Changes between Version 5 and Version 6 of palm

Timestamp:

Dec 7, 2017 8:37:39 AM (7 years ago)

Author:

maronga

Comment:

--

palm

@@ -21 +21 @@
 The parallelization of the code is achieved by a 2-D domain decomposition method along the x and y direction with equally sized subdomains. Ghost layers are added at the side boundaries of the subdomains in order to account for the local data dependencies, which are caused by the need to compute finite differences at these positions. The number of ghost layers that are used in PALM depend on the order of the advection scheme, with three layers for the 5th-order Wicker-Skamarock scheme. Ghost layer data are exchanged after every time step. Data exchange between proecessor cores is realized using the Message Passing Interface (MPI). Additional loop vectorization via OpenMP is realized which also allows a so-called hybrid parallelization.
 
-PALM shows excellent scaling which was tested for up to 50,000 processor cores [wiki:doc/tec/parallel#Parallelizationandoptimizationdetails (details)].
+PALM shows excellent scaling which was tested for up to 50,000 processor cores ([wiki:doc/tec/parallel#Parallelizationandoptimizationdetails details]).
 
 == External forcing and nesting ==
@@ -44 +44 @@
 For more details, see [wiki:doc PALM documentation].
 
+== Data output and handling ==
+ ue to the enormous amount of data that comes along with computationally expensive LES, the data handling plays a key role for the performance of LES models and for data analysis during post-processing. PALM is optimized to pursue the strategy of performing data operations to great extent online during the simulation instead of postpone these operations to the post-processing. In this way, the data output (e.g., of huge 4-D data, or temporal averages) can be significantly reduced. In order to allow the user to perform own calculations during runtime, the user interface offers a wide range of possibilities, e.g., for defining user-defined output quantities (see Sect. user interface).
 
+PALM allows data output for different quantities as time series, (horizontally-averaged) vertical profiles, 2-D cross sections, 3-D volume data, and masked data (see Sect. user interface ). All data output files are in netCDF format, which can be processed by different public domain and commercial software. NetCDF data can also be easily read from Fortran programs, provided that a netCDF library is available. The netCDF libraries currently support three different binary formats for netCDF files: classic, 64-bit offset, and netCDF-4. The latter was introduced in netCDF version 4.0 and is based on the HDF5 (​http://www.hdfgroup.org/HDF5) data format. PALM is able to handle all three netCDF formats and also supports parallel I/O for netCDF-4.