Infrastructure#
This page describe the main infrastructures of the icon model such as the parallelization and I/O.
Parallelization#
The ICON model leverages parallelization approaches to enhance computational efficiency and performance. MPI (Message Passing Interface) is employed for distributed memory parallelization, specifically distributing the computational workload horizontally across multiple nodes. This means that the horizontal domain of the model is divided into smaller subdomains, distributed across nodes, allowing for efficient communication and computation on a distributed system. OpenMP (Open Multi-Processing) is used for shared memory parallelization, allowing multiple threads to execute concurrently within a single node, thus speeding up computations. For GPU acceleration, OpenACC compiler directives are utilized to offload compute-intensive tasks to GPUs, significantly reducing runtime by exploiting the massive parallelism offered by modern GPUs. These parallelization strategies collectively ensure that the ICON model can handle the complex and large-scale computations required for accurate and timely weather forecasting.
A more detailed description of the parallelization is provided in the ICON Tutorial section 8.
Input & Output (I/O)#
Detail information to Input and Output can be found in the ICON Tutorial in respectively section 2 and 7.
Coupling (YAC)#
YAC (Yet Another Coupler) is a flexible coupling library which comes with ICON. Its interface is compatible to the well-known OASIS coupler and it can be used as a full replacement of it. YAC supports many different horizontal interpolations and a unique interpolation stack to control alternatives in case direct interpolation is not feasible. It is not only used for coupling atmosphere and ocean components of ICON, but also for a highly flexible output method.
Output Coupling#
Testing#
The ICON model integrates a set of tests for supported systems. The internal CI infrastructure includes: