OpenMP: Difference between revisions
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See also | == See also == | ||
* [https://docs.loni.org/wiki/Introduction_to_OpenMP Introduction to OpenMP] | * [https://docs.loni.org/wiki/Introduction_to_OpenMP Introduction to OpenMP] | ||
* [https://computing.llnl.gov/tutorials/openMP openMP tutorial] | * [https://computing.llnl.gov/tutorials/openMP openMP tutorial] | ||
* [http://thread.gmane.org/gmane.comp.gis.grass.devel/30313 Experimental MP support for r.mapcalc is already done in GRASS 7svn] | |||
** it is important to understand which modules are processor bound, and concentrate on them. i.e. do not needlessly complicate the code of non-long running processor bound modules | |||
[[Category: Development]] | [[Category: Development]] |
Revision as of 23:57, 30 March 2009
Multithreaded jobs in GRASS
OpenMP is an implementation of multithreading, a method of parallelization whereby the master "thread" (a series of instructions executed consecutively) "forks" a specified number of slave "threads" and a task is divided among them (from wikipedia). The job is distributed over the available processor cores (2-core, 4-core, ...).
The (yet) only parallelized library in GRASS >=6.3 is GRASS Partial Differential Equations Library (GPDE). The library design is thread safe and supports threaded parallelism with OpenMP. The code is not yet widely used in GRASS. See here for details.
How to activate it with GCC >= 4.2 (compiler flag '-fopenmp' as well as library '-lgomp' are needed):
# GPDE with openMP support: cd lib/gpde/ vim Makefile # uncomment the EXTRA_CFLAGS row and switch the two existing EXTRA_LIBS rows
General code structure
Example cited from "openMP tutorial" (see below):
#include <omp.h> int main () { int var1, var2, var3; Some serial code ... /* Beginning of parallel section. Fork a team of threads. */ /* Specify variable scoping */ #pragma omp parallel private(var1, var2) shared(var3) { /* Parallel section executed by all threads */ ... /* All threads join master thread and disband */ } /* end pragma */ /* Resume serial code */ ... }
See also
- Introduction to OpenMP
- openMP tutorial
- Experimental MP support for r.mapcalc is already done in GRASS 7svn
- it is important to understand which modules are processor bound, and concentrate on them. i.e. do not needlessly complicate the code of non-long running processor bound modules