OpenStack and HPC Network Fabrics¶

HPC and cloud infrastructure are not built to the same requirements. As much as anything else, networking exemplifies the divergent criteria between HPC applications and the typical workloads served by cloud infrastructure.

With sweeping generalisations, one typically assumes an HPC parallel workload is tightly-coupled, and a cloud-native workload is loosely-coupled. A typical HPC parallel workload might be computational fluid dynamics using a partitioned geometric grid. The application code is likely to be structured in a bulk synchronous parallel model, comprising phases of compute and data exchange between neighbouring workers. Progress is made in lock-step, and is blocked until all workers complete each phase.

Compare this with typical cloud-native application, which might be a microservice architecture consisting of a number of communicating sequential processes. The overall application structure is completely different, and workers do not have the same degree of dependency upon other workers in order to make progress.

The different requirements of HPC and cloud-native applications have led to different architectural choices being made at every level in order to deliver optimal and cost-effective solutions for each target application.

A cloud environment experiences workload diversity to a far greater extent than seen in HPC. This has become the quintessential driving force of software-defined infrastructure. Cloud environments are designed to be flexible and adaptable. As a result, cloud infrastructure has the flexibility to accommodate HPC requirements.

The flexibility of cloud infrastructure is delivered through layers of abstraction. OpenStack’s focus is on defining the intent of the multi-tenant cloud infrastructure. Dedicated network management applications decide on the implementation. As an orchestrator, OpenStack delegates knowledge of physical network connectivity to the network management platforms to which it connects.

OpenStack’s surging momentum has ensured that support already exists for all but the most exotic of HPC network architectures. This article will describe several solutions for delivering HPC networking in an OpenStack cloud.

Using SR-IOV for Virtualised HPC Networking¶

SR-IOV is a technology that demonstrates how software-defined infrastructure can introduce new flexibility in the management of HPC resources, whilst retaining the high-performance benefits. With current generation devices, there is a slight increase in I/O latency when using SR-IOV virtual functions. However, this overhead is negligible for all but the most latency-sensitive of applications.

The Process Flow for Using SR-IOV¶

The Nova compute hypervisor is configured at boot time with kernel flags to support extensions for SR-IOV hardware management.

The network kernel device driver is configured to create virtual functions. These are present alongside the physical function. When they are not assigned to a guest workload instance, the virtual functions are visible in the device tree of the hypervisor.

The OpenStack services are configured with identifiers or addresses of devices configured to support SR-IOV. This is most easily done by identifying the physical function (for example using its network device name, PCI bus address, or PCI vendor/device IDs). All virtual functions associated with this device will be made available for virtualised compute instances. The configuration that identifies SR-IOV devices is known as the whitelist.

To use an SR-IOV virtual function for networking in an instance, a special direct-bound network port is created and connected with the VM. This causes one of the virtual functions to be configured and passed-through from the hypervisor into the VM.

Support for launching an instance using SR-IOV network interfaces from OpenStack’s Horizon web interface was introduced in the OpenStack Mitaka release (April 2016). Prior to this, it was only possible to launch instances using SR-IOV ports through a sequence of command-line invocations (or through direct interaction with the OpenStack APIs).

The Limitations of Using SR-IOV in Cloud Infrastructure¶

SR-IOV places some limitations on the cloud computing model that can be detrimental to the overall flexibility of the infrastructure:

Current SR-IOV hardware implementations support flat (unsegregated) and VLAN network separation but not VXLAN for tenant networks. This limitation can constrain the configuration options for the network fabric. Layer-3 IP-based fabrics using technologies such as ECMP are unlikely to interoperate with VLAN-based network separation.
Live migration of VMs connected using SR-IOV is not possible with current hardware and software. The capability is being actively developed for Mellanox SR-IOV NICs. It is not confirmed whether live migration of RDMA applications will be possible.
SR-IOV devices bypass OpenStack’s security groups, and consequently should only be used for networks that are not externally connected.

Virtualisation-aware MPI for Tightly-Coupled Cloud Workloads¶

The MVAPICH2 library implements MPI-3 (based on MPI 3.1 standard) using the IB verbs low-level message passing primitives. MVAPICH2 was created and developed by the Network-Based Computing Laboratory (NOWLAB) at The Ohio State University, and has been freely available for download for 15 years. Over that time MVAPICH2 has been continuously developed and now runs on systems as big as 500,000 cores.

