12.2 TUFLOW Classic

TUFLOW Classic uses the ADI (alternating direction implicit (Stelling, 1984)) finite difference scheme that has been further refined by Syme (1991). For further details on the TUFLOW Classic solution scheme refer to Section 1.1.1. It is a highly efficient scheme, but is difficult to parallelise to use multiple threads. As such it runs on a single core of the main CPU (central processing unit). General hardware advice for TUFLOW Classic is summarised below:

• As all modern CPUs have multiple cores, it is possible to run more than one model at a time on the computer, provided the user has access to sufficient licences.
• If running only one model at a time, the primary factor for model speed is the single core computational capability of the CPU. Generally this is mostly driven by the CPU clock speed - a CPU with fewer cores but a higher clock speed will likely provide better performance than one with many cores and a lower clock speed. CPU type and architecture do also play a role in single core performance. For more information refer to the subsection on performance proxies. When running models with Classic, having a dual socket mainboard will be of little benefit if running just one model at a time. Dual socket mainboards are capable of holding two CPUs, doubling the total number of CPU cores available (e.g. 2 x 16 core CPUs), these are primarily found in server setups.
• If running multiple models simultaneously, the memory bandwidth between CPUs and the mainboard RAM is quite important, in which case a dual CPU mainboard will usually offer better performance than a single CPU mainboard. Further, running multiple models simultaneously will also require increased CPU RAM, which again typically favours a dual CPU mainboard.
• The mainboard memory required for a particular model will vary significantly due to a number of factors related to model setup. In general the memory required will scale proportionally with number of model cells.
• There is no requirement for a particular GPU (Graphics Processing Unit) to be installed. However, having a medium quality GPU installed may assist with graphics rendering in any GIS software being used for visualising model inputs and outputs.
• The amount of CPU RAM will determine the size of the model that can be run or a number of models that can be run at one time. Faster RAM will result in quicker runtimes, however this is usually a secondary consideration to chip speed, cache size and architecture.