openstack-manuals/doc/admin-guide/source/compute-numa-cpu-pinning.rst

Enabling advanced CPU topologies in guests

The NUMA topology and CPU pinning features in OpenStack provide high level control over how instances run on host CPUs, and the topology of CPUs inside the instance. These features can be used to minimize latency and maximize per-instance performance.

SMP, NUMA, and SMT overviews

Symmetric multiprocessing (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 IO ports.

Non-uniform memory access (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 this resource can incur performance penalties.

Simultaneous Multi-Threading (SMT), known as as Hyper-Threading on Intel platforms, is a design that is 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.

Customizing instance NUMA topologies

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 the said devices.

By default, an instance will float across all NUMA nodes on a host. NUMA awareness can be enabled implicitly, through the use of hugepages 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 which 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 six 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 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: configuring competing policies will result in 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 bug for more information.

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