An Infiniband NIC with SR-IOV capability was first developed by Mellanox in the ConnectX-3 generation of its product, unlocking the possibility of achieving near-native Infiniband performance in a virtualised environment. MVAPICH2-Virt was introduced in 2015 to develop HPC levels of performance for cloud infrastructure. The techniques adopted by MVAPICH2-Virt currently support KVM and Docker based cloud environments. MVAPICH2-Virt introduces Inter-VM Shared Memory (IVSHMEM) support to KVM hypervisors, increasing performance between co-resident VMs. In order to run MVAPICH2-Virt based applications on top of OpenStack-based cloud environments easily, several extensions to set up SR-IOV and IVSHMEM devices in VMs have been developed for OpenStack’s Nova compute manager.

MVAPICH2-Virt has two principal optimisation strategies for KVM-based cloud environments:

Dynamic locality awareness for MPI communication among co-resident VMs. A new communication channel, IVSHMEM, introduces a memory-space communication mechanism between different VMs co-resident on the same hypervisor. Inter-node communication continues to use the SR-IOV virtual function.
Tuning of MPI performance for both SR-IOV and IVSHMEM channels.

IV-SHMEM device for intra-hypervisor communication

Similarly, MVAPICH2-Virt has two principal optimisation strategies for Docker based cloud environments:

Dynamic locality awareness for MPI communication among co-resident containers. All Intra-Node MPI communication can go through either IPC-SHM enabled channel or CMA channel, no matter they are in the same container or different ones. Inter-Node-Inter-Container MPI communication will leverage the InfiniBand HCA channel.
Tuning of MPI performance for all different channels, including IPC-SHM, CMA, and InfiniBand HCA.

With these strategies in effect, the performance overhead of KVM and Docker based virtualisation on standard MPI benchmarks and applications are less than 10%.

“The novel designs introduced in MVAPICH2-Virt take advantage of the latest advances in virtualisation technologies and promise to design next-generation HPC cloud environments with good performance”, says Prof. DK Panda and Dr. Xiaoyi Lu of NOWLAB.

OpenStack as an environment for supporting MPI based HPC workloads has many benefits such as fast VM or container deployment for setting up MPI job execution environments, security, enabling resource sharing, providing privileged access in virtualized environments, supporting high-performance networking technologies (e.g. SR-IOV), etc

Currently, OpenStack still could not fully support or work seamlessly with technologies in HPC environments, such as IVSHMEM, SLURM, PBS, etc. But with several extensions proposed by NOWLAB researchers, running MPI based HPC workloads on top of OpenStack managed environments seems a promising approach for building efficient clouds.

The future direction of MVAPICH2-Virt includes

Further support different kinds of virtualised environments

Further improve MPI application performance on cloud environments through novel designs

Support live migration of MPI applications in SR-IOV and IVSHMEM enabled VMs

Infiniband and other Non-Ethernet Fabrics¶

Infiniband is the dominant fabric interconnect for HPC clusters. Of the TOP500 list published in June 2016, 41% of entries use Infiniband.

In part, OpenStack’s flexibility comes from avoiding many rigid assumptions in infrastructure management. However, OpenStack networking does have some expectations of an Ethernet and IP-centric network architecture, which can present challenges for the network architectures often used in HPC. The Neutron driver for Infiniband circumvents this assumption by applying Neutron’s layer-3 network configuration to an IP-over-IB interface, and mapping Neutron’s layer-2 network segmentation ID to Infiniband pkeys.

However, Neutron is limited to an allocation of 126 pkeys, which imposes a restrictive upper limit on the number of distinct tenant networks an OpenStack Infiniband cloud can support.

A technical lead with experience of using OpenStack on Infiniband reports mixed experiences from an evaluation performed in 2015. The overall result led him to conclude that HPC fabrics such as Infiniband are only worthwhile in an OpenStack environment if one is also using RDMA communication protocols in the client workload:

“MPI jobs were never a targeted application for our system. Rather, the goal for our OpenStack was to accommodate all the scientific audiences for whom big HPC clusters, and batch job schedulers, weren’t a best fit. So, no hard, fast requirement for a low-latency medium. What we realized was that it’s complex. It may be hard to keep it running in production. IPoIB on FDR, in unconnected mode, is slower than 10Gbps Ethernet, and if you’re not making use of RDMA, then you’re really just kind of hurting yourself. Getting data in and out is tricky. All the big data we have is on a physically separate IB fabric, and no one wanted to span those fabrics, and doing something involving IP routing would break down the usefulness of RDMA.”

