Deploy Altair Radioss on an Azure virtual machine

Virtual Machines
Virtual Network
CycleCloud

This article briefly describes the steps for running Altair Radioss on a virtual machine (VM) that's deployed on Azure. It also presents the performance results of running Radioss on Azure.

Radioss is a multidisciplinary finite-element solver for linear and nonlinear problems. It’s used to predict crash response and dynamic, transient-loading effects on vehicles, structures, and other products. Radioss:

  • Uses battery and module macro models for crash events, road debris impacts, and shocks to simulate mechanical failures that cause electrical short circuits, thermal runaway, and risk of fire.
  • Provides a composite shell element with delamination tracking and a fast parabolic tetra element.
  • Implements extensive material laws and rupture criteria for crack propagation in brittle materials like windshields.
  • Provides a fast solution for airbag deployment that uses finite-volume method technology.

Radioss is used across industry sectors to provide multiphysics solutions to dynamic problems that combine structures, mechanisms, fluids, and thermal and electromagnetic effects. It's ideal for the automotive, aerospace, and energy industries.

Why deploy Radioss on Azure?

  • Modern and diverse compute options to align with your workload's needs
  • The flexibility of virtualization without the need to buy and maintain physical hardware
  • Rapid provisioning
  • On a single node, performance improvements of as much as 2.76 times over that of 16 CPUs

Architecture

This architecture shows a multi-node configuration, orchestrated with Azure CycleCloud:

Diagram that shows a multi-node configuration for deploying Altair Radioss.

Download a Visio file of this architecture.

This architecture shows a single-node configuration:

Diagram that shows a single-node configuration for deploying Altair Radioss.

Download a Visio file of this architecture.

Components

Compute sizing and drivers

Performance tests of Radioss on Azure used HBv3-series VMs running Linux. The following table provides the configuration details.

VM size vCPU RAM memory (GiB) Memory bandwidth (GBps) Base CPU frequency (GHz) All-cores frequency (GHz, peak) Single-core frequency (GHz, peak) RDMA performance (GBps) Maximum data disks
Standard_HB120rs_v3 120 448 350 1.9 3.0 3.5 200 32
Standard_HB120-96rs_v3 96 448 350 1.9 3.0 3.5 200 32
Standard_HB120-64rs_v3 64 448 350 1.9 3.0 3.5 200 32
Standard_HB120-32rs_v3 32 448 350 1.9 3.0 3.5 200 32
Standard_HB120-16rs_v3 16 448 350 1.9 3.0 3.5 200 32

HBv3-series VMs are optimized for HPC applications like fluid dynamics, explicit and implicit finite-element analysis, weather modeling, seismic processing, reservoir simulation, and RTL simulation.

HBv3 VMs with different numbers of vCPUs were deployed to determine the optimal configuration for Radioss test simulations on a single node. That optimal configuration was then tested in a multi-node cluster deployment.

Required drivers

To use the AMD CPUs on HBv3-series VMs, you need to install AMD drivers.

Radioss installation

Before you install Radioss, you need to deploy and connect a Linux VM and install the required AMD drivers.

For information about deploying the VM, see Run a Linux VM on Azure.

You can install Radioss from Altair One Marketplace. You also need to install Altair License Manager and activate your license via Altair Units Licensing. See the Altair Units Licensing document on Altair One Marketplace. You can find more information about installing Radioss and License Manager and activating your license on Altair One Marketplace. For multi-node installation, see the next section.

Multi-node configuration

You can easily deploy an HPC cluster on Azure by using Azure CycleCloud.

Azure CycleCloud is a tool for orchestrating and managing HPC environments on Azure. You can use CycleCloud to provision infrastructure for HPC systems, deploy HPC schedulers, and automatically scale the infrastructure to run jobs efficiently at any scale.

Azure CycleCloud is a Linux-based web application. We recommend that you set it up by deploying an Azure VM that's based on a preconfigured Azure Marketplace image.

To set up an HPC cluster on Azure, complete these steps:

  1. Install and configure Azure CycleCloud.
  2. Create an HPC cluster from built-in templates.
  3. Connect to the head node (the scheduler).

For multi-node configurations, the Radioss installation process is the same as the process described previously for a single node, except for the path to the installation directory:

  • You need to select /shared for the installation directory path so that the directory is accessible for all nodes.
  • The shared folder path depends on your network attached storage service, like an NFS server, BeeGFS cluster, Azure NetApp Files, Azure HPC Cache, or Azure Active Directory Domain Services.
  • To authorize multi-node VMs to access License Manager, include your authorization code in the job script. For more information about installing Radioss, see Altair One Marketplace.

