Operating systems supported¶
Windows Server 2016+
Ubuntu 18 and derivatives (eg. Linux Mint)
Ubuntu 20 and derivatives (eg. Linux Mint)
GPU Hardware Support¶
The M-Star Solver requires GPUs with Compute Capability 3.5 or newer. Most NVidia GPUs produced since 2014 will be compatible. For a complete list of compute capability, refer to NVidia CUDA GPU Capability List .
When multiple GPUs are in use, NVlink should always be installed between the GPUs to ensure top performance.
To execute multi-GPU jobs on Windows, the GPUs must support a feature called TCC Mode. Generally this feature is limited NVidia Quadro, Tesla, and TITAN products. Geforce tends not to have this feature. You should verify independently this specification if you intend to run multi-GPU jobs on Windows. Additionally, TCC mode requires dedicated usage for CUDA jobs, and may not be used for display. For example, you cannot put 2x V100 GPUs in TCC mode and plug one of them into your monitor. You must have a third GPU dedicated for display in that case.
Multi node jobs that span multiple comptuers are currently not supported on Windows. This type of workload is only supported under linux.
Multi-GPU jobs on Windows require TCC Mode. TCC Mode GPUs must only be used for CUDA jobs and may not be used for display purposes.
GPU Driver Support¶
Linux - NVidia driver version 418.39 or newer
Windows - NVidia driver version 418.96 or newer
NVidia Hardware Notes¶
Tesla based hardware¶
Whenever possible, we suggest using NVidia Tesla GPUs. This is enterprise grade equipment intended to be used for heavy computational loads. They have features such as TCC mode on windows, ECC memory, and higher memory capacity.
Quadro based hardware¶
These GPUs work fine and can be cost effective means to run M-Star
Geforce based hardware¶
These are the more consumer grade GPUs intended primarily for rendering 3D scenes. These are compatible with the solver and can be highly cost effective in regards to memory and CUDA cores. Multi-GPU use of Geforce hardware is generally limited to Linux.
Multi-GPU jobs with NVidia RTX 3090 is currently only supported on Linux
For smaller simulation domain sizes, a basic setup as follows can be used.
OS: Microsoft Windows
CPU: Dual core or better
Disk Space: 100GB
GPU: NVIDIA Geforce RTX 2060 6GB
OS: Microsoft Windows
CPU: Quad core or better
Disk 1: 1TB Operating System Drive
Disk 2: 2TB Data Drive for additional simulation scratch space.
Display GPU: Any recent/modern Geforce GPU 2GB+
Accelerator GPU: NVIDIA Tesla V100 or A100
Accelerator GPU: NVIDIA Tesla V100 or A100 (optional). Bridge with NVLINK. Add additional GPU as necessary.
These architectures are out of scope for this document. It is suggested to speak with an HPC representative from a reputable vendor. These general guidelines shown below are a starting point for that discussion:
CPU: Core count must be equal to or greater than number of GPUs in the node.
Memory: 128GB - 256GB+ . More is typically better generally speaking, although the M-Star solver is typically limited to the memory on the GPU
Disk: 200GB - 1TB per M-Star user. This depends on specific needs of end user. Disk requirement vary wildly based on simulation config.
GPU: Always buy the latest, best Tesla GPU available, with the maximum amount of GPU memory. Always max out the number of GPU per node. Always place GPU bridges between GPUs when applicable.
Node Interconnect: Fast interconnect fabric between nodes (20Gb/s or better bandwidth, eg Infiniband)
Running on CPUs¶
This method of execution is still supported. It is not recommended to run on CPU due to the fact that GPUs, even consumer grade level ones, will always beat CPUs in performance. Additionally many advanced features in M-Star that use custom UDFs are not supported on CPU.
Buy a GPU