IP-over-IB performance and scalability has improved substantially in subsequent hardware and software releases. A modern Infiniband host channel adaptor with a current driver stack operating in connected mode can sustain 35-40 Gbits/sec in a single TCP stream on FDR Infiniband.

The Canadian HPC4Health consortium have deployed a federation of OpenStack private clouds using a Mellanox FDR Infiniband network fabric.

Intel’s Omnipath network architecture is starting to emerge in the Scientific OpenStack community. At Pittsburgh Supercomputer Center, the BRIDGES system entered production in early 2016 for HPC and data analytics workloads. It comprises over 800 compute nodes with an Omnipath fabric interconnect. In its current product generation, Omnipath does not support SR-IOV. BRIDGES is a bare metal system, managed using OpenStack Ironic. The Omnipath network management is managed independently of OpenStack.

BRIDGES is described in greater detail in the section OpenStack and HPC Infrastructure Management.

An RDMA-Centric Bioinformatics Cloud at Cambridge University¶

Cambridge University’s Research Computing Services group has a long track record as a user of RDMA technologies such as Infiniband and Lustre across all its HPC infrastructure platforms. When scoping a new bioinformatics compute resource in 2015, the desire to combine this proven HPC technology with a flexible self-service platform led to a requirements specification for an RDMA-centric OpenStack cloud.

Bioinformatics workloads can be IO-intensive in nature, and can also feature IO access patterns that are highly sensitive to IO latency. Whilst this class of workload is typically a weakness of virtualised infrastructure, the effects are mitigated through use of HPC technologies such as RDMA and virtualisation technologies such as SR-IOV to maximise efficiency and minimise overhead.

The added complexity of introducing HPC networking technologies is considerable, but remains hidden from the bioinformatics users of the system. Block-based IO via RDMA is delivered to the kernel of the KVM hypervisor. The compute instances simply see a paravirtualised block device. File-based IO via RDMA is delivered using the Lustre filesystem client drivers running in the VM instances. Through use of SR-IOV virtual functions, this is identical to a bare metal compute node in a conventional HPC configuration. Similarly, MPI communication is performed on the virtualised network interfaces with no discernable difference for the user of the compute instance.

Software architecture of the compute node of an RDMA-centric OpenStack cloud.

The cloud contains 80 compute nodes, 3 management nodes and a number of storage nodes of various kinds. The system runs Red Hat OpenStack Platform (OSP) and is deployed using Red Hat’s TripleO-based process. All the HPC-centric features of the system have been implemented using custom configuration and extensions to TripleO. Post-deployment configuration management is performed using Ansible-OpenStack playbooks, resulting in a devops approach for managing an HPC system.

To deploy a system with RDMA networking enabled in the compute node hypervisor, overcloud management QCOW2 images are created with OpenFabrics installed. Cinder is configured to use iSER (iSCSI Extensions for RDMA) as a transport protocol.

The cloud uses a combination of Mellanox 50G Ethernet NICs and 100G Ethernet switches for its HPC network fabric. RDMA support using RoCEv1 requires layer-2 network connectivity. Consequently OpenStack’s networking is configured to use VLANs for control plane traffic and HPC tenant networking. VXLAN is used for other classes of tenant networking.

A multi-path layer-2 network fabric is created using multi-chassis LAGs. Traffic is distributed across multiple physical links whilst presenting a single logical link for the Ethernet network topology. Port memberships of the tenant network VLANs are managed dynamically using the NEO network management platform from Mellanox, which integrates with OpenStack Neutron.

The Forces Driving HPC and Cloud Diverge in Network Management¶

At the pinnacle of HPC, ultimate performance is achieved through exploiting full knowledge of all hardware details: the microarchitecture of a processor, the I/O subsystem of a server - or the physical location within a network. HPC network management delivers performance by enabling workload placement with awareness of the network topology.

The cloud model succeeds because of its abstraction. Cloud infrastructure commits to delivering a virtualised flat network to its instances. All details of the underlying physical topology are obscured. Where an HPC network management system can struggle to handle changes in physical network topology, cloud infrastructure adapts.

