Provide a software-as-a-service (SaaS) platform for computer-aided engineering (CAE) on Azure.
High Performance Computing (HPC) on Azure
Introduction to HPC
High Performance Computing (HPC), also called "Big Compute", uses a large number of CPU or GPU-based computers to solve complex mathematical tasks.
Many industries use HPC to solve some of their most difficult problems. These include workloads such as:
- Oil and gas simulations
- Semiconductor design
- Weather modeling
How is HPC different on the cloud?
One of the primary differences between an on-premise HPC system and one in the cloud is the ability for resources to dynamically be added and removed as they're needed. Dynamic scaling removes compute capacity as a bottleneck and instead allow customers to right size their infrastructure for the requirements of their jobs.
The following articles provide more detail about this dynamic scaling capability.
As you're looking to implement your own HPC solution on Azure, ensure you're reviewed the following topics:
There are a number of infrastructure components necessary to build an HPC system. Compute, Storage, and Networking provide the underlying components, no matter how you choose to manage your HPC workloads.
Example HPC architectures
There are a number of different ways to design and implement your HPC architecture on Azure. HPC applications can scale to thousands of compute cores, extend on-premises clusters, or run as a 100% cloud-native solution.
The following scenarios outline a few of the common ways HPC solutions are built.
Azure offers a range of sizes that are optimized for both CPU & GPU intensive workloads.
CPU-based virtual machines
GPU-enabled virtual machines
N-series VMs feature NVIDIA GPUs designed for compute-intensive or graphics-intensive applications including artificial intelligence (AI) learning and visualization.
Large-scale Batch and HPC workloads have demands for data storage and access that exceed the capabilities of traditional cloud file systems. There are a number of solutions to manage both the speed and capacity needs of HPC applications on Azure
- Avere vFXT for faster, more accessible data storage for high-performance computing at the edge
- Storage Optimized Virtual Machines
- Blob, table, and queue storage
- Azure SMB File storage
For more information comparing Lustre, GlusterFS, and BeeGFS on Azure, review the Parallel Files Systems on Azure e-book.
H16r, H16mr, A8, and A9 VMs can connect to a high throughput back-end RDMA network. This network can improve the performance of tightly coupled parallel applications running under Microsoft MPI or Intel MPI.
Building an HPC system from scratch on Azure offers a significant amount of flexibility, but is often very maintenance intensive.
- Set up your own cluster environment in Azure virtual machines or virtual machine scale sets.
- Use Azure Resource Manager templates to deploy leading workload managers, infrastructure, and applications.
- Choose HPC and GPU VM sizes that include specialized hardware and network connections for MPI or GPU workloads.
- Add high performance storage for I/O-intensive workloads.
Hybrid and cloud Bursting
If you have an existing on-premise HPC system that you'd like to connect to Azure, there are a number of resources to help get you started.
First, review the Options for connecting an on-premises network to Azure article in the documentation. From there, you may want information on these connectivity options:
Once network connectivity is securely established, you can start using cloud compute resources on-demand with the bursting capabilities of your existing workload manager.
There are a number of workload managers offered in the Azure Marketplace.
- RogueWave CentOS-based HPC
- SUSE Linux Enterprise Server for HPC
- TIBCO Grid Server Engine
- Azure Data Science VM for Windows and Linux
Azure Batch is a platform service for running large-scale parallel and high-performance computing (HPC) applications efficiently in the cloud. Azure Batch schedules compute-intensive work to run on a managed pool of virtual machines, and can automatically scale compute resources to meet the needs of your jobs.
SaaS providers or developers can use the Batch SDKs and tools to integrate HPC applications or container workloads with Azure, stage data to Azure, and build job execution pipelines.
Azure CycleCloud Provides the simplest way to manage HPC workloads using any scheduler (like Slurm, Grid Engine, HPC Pack, HTCondor, LSF, PBS Pro, or Symphony), on Azure
CycleCloud allows you to:
- Deploy full clusters and other resources, including scheduler, compute VMs, storage, networking, and cache
- Orchestrate job, data, and cloud workflows
- Give admins full control over which users can run jobs, as well as where and at what cost
- Customize and optimize clusters through advanced policy and governance features, including cost controls, Active Directory integration, monitoring, and reporting
- Use your current job scheduler and applications without modification
- Take advantage of built-in autoscaling and battle-tested reference architectures for a wide range of HPC workloads and industries
The following are examples of cluster and workload managers that can run in Azure infrastructure. Create stand-alone clusters in Azure VMs or burst to Azure VMs from an on-premises cluster.
- Alces Flight Compute
- TIBCO DataSynapse GridServer
- Bright Cluster Manager
- IBM Spectrum Symphony and Symphony LSF
- PBS Pro
- Microsoft HPC Pack
Containers can also be used to manage some HPC workloads. Services like the Azure Kubernetes Service (AKS) makes it simple to deploy a managed Kubernetes cluster in Azure.
Managing your HPC cost on Azure can be done through a few different ways. Ensure you've reviewed the Azure purchasing options to find the method that works best for your organization.
Low priority VMs allow you to take advantage of our unutilized capacity at a significant cost savings.
For an overview of security best practices on Azure, review the Azure Security Documentation.
In addition to the network configurations available in the Cloud Bursting section, you may want to implement a hub/spoke configuration to isolate your compute resources:
Run custom or commercial HPC applications in Azure. Several examples in this section are benchmarked to scale efficiently with additional VMs or compute cores. Visit the Azure Marketplace for ready-to-deploy solutions.
Check with the vendor of any commercial application for licensing or other restrictions for running in the cloud. Not all vendors offer pay-as-you-go licensing. You might need a licensing server in the cloud for your solution, or connect to an on-premises license server.
Graphics and rendering
- Autodesk Maya, 3ds Max, and Arnold on Azure Batch
AI and deep learning
There are a number of customers who have seen great success by using Azure for their HPC workloads. You can find a few of these customer case studies below:
- AXA Global P&C
- Hymans Robertson
- Microsoft Research
- Mitsubishi UFJ Securities International
- Towers Watson
Other important information
- Ensure your vCPU quota has been increased before attempting to run large-scale workloads.
For the latest announcements, see:
Microsoft Batch Examples
These tutorials will provide you with details on running applications on Microsoft Batch