Deploy Samadii SCIV on a virtual machine

Azure Virtual Machines
Azure 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. Samadii SCIV is based on a GPU architecture and uses CUDA technology.

SCIV is used by manufacturers of display devices and semiconductors, in aerospace and 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 either optimized scaling or optimized cost efficiency


Diagram that shows an architecture for deploying Samadii SCIV.

Download a Visio file of this architecture.


Compute sizing and drivers

The performance tests of Samadii SCIV on Azure used NVv3, NCasT4_v3, NCv3, ND_A100_v4, and NC_A100_v4 series VMs running Windows 10. The following table provides details about the VMs.

VM size GPU name vCPUs Memory, in GiB Maximum data disks 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
Standard_NC24ads_A100_v4 A100 24 220 32 1 80 30,000 / 1,000 1,123 2

Required drivers

To take advantage of the GPU capabilities of NVv3, NCasT4_v3, NCv3, ND_A100_v4, and NC_A100_v4 series VMs, you need to install NVIDIA GPU drivers.

To use AMD processors on NCasT4_v3, NCv3, ND_A100_v4, and NC_A100_v4 series VMs, you need to install AMD drivers.

Samadii SCIV installation

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

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


NVIDIA Fabric Manager installation is required for VMs that use NVLink. ND_A100_v4 and NC_A100_v4 series VMs use this technology.

Following are some prerequisites for running Samadii applications.

  • Windows 10 (x64) OS
  • One or more NVIDIA CUDA-enabled GPUs: Tesla, Quadro, or GeForce series
  • Visual C++ 2010 SP1 Redistributable Package
  • Microsoft MPI v7.1
  • .NET Framework 4.5

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

The following table shows the operating system versions and processors that were used for the tests.

VM series ND_A100_v4 NCv3 NCasT4_v3 NVv3 NC_A100_v4
Operating system version Windows 10 Professional, version 20H2 Windows 10 Professional, version 20H2 Windows 10 Professional, version 20H2 Windows 10 Professional, version 20H2 Windows 10 Professional, version 21H2
OS architecture x86-64 x86-64 x86-64 x86-64 x86-64
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 AMD EPYC 7V13, 64-core processor, 2.44 GHz

The PNS model was used for testing:

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

  • Size: 17,652
  • Cell type: Shell
  • Solver: Samadii SCIV V21 R1
  • Number of GPUs used for all simulations: One

The following table shows the elapsed runtimes and relative speed increases on each VM series. The NVv3 series VM is used as a baseline for the relative speed increases.

VM series Number of GPUs Elapsed time, in seconds Relative speed increase
NVv3 1 93,483.74 N/A
NCasT4_v3 1 38,311.8 2.44
NCv3 1 27,096.83 3.45
ND_A100_v4 1 16,322.98 5.73
NC_A100_v4 1 40,895.55 2.29

This graph shows the relative speed increases:

Graph that shows the relative speed increases.

Azure cost

The following table presents simulation runtimes in hours. To compute the total cost, multiply these times by the Azure VM hourly costs for NVv3, NCasT4_v3, NCv3, ND_A100_v4, and NC_A100_v4 series 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 1 4.53
Standard_NC24ads_A100_v4 1 11.36


  • Samadii SCIV was tested on ND_A100_v4, NCv3, NCasT4_v3, NVv3, and NC_A100_v4 series VMs.
  • SCIV performance scales well on NCasT4_v3, NCv3, ND_A100_v4, and NC_A100_v4 series VMs.
  • The NCasT4_v3 series VMs are more cost efficient than ND_A100_v4, NCv3, and NVv3 series VMs.
  • Of the five VM series, ND_A100_v4 provides the best relative speed increase.


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

Principal authors:

Other contributors:

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