CPU topologies

CPU topologies

The NUMA topology and CPU pinning features in OpenStack provide high-level control over how instances run on hypervisor CPUs and the topology of virtual CPUs available to instances. These features help minimize latency and maximize performance.

SMP, NUMA, and SMT

Symmetric multiprocessing (SMP)
SMP is a design found in many modern multi-core systems. In an SMP system, there are two or more CPUs and these CPUs are connected by some interconnect. This provides CPUs with equal access to system resources like memory and input/output ports.
Non-uniform memory access (NUMA)
NUMA is a derivative of the SMP design that is found in many multi-socket systems. In a NUMA system, system memory is divided into cells or nodes that are associated with particular CPUs. Requests for memory on other nodes are possible through an interconnect bus. However, bandwidth across this shared bus is limited. As a result, competition for this resource can incur performance penalties.
Simultaneous Multi-Threading (SMT)
SMT is a design complementary to SMP. Whereas CPUs in SMP systems share a bus and some memory, CPUs in SMT systems share many more components. CPUs that share components are known as thread siblings. All CPUs appear as usable CPUs on the system and can execute workloads in parallel. However, as with NUMA, threads compete for shared resources.

In OpenStack, SMP CPUs are known as cores, NUMA cells or nodes are known as sockets, and SMT CPUs are known as threads. For example, a quad-socket, eight core system with Hyper-Threading would have four sockets, eight cores per socket and two threads per core, for a total of 64 CPUs.

Customizing instance NUMA placement policies

Important

The functionality described below is currently only supported by the libvirt/KVM driver.

When running workloads on NUMA hosts, it is important that the vCPUs executing processes are on the same NUMA node as the memory used by these processes. This ensures all memory accesses are local to the node and thus do not consume the limited cross-node memory bandwidth, adding latency to memory accesses. Similarly, large pages are assigned from memory and benefit from the same performance improvements as memory allocated using standard pages. Thus, they also should be local. Finally, PCI devices are directly associated with specific NUMA nodes for the purposes of DMA. Instances that use PCI or SR-IOV devices should be placed on the NUMA node associated with these devices.

By default, an instance floats across all NUMA nodes on a host. NUMA awareness can be enabled implicitly through the use of huge pages or pinned CPUs or explicitly through the use of flavor extra specs or image metadata. In all cases, the NUMATopologyFilter filter must be enabled. Details on this filter are provided in Scheduling configuration guide.

Caution

The NUMA node(s) used are normally chosen at random. However, if a PCI passthrough or SR-IOV device is attached to the instance, then the NUMA node that the device is associated with will be used. This can provide important performance improvements. However, booting a large number of similar instances can result in unbalanced NUMA node usage. Care should be taken to mitigate this issue. See this discussion for more details.

Caution

Inadequate per-node resources will result in scheduling failures. Resources that are specific to a node include not only CPUs and memory, but also PCI and SR-IOV resources. It is not possible to use multiple resources from different nodes without requesting a multi-node layout. As such, it may be necessary to ensure PCI or SR-IOV resources are associated with the same NUMA node or force a multi-node layout.

When used, NUMA awareness allows the operating system of the instance to intelligently schedule the workloads that it runs and minimize cross-node memory bandwidth. To restrict an instance’s vCPUs to a single host NUMA node, run:

$ openstack flavor set m1.large --property hw:numa_nodes=1

Some workloads have very demanding requirements for memory access latency or bandwidth that exceed the memory bandwidth available from a single NUMA node. For such workloads, it is beneficial to spread the instance across multiple host NUMA nodes, even if the instance’s RAM/vCPUs could theoretically fit on a single NUMA node. To force an instance’s vCPUs to spread across two host NUMA nodes, run:

$ openstack flavor set m1.large --property hw:numa_nodes=2

The allocation of instances vCPUs and memory from different host NUMA nodes can be configured. This allows for asymmetric allocation of vCPUs and memory, which can be important for some workloads. To spread the 6 vCPUs and 6 GB of memory of an instance across two NUMA nodes and create an asymmetric 1:2 vCPU and memory mapping between the two nodes, run:

$ openstack flavor set m1.large --property hw:numa_nodes=2
$ openstack flavor set m1.large \  # configure guest node 0
  --property hw:numa_cpus.0=0,1 \
  --property hw:numa_mem.0=2048
$ openstack flavor set m1.large \  # configure guest node 1
  --property hw:numa_cpus.1=2,3,4,5 \
  --property hw:numa_mem.1=4096

For more information about the syntax for hw:numa_nodes, hw:numa_cpus.N and hw:num_mem.N, refer to the Flavors guide.

Customizing instance CPU pinning policies

Important

The functionality described below is currently only supported by the libvirt/KVM driver.

By default, instance vCPU processes are not assigned to any particular host CPU, instead, they float across host CPUs like any other process. This allows for features like overcommitting of CPUs. In heavily contended systems, this provides optimal system performance at the expense of performance and latency for individual instances.

