Desmond

System Requirements

RedHat Enterprise Linux (RHEL) 8.8, 8.10, 9.2, 9.4

Please make sure the listed packages are installed:

Required packages

sudo yum/dnf install <lib>

Rocky Linux 8.8, 8.10, 9.2, 9.4

Please make sure the listed packages are installed:

Required packages

sudo yum/dnf install <lib>

Ubuntu 22.04 LTS and 24.04 LTS

Please make sure the listed packages are installed:

Required packages

sudo apt-get install <lib>

NOTES:

All supported distributions have a glibc of 2.28 or greater.

If using NFS, file locking must be enabled.

We aim to provide support for new operating system versions 6 months after their public release.

Support cannot be provided once an OS platform version has reached "end of life" (EOL). Check with your platform provider for EOL information.

In 2025-2 Schrödinger software uses Schrödinger License Manager by default. FlexNet licensing has been discontinued. See Schrödinger License Manager Instructions. Learn more about Transitioning from FlexNet licensing to Schrödinger License Manager.

To view a list of recent infrastructure changes that may require changes from your IT team click here.

	Required	Considerations
Driver	The driver (master job) must run for the complete duration of the job without being interrupted. This means the computing resource on which it runs cannot be a spot or preemptible cloud instance. These nodes can be pre-empted (terminated) and if that happens your whole job will be lost. The -DRIVERHOST argument determines where the driver runs. Select a host entry that is for an on-demand (i.e. not preemptible) node type.	If sufficient licenses and computational resources are available to run multiple Active Learning Glide jobs simultaneously, it is recommended to configure the driver host entry so that it requests an entire node, to avoid multiple drivers potentially using the same node and scratch filesystem, and thereby doubling (or more) the space requirement.
Processor (CPU)	x86_64 compatible processor	For large jobs, computing on a cluster with a queueing system is recommended, with the following hardware components: A highly capable file server for the external network. Shared storage for the intra-cluster network, to reduce traffic to and from the external network. Fast processors, large memory, and high-quality motherboards and network interfaces, especially on the management nodes.
System memory (RAM)	64 GB memory for the entire node	RAM is not related to the input file size, only the disk space is related.
Disk space		The amount of scratch space required on the DRIVERHOST is relative to the size of the input ligand file. See example below.

(General-purpose computing on graphics processing units)

Architecture	Server / HPC	Workstation
Pascal	Tesla P40 Tesla P100	Quadro P5000
Volta	Tesla V100
Turing	Tesla T4	Quadro RTX 5000
Ampere	Tesla A100	RTX A4000 RTX A5000
Ada Lovelace	L4	NVIDIA RTX 4000 SFF Ada NVIDIA RTX 2000 Ada
Hopper	H100

To check the compute capability of Nvidia cards, visit Nvidia's website.

We support only the NVIDIA 'recommended / certified / production branch' Linux drivers for these cards with minimum CUDA version 12.0.
To check Nvidia's driver information, see this page.
To check the compute capability of Nvidia cards, visit Nvidia's website.

We support NVIDIA’s Multi-Instance GPU (MIG) feature. Please make sure that MIG-enabled GPUs are being used in conjunction with a queueing system which supports this feature, see How do I make use of Multi-instance GPUs (MIG).

For information on pre-configured Schrödinger compatible GPU boxes see MD Compatible Systems and FEP+ Compatible Systems.

Standard support does not cover consumer-level GPU cards such as GeForce GTX cards.
In the 2024-2 release, the "Discounted Cores" of an L4 was decreased from 7424 to 5120. This adjustment was made to align with the L4's status as a preferred card due to its widespread availability on-premises and through cloud providers. Additionally, the L4's low power consumption, speed, and sufficient memory make it suitable for running the majority of workflows in a configuration with one GPU per node.
Ensure that your machine has adequate cooling for the graphics cards. Running simulations on a GPU generates a lot of heat which must be dissipated. Inadequate cooling may lead to lower performance and damages of the graphics cards.
The minimum is not necessarily the optimum hardware configuration, particularly if you are planning to run large modeling jobs.
If you already have another NVIDIA GPGPU and would like to know if we have experience with it, please contact our support at help@schrodinger.com.