High performance computing VM sizes
Azure H-series virtual machines (VMs) are designed to deliver leadership-class performance, scalability, and cost efficiency for various real-world HPC workloads.
HBv3-series VMs are optimized for HPC applications such as fluid dynamics, explicit and implicit finite element analysis, weather modeling, seismic processing, reservoir simulation, and RTL simulation. HBv3 VMs feature up to 120 AMD EPYC™ 7003-series (Milan) CPU cores, 448 GB of RAM, and no hyperthreading. HBv3-series VMs also provide 350 GB/sec of memory bandwidth, up to 32 MB of L3 cache per core, up to 7 GB/s of block device SSD performance, and clock frequencies up to 3.675 GHz.
All HBv3-series VMs feature 200 Gb/sec HDR InfiniBand from NVIDIA Networking to enable supercomputer-scale MPI workloads. These VMs are connected in a non-blocking fat tree for optimized and consistent RDMA performance. The HDR InfiniBand fabric also supports Adaptive Routing and the Dynamic Connected Transport (DCT, in addition to standard RC and UD transports). These features enhance application performance, scalability, and consistency, and their usage is strongly recommended.
HBv2-series VMs are optimized for applications driven by memory bandwidth, such as fluid dynamics, finite element analysis, and reservoir simulation. HBv2 VMs feature 120 AMD EPYC 7742 processor cores, 4 GB of RAM per CPU core, and no simultaneous multithreading. Each HBv2 VM provides up to 340 GB/sec of memory bandwidth, and up to 4 teraFLOPS of FP64 compute.
HBv2 VMs feature 200 Gb/sec Mellanox HDR InfiniBand, while both HB and HC-series VMs feature 100 Gb/sec Mellanox EDR InfiniBand. Each of these VM types is connected in a non-blocking fat tree for optimized and consistent RDMA performance. HBv2 VMs support Adaptive Routing and the Dynamic Connected Transport (DCT, in addition to standard RC and UD transports). These features enhance application performance, scalability, and consistency, and their usage is strongly recommended.
HB-series VMs are optimized for applications driven by memory bandwidth, such as fluid dynamics, explicit finite element analysis, and weather modeling. HB VMs feature 60 AMD EPYC 7551 processor cores, 4 GB of RAM per CPU core, and no hyperthreading. The AMD EPYC platform provides more than 260 GB/sec of memory bandwidth.
HC-series VMs are optimized for applications driven by dense computation, such as implicit finite element analysis, molecular dynamics, and computational chemistry. HC VMs feature 44 Intel Xeon Platinum 8168 processor cores, 8 GB of RAM per CPU core, and no hyperthreading. The Intel Xeon Platinum platform supports Intel’s rich ecosystem of software tools such as the Intel Math Kernel Library.
H-series VMs are optimized for applications driven by high CPU frequencies or large memory per core requirements. H-series VMs feature 8 or 16 Intel Xeon E5 2667 v3 processor cores, 7 or 14 GB of RAM per CPU core, and no hyperthreading. H-series features 56 Gb/sec Mellanox FDR InfiniBand in a non-blocking fat tree configuration for consistent RDMA performance. H-series VMs support Intel MPI 5.x and MS-MPI.
All HBv3, HBv2, HB, and HC-series VMs have exclusive access to the physical servers. There is only 1 VM per physical server and there is no shared multi-tenancy with any other VMs for these VM sizes.
The A8 – A11 VMs are retired as of 3/2021. No new VM deployments of these sizes are now possible. If you have existing VMs, refer to emailed notifications for next steps including migrating to other VM sizes in HPC Migration Guide.
Most of the HPC VM sizes feature a network interface for remote direct memory access (RDMA) connectivity. Selected N-series sizes designated with 'r' are also RDMA-capable. This interface is in addition to the standard Azure Ethernet network interface available in the other VM sizes.
This secondary interface allows the RDMA-capable instances to communicate over an InfiniBand (IB) network, operating at HDR rates for HBv3, HBv2, EDR rates for HB, HC, NDv2, and FDR rates for H16r, H16mr, and other RDMA-capable N-series virtual machines. These RDMA capabilities can boost the scalability and performance of Message Passing Interface (MPI) based applications.
SR-IOV support: In Azure HPC, currently there are two classes of VMs depending on whether they are SR-IOV enabled for InfiniBand. Currently, almost all the newer generation, RDMA-capable or InfiniBand enabled VMs on Azure are SR-IOV enabled except for H16r, H16mr, and NC24r. RDMA is only enabled over the InfiniBand (IB) network and is supported for all RDMA-capable VMs. IP over IB is only supported on the SR-IOV enabled VMs. RDMA is not enabled over the Ethernet network.