OpenStack provides a limited solution to locality-aware placement, through use of availability zones (AZ). By defining an AZ per top-of-rack switch, a user can request instances be scheduled to be co-resident on the same edge switch. However, this can be a clumsy interface for launching instances on a large private cloud, and AZs cannot be nested to provide multiple levels of locality for co-locating larger workloads.

OpenStack depends on other network management platforms for physical network knowledge, and delegates to them all aspects of physical network management. Network management and monitoring packages such as Observium and Mellanox NEO are complementary to the functionality provided by OpenStack.

Another key theme in HPC network management is in gathering network-centric performance telemetry.

While HPC does not deliver on all of its promise in this area, there is greater focus within HPC network management on the ability to collect telemetry data on the performance of a network for optimising the workload.

Cloud and HPC take very different approaches in this sector.

In general, HPC performance monitoring is done at the application level. HPC application performance analysis typically follows a model in which runtime trace data is gathered during execution for later aggregation and visualisation. This approach avoids overhead when monitoring is not required and minimises the overhead when monitoring is active. When application monitoring is active, leading packages such as OVIS minimise overhead by using RDMA for aggregation of runtime telemetry data. Application performance visualisation is performed using tools such as VAMPIR. All these HPC-derived application performance monitoring tools will also work for applications running within an OpenStack/HPC environment.

At a system level, HPC network performance analysis is more limited in scope, but developments such as PAVE at Lawrence Livermore and more recently INAM^2 from Ohio State University are able to demonstrate a more holistic capability to identify adverse interactions between applications sharing a network, in addition to performance bottlenecks within an application itself.

The pace of development of cloud infrastructure monitoring is faster, and in many cases is derived from open-source equivalents of hyperscaler-developed capabilities. Twitter’s Zipkin is a distributed application performance monitoring framework derived from conceptual details from Google’s Dapper. LinkedIn developed and published Kafka, a distributed near-real-time message log. However, the layers of abstraction that give cloud its flexibility can prevent cloud monitoring from providing performance insights from the physical domain that inform performance in the virtual domain.

At OpenStack Paris in November 2014 Intel demonstrated Apex Lake, a project which aims to provide performance telemetry across these abstraction boundaries - including across virtual/physical network abstractions. Some of these features may have been incorporated into the Intel’s open source Snap telemetry/monitoring framework.

In its present situation, through use of SR-IOV network devices, cloud network infrastructure has demonstrated that it is capable at achieving performance levels that are typically within 1-9% of bare metal. OpenStack can be viewed as the integration and orchestration of existing technology platforms. The physical network performance telemetry of cloud network infrastructure is delegated to the technology platforms upon which it is built. In future, projects such as INAM^2 on the HPC side and Apex Lake on the cloud side may lead to a telemetry monitoring framework capable of presenting performance data from virtual and physical domains in the context of one another.

Acknowledgements¶

This document was originally written by Stig Telfer of StackHPC Ltd with the support of Cambridge University, with contributions, guidance and feedback from subject matter experts:

Professor DK Panda and Dr. Xiaoyi Lu from NOWLAB, Ohio State University.
Jonathan Mills from NASA Goddard Spaceflight Center.

This document is provided as open source with a Creative Commons license with Attribution + Share-Alike (CC-BY-SA)

OpenStack

OpenStack and HPC Network Fabrics¶

Using SR-IOV for Virtualised HPC Networking¶

The Process Flow for Using SR-IOV¶

The Limitations of Using SR-IOV in Cloud Infrastructure¶

Virtualisation-aware MPI for Tightly-Coupled Cloud Workloads¶

Infiniband and other Non-Ethernet Fabrics¶

An RDMA-Centric Bioinformatics Cloud at Cambridge University¶

The Forces Driving HPC and Cloud Diverge in Network Management¶

Further Reading¶

Acknowledgements¶

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OpenStack

OpenStack and HPC Network Fabrics¶

Using SR-IOV for Virtualised HPC Networking¶

The Process Flow for Using SR-IOV¶

The Limitations of Using SR-IOV in Cloud Infrastructure¶

Virtualisation-aware MPI for Tightly-Coupled Cloud Workloads¶

Infiniband and other Non-Ethernet Fabrics¶

An RDMA-Centric Bioinformatics Cloud at Cambridge University¶

The Forces Driving HPC and Cloud Diverge in Network Management¶

Further Reading¶

Acknowledgements¶

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