Radioss performance results

Radioss was tested in single-node and multi-node configurations. Computation time (wall-clock time) was measured. The Linux platform was used, with an Azure Marketplace CentOS 8.1 HPC Gen2 image. The following table provides details.

Operating system version OS architecture MPI
CentOS Linux release 8.1.1911 (Core) x86-64 Intel MPI

Results for a single-node configuration

Nonlinear finite-element analysis was performed to test Radioss on a single node with various numbers of CPUs. See the table in the Compute sizing and drivers section of this article for details about the VMs.

The Neon model was used as a test case:

Figure that shows the Neon model.

The following table provides the numbers of various elements in the model.

Nodal points Parts Materials Property sets 3D solid elements 3D shell elements (4 nodes) 3D beam elements 3D spring elements 3D shell elements (3 nodes) Accelerometers Interfaces Rigid walls Rigid bodies Added nodal masses
1,096,865 340 21 148 2,860 1,054,611 63 4,180 176 7 18 1 694 273

The following table presents the results, in wall-clock time, in seconds.

Model Simulation time (ms) 16 CPUs 32 CPUs 64 CPUs 96 CPUs 120 CPUs
Neon 8 Starter 20.27 22.91 25.78 29.18 31.46
Neon 8 Engine 421.99 246.65 147.31 131.74 128.74
Neon 8 Total runtime 442.26 269.56 173.09 160.92 160.2

The following table shows the relative speed increase for each increase in number of CPUs.

Model 16 CPUs 32 CPUs 64 CPUs 96 CPUs 120 CPUs
Neon 1.00 1.64 2.56 2.75 2.76

Graph that shows the relative speed increases for the Neon model.

Results for a multi-node configuration

As the preceding performance results show, a Standard_HB120-64rs_v3 VM with 64 cores is the optimal configuration. This configuration was used in the multi-node tests. 64 cores were used on each node.

The Taurus model was used as a test case:

Figure that shows the Taurus model.

The following table provides the numbers of various elements in the model.

Nodal points Parts Materials Property sets Boundary conditions 3D solid elements 3D shell elements (4 nodes) 3D beam elements 3D spring elements 3D shell elements (3 nodes) Gravity loads Initial velocities Accelerometers Sensors Interfaces Rigid bodies Added nodal masses Rayleigh damping groups Monitored volumes
9,754,355 1,585 66 762 1 330,418 9,196,272 3,766 417 345,409 1 5 4 4 1,712 901 5 4 8

The following table shows the elapsed wall-clock time, in hours, for the test runs.

Model Simulation time (ms) 1 node 4 nodes 8 nodes 16 nodes
Taurus (T10M) 120 Starter 00:07:47 00:11:12 00:07:04 00:08:13
Taurus (T10M) 120 Engine 37:21:22 10:23:02 08:18:28 04:34:59
Taurus (T10M) 120 Total runtime 37:29:10 10:34:14 08:25:32 04:43:13

Azure cost

Only rendering time is considered for these cost calculations. Application installation time isn't considered.

You can use the wall-clock time and the Azure hourly cost to compute total costs. For the current hourly costs, see Linux Virtual Machines Pricing.

You can use the Azure pricing calculator to estimate the costs for your configuration.

The following table provides the wall-clock times for single-node configurations.

VM size Model Number of CPUs Wall clock time (seconds)
HB120rs_v3 Neon 16 442.26
HB120rs_v3 Neon 32 269.56
HB120rs_v3 Neon 64 173.09
HB120rs_v3 Neon 96 160.92
HB120rs_v3 Neon 120 160.2

The following table provides the wall-clock times for multi-node configurations.

VM size Model Number of CPUs Number of nodes Wall clock time (hours)
HB120-64rs_v3 Taurus (T10M) 64 1 37:29:10
HB120-64rs_v3 Taurus (T10M) 256 4 10:34:14
HB120-64rs_v3 Taurus (T10M) 512 8 08:25:32
HB120-64rs_v3 Taurus (T10M) 1024 16 04:43:13

Summary

  • Radioss was successfully tested on HBv3-series VMs on Azure.
  • Radioss on an Azure VM can solve complex workloads.
  • In a single-node configuration, increasing the number of CPUs increases the relative speed. The optimal configuration is 64 CPUs.
  • Radioss scales impressively up to 16 nodes (1024 CPUs).

Contributors

This article is maintained by Microsoft. It was originally written by the following contributors.

Principal authors:

Other contributors:

To see non-public LinkedIn profiles, sign in to LinkedIn.

Next steps