Deploy Samadii Plasma on a virtual machine

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Virtual Network

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

Samadii Plasma is a particle-based solution for the analysis of plasma behavior.

Samadii Plasma:

  • Enables high-speed plasma analysis by means of high-speed electromagnetic field analysis and particle-based gas analysis.
  • Uses the finite element method to analyze the Maxwell equation.
  • Calculates various reactions based on collision theory by freely inputting collision cross section and chemical reaction equations.

Organizations that use Samadii Plasma include manufacturers of flat panel and OLED displays and manufacturers of semiconductors. This solution is ideal for the manufacturing and electronics industries.

Why deploy Samadii Plasma 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
  • Impressive performance results for simulations with varying levels of complexity

Architecture

Diagram that shows an architecture for deploying Samadii Plasma.

Download a Visio file of this architecture.

Components

Compute sizing and drivers

The performance tests of Samadii Plasma 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 vCPU Memory, in GiB Maximum data disks Number of GPUs GPU memory, in GiB Maximum uncached disk throughput, in 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 Plasma installation

Before you install Plasma, 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 Plasma, and configuring the license server. For more information about installing Plasma, contact Metariver Technology.

Samadii Plasma 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

Three models were used for testing, as shown in the following sections.

Results for the magnetron sputter model

Screenshot that shows the magnetron sputter model.

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

VM Elapsed time, in seconds Relative speed increase
NVv3 12,825.36 1.00
NCas_T4_v3 7,606.59 1.69
NCv3 2,798.55 4.58
ND_A100_v4 1,977 6.49

This graph shows the relative speed increases:

Graph that shows the relative speed increases for the magnetron sputter model.

Results for the import inlet model

Screenshot that shows the import inlet model.

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

VM Elapsed time, in seconds Relative speed increase
NVv3 248.99 1.00
NCas_T4_v3 159.61 1.56
NCv3 141.59 1.76
ND_A100_v4 112 2.22

This graph shows the relative speed increases:

Graph that shows the relative speed increases for the import inlet model.

Results for the sputtering target model

Screenshot that shows the sputtering target model.

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

VM Elapsed time, in seconds Relative speed increase
NVv3 13.82 1.00
NCas_T4_v3 8.46 1.63
NCv3 6.86 2.01
ND_A100_v4 5.9 2.34

This graph shows the relative speed increases:

Graph that shows the relative speed increases for the sputtering target model.

Azure cost

The following tables present 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.

Cost, magnetron sputter model

VM size Number of GPUs Wall-clock time, in hours
Standard_NV12s_v3 1 3.56
Standard_NC4as_T4_v3 1 2.11
Standard_NC6s_v3 1 0.78
Standard_ND96asr_v4 8 0.55

Cost, import inlet model

VM size Number of GPUs Wall-clock time, in hours
Standard_NV12s_v3 1 0.07
Standard_NC4as_T4_v3 1 0.04
Standard_NC6s_v3 1 0.04
Standard_ND96asr_v4 8 0.03

Cost, sputtering target model

VM size Number of GPUs Wall-clock time, in hours
Standard_NV12s_v3 1 0.0038
Standard_NC4as_T4_v3 1 0.0024
Standard_NC6s_v3 1 0.0019
Standard_ND96asr_v4 8 0.0016

Summary

  • Samadii Plasma was successfully tested on NDv4, NCv3, NCasT4_v3, and NVv3 VMs.
  • For complex models, the NCv3 and NDv4 VMs provide good results.
  • For models with less complexity, the NCasT4_v3 VM provides good scale-up and is cost efficient.

Contributors

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

Principal authors:

Other contributors:

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