Some workloads require real-time or near real-time behavior, which is not possible with the latency introduced by the default CPU policy. For such workloads, it is beneficial to control which host CPUs are bound to an instance’s vCPUs. This process is known as pinning. No instance with pinned CPUs can use the CPUs of another pinned instance, thus preventing resource contention between instances. To configure a flavor to use pinned vCPUs, a use a dedicated CPU policy. To force this, run:

$ openstack flavor set m1.large --property hw:cpu_policy=dedicated

Caution

Host aggregates should be used to separate pinned instances from unpinned instances as the latter will not respect the resourcing requirements of the former.

When running workloads on SMT hosts, it is important to be aware of the impact that thread siblings can have. Thread siblings share a number of components and contention on these components can impact performance. To configure how to use threads, a CPU thread policy should be specified. For workloads where sharing benefits performance, use thread siblings. To force this, run:

$ openstack flavor set m1.large \
  --property hw:cpu_policy=dedicated \
  --property hw:cpu_thread_policy=require

For other workloads where performance is impacted by contention for resources, use non-thread siblings or non-SMT hosts. To force this, run:

$ openstack flavor set m1.large \
  --property hw:cpu_policy=dedicated \
  --property hw:cpu_thread_policy=isolate

Finally, for workloads where performance is minimally impacted, use thread siblings if available. This is the default, but it can be set explicitly:

$ openstack flavor set m1.large \
  --property hw:cpu_policy=dedicated \
  --property hw:cpu_thread_policy=prefer

For more information about the syntax for hw:cpu_policy and hw:cpu_thread_policy, refer to the Flavors guide.

Applications are frequently packaged as images. For applications that require real-time or near real-time behavior, configure image metadata to ensure created instances are always pinned regardless of flavor. To configure an image to use pinned vCPUs and avoid thread siblings, run:

$ openstack image set [IMAGE_ID] \
  --property hw_cpu_policy=dedicated \
  --property hw_cpu_thread_policy=isolate

Image metadata takes precedence over flavor extra specs. Thus, configuring competing policies causes an exception. By setting a shared policy through image metadata, administrators can prevent users configuring CPU policies in flavors and impacting resource utilization. To configure this policy, run:

$ openstack image set [IMAGE_ID] --property hw_cpu_policy=shared

Note

There is no correlation required between the NUMA topology exposed in the instance and how the instance is actually pinned on the host. This is by design. See this invalid bug for more information.

For more information about image metadata, refer to the Image metadata guide.

Customizing instance CPU topologies

Important

The functionality described below is currently only supported by the libvirt/KVM driver.

In addition to configuring how an instance is scheduled on host CPUs, it is possible to configure how CPUs are represented in the instance itself. By default, when instance NUMA placement is not specified, a topology of N sockets, each with one core and one thread, is used for an instance, where N corresponds to the number of instance vCPUs requested. When instance NUMA placement is specified, the number of sockets is fixed to the number of host NUMA nodes to use and the total number of instance CPUs is split over these sockets.

Some workloads benefit from a custom topology. For example, in some operating systems, a different license may be needed depending on the number of CPU sockets. To configure a flavor to use a maximum of two sockets, run:

$ openstack flavor set m1.large --property hw:cpu_sockets=2

Similarly, to configure a flavor to use one core and one thread, run:

$ openstack flavor set m1.large \
  --property hw:cpu_cores=1 \
  --property hw:cpu_threads=1

Caution

If specifying all values, the product of sockets multiplied by cores multiplied by threads must equal the number of instance vCPUs. If specifying any one of these values or the multiple of two values, the values must be a factor of the number of instance vCPUs to prevent an exception. For example, specifying hw:cpu_sockets=2 on a host with an odd number of cores fails. Similarly, specifying hw:cpu_cores=2 and hw:cpu_threads=4 on a host with ten cores fails.

For more information about the syntax for hw:cpu_sockets, hw:cpu_cores and hw:cpu_threads, refer to the Flavors guide.

It is also possible to set upper limits on the number of sockets, cores, and threads used. Unlike the hard values above, it is not necessary for this exact number to used because it only provides a limit. This can be used to provide some flexibility in scheduling, while ensuring certains limits are not exceeded. For example, to ensure no more than two sockets are defined in the instance topology, run:

$ openstack flavor set m1.large --property=hw:cpu_max_sockets=2

For more information about the syntax for hw:cpu_max_sockets, hw:cpu_max_cores, and hw:cpu_max_threads, refer to the Flavors guide.

Applications are frequently packaged as images. For applications that prefer certain CPU topologies, configure image metadata to hint that created instances should have a given topology regardless of flavor. To configure an image to request a two-socket, four-core per socket topology, run:

$ openstack image set [IMAGE_ID] \
  --property hw_cpu_sockets=2 \
  --property hw_cpu_cores=4

To constrain instances to a given limit of sockets, cores or threads, use the max_ variants. To configure an image to have a maximum of two sockets and a maximum of one thread, run:

$ openstack image set [IMAGE_ID] \
  --property hw_cpu_max_sockets=2 \
  --property hw_cpu_max_threads=1

Image metadata takes precedence over flavor extra specs. Configuring competing constraints causes an exception. By setting a max value for sockets, cores, or threads, administrators can prevent users configuring topologies that might, for example, incur an additional licensing fees.

For more information about image metadata, refer to the Image metadata guide.

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