to facilitate communication between nodes. Depending on the system’s size, scale, and performance requirements, these interconnects can be realized through various means, such as dedicated bus systems, advanced crossbar switches, or scalable mesh networks.

The choice of interconnect affects the system’s ability to handle data traffic between nodes and thus directly impacts overall system performance. Efficient inter-node communication is crucial for maintaining high performance across the system, especially under load conditions where multiple nodes must interact frequently. These interconnects minimize the delay even when data must be accessed from remote memory and data throughput is maximized.

Memory Controllers

Each node within a NUMA architecture is equipped with its memory controller, which manages all read and write operations to the node’s memory. These controllers are critical for maintaining fast access times to local memory and efficiently managing the more complex task of accessing memory across nodes.

The memory controllers also handle the necessary routing and data coherence protocols, which ensure that data remains consistent and intact across the system. This process involves complex logic to track where different data segments are stored and how they are accessed, ensuring that processors always have access to the most current and coherent data. This aspect of NUMA design is vital for applications that require high levels of data integrity and consistency, such as database systems and large-scale scientific simulations.

How Does NUMA Work?

A NUMA system optimizes memory access patterns across different nodes and processors. Here is a detailed explanation of how this works:

Optimized Memory Access

In NUMA systems, the approach to memory access is hierarchical and tiered, designed to first utilize the nearest and, thus, fastest accessible memory. Processors attempt to access local memory within the same node before reaching out to remote memory located in other nodes. This prioritization significantly reduces the latency that can accumulate in large-scale computing systems where distances between processing and memory units can be considerable.

The benefit of such a structure is particularly evident in data-intensive applications where frequent memory access is necessary. By minimizing the distance data must travel, the overall speed and responsiveness of the system are enhanced. Furthermore, this design helps prevent the processor from stalling while waiting for data, thereby improving the efficiency of computational operations across the system.

Data Traffic Management

NUMA architectures incorporate sophisticated inter-node communication protocols to effectively manage data flow between nodes. These protocols are designed to prioritize local memory access and use remote memory access sparingly. The rationale behind this approach is to prevent the saturation of interconnects—the pathways that link different nodes within the system. Saturation can lead to bottlenecks, significantly degrading system performance.

To counteract this, advanced NUMA systems are equipped with dynamic routing algorithms capable of adjusting data paths in real time. These algorithms assess current traffic conditions and node availability, rerouting data to balance loads and optimize traffic across the network. This dynamic adjustment helps maintain high levels of system performance even under varying load conditions, ensuring that data is always routed through the most efficient path available.

Operating System Support

The underlying operating system must be designed or adapted to be NUMA-aware for a NUMA system to achieve optimal performance. This awareness means the operating system understands and considers the physical layout of processors and memory nodes when allocating processes and managing memory. Such awareness is crucial because it allows the operating system to place processes on nodes in a way that maximizes the use of local memory and minimizes the frequency and volume of remote memory accesses.

NUMA-aware operating systems employ sophisticated scheduling algorithms that distribute processes intelligently across the available nodes, considering current workload distributions and memory utilization patterns. Additionally, memory management strategies in these operating systems are tailored to exploit the locality of reference provided by NUMA architectures, thereby enhancing the performance and efficiency of the system.

What Is NUMA Affinity?

NUMA affinity is a crucial system optimization technique in NUMA architectures that strategically ties specific processes or threads to designated NUMA nodes. This technique maximizes the efficiency of memory usage and processor allocation.

Here is a detailed explanation of how NUMA affinity works:

Affinity Setting 

System administrators implement affinity policies through system configuration tools or directly within the application code. These policies bind processes or threads to specific nodes within the NUMA architecture. By setting these affinity policies, administrators ensure that each process runs on a designated processor and accesses the memory local to that processor as much as possible. This localization reduces the need for processes to access remote memory, which is a slower operation due to the additional distance the data must travel and potential congestion on the data paths.

Effectively managing these settings helps in achieving a significant reduction in operational delays caused by long memory access times. Additionally, affinity settings can be dynamically adjusted to respond to changing workload requirements, allowing for flexible and efficient resource utilization across the system.

Performance Optimization

NUMA affinity's primary goal is to optimize a system's performance by managing two critical aspects: where processes are executed and how memory is allocated.

  • High cache hit rates. By ensuring that a process runs close to its memory data, NUMA affinity increases the likelihood that data needed by a process is already in the cache associated with the processor. This high cache hit rate minimizes the need to fetch data from main memory, significantly speeding up process execution.
  • Lower memory access latencies. NUMA affinity reduces memory access latencies by minimizing the distance data travels between memory and processors. When processes access local memory, the travel time for data is considerably shorter than if the same processes were to access remote memory across nodes.
  • Resource utilization. Optimally utilizing computational and memory resources within a NUMA environment means that the system can handle more tasks simultaneously and maintain stability under high load.
  • Scalability. With effective NUMA affinity policies, systems can achieve better scalability. As more nodes and processors are added, maintaining proper affinity ensures that the added resources contribute positively to system performance rather than exacerbating bottlenecks due to inefficient resource use.


The distinction between NUMA and UMA architectures highlights significant design philosophy and capability shifts. Here is an overview of the main differences:

  • Memory access paradigm. UMA architectures allow all processors to share equal access to memory, which simplifies management. However, this can lead to bottlenecks as the number of processors increases, due to all processors contending for the same memory bandwidth. Conversely, NUMA architectures assign memory locally to processors within discrete nodes, thereby reducing latency and contention by leveraging memory locality. This setup enhances performance as the system size grows, especially beneficial in large-scale operations.
  • Scalability and efficiency. UMA architectures struggle to scale efficiently with an increasing number of processors, becoming inefficient under heavy loads or in large systems. On the other hand, NUMA architectures scale more effectively. As additional processors and memory are integrated, they are configured into nodes that maintain local memory access. This structure minimizes delays in memory access and improves overall throughput.
  • Complexity and cost. UMA architectures are simpler and less expensive to design and maintain, making them suitable for smaller systems where the overhead of managing a sophisticated memory system is not justified. NUMA systems, however, are more complex and costly due to the need for sophisticated management software and hardware to handle the node-based distribution of memory and processors. Despite the higher initial costs, this complexity is often justified in environments requiring high performance and scalability, such as data centers and scientific computing settings.

Nikola is a seasoned writer with a passion for all things high-tech. After earning a degree in journalism and political science, he worked in the telecommunication and online banking industries. Currently writing for phoenixNAP, he specializes in breaking down complex issues about the digital economy, E-commerce, and information technology.