Deploy Sandi HiFUN on an Azure virtual machine

Azure Virtual Machines
Azure Virtual Network

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

HiFUN is a general-purpose computational fluid dynamics (CFD) application. You can use it to simulate airflow over aircraft, automobiles, and structures like buildings and ships.

HiFUN has these capabilities:

  • Provides a robust, fast, and accurate solver for aerodynamic design data
  • Uses an unstructured cell-centered finite volume method that can handle complex geometries and flow physics
  • Handles MPI directives for parallel computing on distributed-memory HPC
  • Can scale over thousands of processor cores
  • Can be ported to NVIDIA GPUs for parallel computing via OpenACC constructs

Sandi HiFUN is used in the aerospace, automotive, industrial, and wind/turbine industries.

HBv3 and NCasT4_v3 series VMs were used to test the performance of HiFUN on Azure.

Why deploy HiFUN on Azure?

  • Modern and diverse compute options to meet your workload's needs
  • The flexibility of virtualization without the need to buy and maintain physical hardware
  • Rapid provisioning

Architecture

Diagram that shows an architecture for running HiFUN on a virtual machine.

Download a Visio file of this architecture.

Components

Compute sizing and drivers

Performance tests of HiFUN on Azure used HBv3 and NCasT4_v3 VMs running the Linux CentOS operating system. The following table provides details about HBv3-series VMs.

VM size vCPU 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

The following table provides details about NCasT4_v3 VMs.

VM size vCPU Memory, in GiB Temporary storage (SSD), in GiB GPU GPU memory, in GiB Maximum data disks Maximum NICs / Expected network bandwidth, in Mbps
Standard_NC4as_T4_v3 4 28 180 1 16 8 2 / 8,000
Standard_NC8as_T4_v3 8 56 360 1 16 16 4 / 8,000
Standard_NC16as_T4_v3 16 110 360 1 16 32 8 / 8,000
Standard_NC64as_T4_v3 64 440 2,880 4 64 32 8 / 32,000

Required drivers

To use InfiniBand, you need to enable InfiniBand drivers.

To enable the GPU capabilities of NCasT4_v3 VMs, you need to install NVIDIA GPU drivers.

HiFUN installation

Before you install HiFUN, you need to deploy and connect a VM. For information about deploying the VM and installing the drivers, see Run a Linux VM on Azure.

For more information about installing HiFUN on an Azure VM, you can contact Sandi at sales@sandi.co.in or info@sandi.co.in.

HiFUN performance results

The Windsor model is used in this performance evaluation.

Screenshots that show the Windsor model.

The following tables provide details about the model.

Flow conditions
Parameter Value
Mach number 0.1207
Velocity 40 m/s
Reynolds number 1.8 million
Flow direction Aligned to x axis
Workload
Model Windsor car body
Number of volumes 7.456 million

Performance results for HiFUN 4.1.1 on HBv3

VM size Number of iterations Time per iteration (seconds)1 Relative speed increase
Standard_HB120-16rs_v3 100 10.13 1.00
Standard_HB120-32rs_v3 100 5.29 1.91
Standard_HB120-64rs_v3 100 2.76 3.67
Standard_HB120-96rs_v3 100 2.00 5.07
Standard_HB120rs_v3 100 1.70 5.96

1 To negate the effect of input/output operations per second (IOPS), the average time of 51-60 recorded iterations is presented here.

This graph shows the relative speed increase2 as the number of CPUs increases:

Graph that shows the relative speed increase on an HBv3 VM.

2 The 16-CPU configuration is used as a baseline for the relative-speed calculations.

Performance results for HiFUN 4.1.1 on NCasT4

CPU configuration Number of CPUs/GPUs Number of iterations Time per iteration (seconds)3 Relative speed increase
24 CPU 24 CPU 100 7.70 1.00
1 GPU 100 5.55 1.39
2 GPU 100 4.07 1.89
4 GPU 100 2.91 2.65
32 CPU 32 CPU 100 5.59 1.00
1 GPU 100 4.99 1.12
2 GPU 100 3.59 1.56
4 GPU 100 2.45 2.28
48 CPU 48 CPU 100 4.15 1.00
1 GPU 100 5.18 0.80
2 GPU 100 3.32 1.25
4 GPU 100 2.02 2.05

3 To negate the effect of IOPS, the average time of 51-60 recorded iterations is presented here.

This graph shows the relative speed increase4 as the number of GPUs increases:

Graph that shows the relative speed increase on an NCasT4 VM.

4 The CPU configurations listed in the preceding table are used as the baselines for the relative-speed calculations.

Additional notes about the tests

  • HiFUN was successfully tested on HBv3 and NCasT4 VMs on Azure.
  • Every CPU increase provides a good speed increase on all VM sizes. The peak speed increase of 5.96x is achieved with 120 CPUs.
  • Every GPU increase provides a good speed increase on all CPU configurations. The peak speed increase of 2.65x is achieved with 4 GPUs.

Azure cost

Only model running time (wall clock time) is considered for these cost calculations. Application installation time isn't considered. The numbers presented here are indicative of your potential results. The actual numbers depend on the size of the model.

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

The following tables provide elapsed times in hours. To compute the total cost, multiply these times by the hourly costs for Linux VMs.

HBv3

VM size Number of CPUs Elapsed time (hours)
Standard_HB120-16rs_v3 16 0.297
Standard_HB120-32rs_v3 32 0.156
Standard_HB120-64rs_v3 64 0.083
Standard_HB120-96rs_v3 96 0.061
Standard_HB120rs_v3 120 0.052

NC64as_T4_v3

CPU/GPU Elapsed time (hours)
CPU 0.116
4 GPU 0.057

Summary

Azure provides robust compute services that support GPU-intensive workloads and offers unlimited scalability for HPC applications. You can use H-series virtual machines for memory-bound applications and N-series virtual machines for graphic-intensive applications.

Contributors

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

Principal authors:

Other contributors:

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Next steps