Operating System - Linux distributions such as CentOS, RHEL, Ubuntu, SUSE are commonly used. Windows Server 2016 and newer versions are supported on all the HPC series VMs. Windows Server 2012 R2 and Windows Server 2012 are also supported on the non-SR-IOV enabled VMs. Note that Windows Server 2012 R2 is not supported on HBv2 onwards as VM sizes with more than 64 (virtual or physical) cores. See VM Images for a list of supported VM Images on the Marketplace and how they can be configured appropriately. The respective VM size pages also list out the software stack support.
InfiniBand and Drivers - On InfiniBand enabled VMs, the appropriate drivers are required to enable RDMA. See VM Images for a list of supported VM Images on the Marketplace and how they can be configured appropriately. Also see enabling InfiniBand to learn about VM extensions or manual installation of InfiniBand drivers.
MPI - The SR-IOV enabled VM sizes on Azure allow almost any flavor of MPI to be used with Mellanox OFED. On non-SR-IOV enabled VMs, supported MPI implementations use the Microsoft Network Direct (ND) interface to communicate between VMs. Hence, only Intel MPI 5.x and Microsoft MPI (MS-MPI) 2012 R2 or later versions are supported. Later versions of the Intel MPI runtime library may or may not be compatible with the Azure RDMA drivers. See Setup MPI for HPC for more details on setting up MPI on HPC VMs on Azure.
RDMA network address space: The RDMA network in Azure reserves the address space 172.16.0.0/16. To run MPI applications on instances deployed in an Azure virtual network, make sure that the virtual network address space does not overlap the RDMA network.
Cluster configuration options
Azure provides several options to create clusters of HPC VMs that can communicate using the RDMA network, including:
Virtual machines - Deploy the RDMA-capable HPC VMs in the same scale set or availability set (when you use the Azure Resource Manager deployment model). If you use the classic deployment model, deploy the VMs in the same cloud service.
Virtual machine scale sets - In a virtual machine scale set, ensure that you limit the deployment to a single placement group for InfiniBand communication within the scale set. For example, in a Resource Manager template, set the
true. Note that the maximum scale set size that can be spun up with
singlePlacementGroup=trueis capped at 100 VMs by default. If your HPC job scale needs are higher than 100 VMs in a single tenant, you may request an increase, open an online customer support request at no charge. The limit on the number of VMs in a single scale set can be increased to 300. Note that when deploying VMs using Availability Sets the maximum limit is at 200 VMs per Availability Set.
MPI among virtual machines: If RDMA (e.g. using MPI communication) is required between virtual machines (VMs), ensure that the VMs are in the same virtual machine scale set or availability set.
Azure CycleCloud - Create an HPC cluster using Azure CycleCloud to run MPI jobs.
Azure Batch - Create an Azure Batch pool to run MPI workloads. To use compute-intensive instances when running MPI applications with Azure Batch, see Use multi-instance tasks to run Message Passing Interface (MPI) applications in Azure Batch.
Microsoft HPC Pack - HPC Pack includes a runtime environment for MS-MPI that uses the Azure RDMA network when deployed on RDMA-capable Linux VMs. For example deployments, see Set up a Linux RDMA cluster with HPC Pack to run MPI applications.
Azure subscription – To deploy more than a few compute-intensive instances, consider a pay-as-you-go subscription or other purchase options. If you're using an Azure free account, you can use only a limited number of Azure compute cores.
Cores quota – You might need to increase the cores quota in your Azure subscription from the default value. Your subscription might also limit the number of cores you can deploy in certain VM size families, including the H-series. To request a quota increase, open an online customer support request at no charge. (Default limits may vary depending on your subscription category.)
Contact Azure Support if you have large-scale capacity needs. Azure quotas are credit limits, not capacity guarantees. Regardless of your quota, you are only charged for cores that you use.
Virtual network – An Azure virtual network is not required to use the compute-intensive instances. However, for many deployments you need at least a cloud-based Azure virtual network, or a site-to-site connection if you need to access on-premises resources. When needed, create a new virtual network to deploy the instances. Adding compute-intensive VMs to a virtual network in an affinity group is not supported.
Resizing – Because of their specialized hardware, you can only resize compute-intensive instances within the same size family (H-series or N-series). For example, you can only resize an H-series VM from one H-series size to another. Additional considerations around InfiniBand driver support and NVMe disks may need to be considered for certain VMs.
- General purpose
- Compute optimized
- Memory optimized
- Storage optimized
- GPU optimized
- Previous generations
- Learn more about configuring your VMs, enabling InfiniBand, setting up MPI and optimizing HPC applications for Azure at HPC Workloads.
- Review the HBv3-series overview and HC-series overview.
- Read about the latest announcements, HPC workload examples, and performance results at the Azure Compute Tech Community Blogs.
- For a higher level architectural view of running HPC workloads, see High Performance Computing (HPC) on Azure.