- Windows - Intel compatible, 64-bit:
- Linux - Intel compatible, 64-bit:
- RedHat Enterprise 7.6 or higher, and 8
- CentOS 7.6 or higher
- CentOS Stream 8
- Ubuntu LTS 20 and 22
Note: As we add support for newer versions of Linux, support for older versions may have to be withdrawn. If you are using a Linux version near the minimum that we support, we would recommend updating in the near future to ensure you are able to use future CSD software updates without interruption.
- macOS - Intel compatible or Apple ARM (via Rosetta 2 emulation)*, 64-bit:
Note: Apple ARM-based (M1) systems are supported with this release via emulation with Rosetta 2, but with the caveat that there are some issues with certain functionality. We plan to improve support with subsequent releases. Please see this FAQ for more details on our compatibility with macOS Big Sur.
If you choose to use a version other than those listed above we cannot guarantee that the 2022.3 CSD Release software will work correctly, although we will attempt to assist you with any problems you may encounter.
Linux platforms will require the Network Security Services (NSS), OpenGL, Fortran and libXScrnSaver packages to be installed (see Linux Installation in the CSD Release and Installation Notes for more details)
Changes to Supported Platforms and Products
DASH is no longer a core CCDC product and is no longer distributed as part of the CCDC Software Portfolio. In order to support development for its enthusiastic user base, we are opening DASH up for others to contribute to. DASH certainly won't be going away, it will simply be moving to a different home–in particular, an open-source project on GitHub. To find out more about the future direction of DASH, please visit this page: https://www.ccdc.cam.ac.uk/solutions/open-source/dash-open-source/.
The cqbatch and mogulbatch command line utilities are now deprecated - while these remain a part of the CSD Release software during 2022, their use is no longer recommended, and we would instead encourage switching to the use of the CSD Python API. We anticipate that cqbatch will be removed entirely in a future release.
If any of the above will cause difficulties, please contact us at email@example.com to discuss possible solutions.
In addition to the requirement of one of the supported platforms detailed above, the CSD Portfolio software suites also have the following hardware requirements:
- Disk Space:
- CSD Portfolio software and data, 24 GB (7 GB software, 17 GB data).
- Optional CSD-CrossMiner data, 11 GB
- Minimum recommended RAM: 8 GB
- Minimum recommended RAM for use of CSD-CrossMiner: 32 GB
- Graphics card and drivers that support OpenGL version 2 (see this support database entry for more details)
The above supported platforms and system requirements, taken together, form the minimum recommended specification for any system that is planned to be used for a CSD Portfolio software install.
Graphics System Requirements
Products with 3D graphical display functionality are supported only on graphics cards and chipsets that have up-to-date installers and are still in support by the graphics system manufacturers. The graphics drivers in use should support OpenGL 2.1.
Recommended Specification for Improved Performance
The CSD Portfolio software suite is varied in terms of the types of action each software component will perform. There are thus several areas of a computer system where the improved specification of a particular hardware component will improve the performance of the CSD Portfolio software:
- CPU: Improves any aspect of the software that is carrying out calculations, for example, a GOLD docking
- Disk Access Speed: The greatest performance increase will be seen in any aspect of the software that requires access to large data files, such as ConQuest database searches
- RAM: Increased RAM over 8 GB will help when either looking at large datasets or large complex structures or else when using multiple CSD Portfolio programs at the same time. For CSD-CrossMiner use we recommend a minimum of 32 GB RAM as this program requires holding large datasets in memory.
- GPU: A dedicated modern GPU graphics card, such as those available from NVidia or AMD, will improve performance when visualising large complex structures or large packing diagrams.