Deploy Samadii SCIV on a virtual machine

Virtual Machines
Virtual Network

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

Samadii SCIV (Statistical Contact in Vacuum) analyzes fluid behavior, deposition processes, and chemical reactions on rarefied gas regions by using the direct simulation Monte Carlo (DSMC) method. To calculate the physical phenomena represented by the Boltzmann equation, the DSMC method uses representative particles, which replace the real molecules. SCIV also provides functions for traditional flow simulation, display deposition processes, and semiconductor device analysis in rarefied gas regions.

SCIV is used by manufacturers of display devices and semiconductors, in aerospace, manufacturing, and in other industries.

Why deploy Samadii SCIV 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
  • Strong performance scale-up, with configurations that provide optimized scaling or optimized cost efficiency

Architecture

Diagram that shows an architecture for deploying Samadii SCIV.

Download a Visio file of this architecture.

Components

Compute sizing and drivers

The performance tests of Samadii SCIV on Azure used NVv3, NCas_T4_v3, NCv3, and ND_A100_v4 VMs running Windows 10. The following table provides details about the VMs.

VM size GPU name vCPU Memory, in GiB Maximum data disks GPU GPU memory, in GiB Maximum uncached disk throughput, IOPS / MBps) Temporary storage (SSD), in GiB Maximum NICs
Standard_NV12s_v3 Tesla M60 12 112 12 1 8 20,000 / 200 320 4
Standard_NC4as_T4_v3 Tesla T4 4 28 8 1 16 - 180 2
Standard_NC6s_v3 V100 6 112 12 1 16 20,000 / 200 736 4
Standard_ND96asr_v4 A100 96 900 32 8 40 80,000 / 800 6,000 8

Required drivers

To take advantage of the GPU capabilities of NVv3, NCas_T4_v3, NCv3, and ND_A100_v4 VMs, you need to install NVIDIA GPU drivers.

To use AMD processors on NCas_T4_v3, NCv3, and ND_A100_v4 VMs, you need to install AMD drivers.

Samadii SCIV installation

Before you install SCIV, you need to deploy and connect a VM, install an eligible Windows 10 image, and install the required NVIDIA and AMD drivers.

For information about eligible Windows images, see How to deploy Windows 10 on Azure and Use Windows client in Azure for dev/test scenarios.

Important

NVIDIA Fabric Manager installation is required for VMs that use NVLink. ND_A100_v4 VMs use this technology.

For information about deploying the VM and installing the drivers, see Run a Windows VM on Azure.

The product installation process involves installing a license server, installing Samadii SCIV, and configuring the license server. For more information about installing SCIV, contact Metariver Technology.

Samadii SCIV performance results

Windows 10 Professional, version 20H2, with an x86-64 architecture, was used for all tests. The following table shows the processors that were used.

ND_A100_v4 NCv3 NCas_T4_v3 NVv3
Processor AMD EPYC 7V12, 64-core processor, 2.44 GHz (2 processors) Intel Xeon CPU E5-2690 v4 AMD EPYC 7V12, 64-core processor, 2.44 GHz Intel Xeon CPU E5-2690 v4

The PNS model was used for testing.

Screenshot that shows the PNS model that was used for testing.

The following table shows the elapsed runtimes and relative speed increases on the four VMs.

VM Elapsed time, in seconds Relative speed increase
NVv3 93,483.74 1.00
NCas_T4_v3 38,311.8 2.44
NCv3 27,096.83 3.45
ND_A100_v4 16,322.98 5.73

This graph shows the relative speed increases:

Graph that shows the relative speed increases.

Azure cost

The following table presents wall-clock times in hours. To compute the total cost, multiply these times by the Azure VM hourly costs for NVv3, NCasT4_v3, NCsv3, and NDA100v4 VMs. For the current hourly costs, see Windows Virtual Machines Pricing.

Only simulation runtime is considered in these cost calculations. Application installation time and license costs aren't included.

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

VM size Number of GPUs Wall-clock time, in hours
Standard_NV12s_v3 1 25.97
Standard_NC4as_T4_v3 1 10.64
Standard_NC6s_v3 1 7.53
Standard_ND96asr_v4 8 4.53

Summary

  • Samadii SCIV was successfully tested on NDv4, NCv3, NCasT4_v3, and NVv3 VMs.
  • SCIV performance scales well on NCasT4_v3, NCv3, and NDv4 VMs.
  • The NCasT4_v3 VM performs well and is cost efficient.
  • Of the four VMs, NDv4 provides the best scale-up.

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