Category Archives: Uncategorized

PCRaster-4.0.2 released

We are glad to announce the final release of PCRaster-4.0.2! We fixed several bugs and added some functional enhancements for the Modflow extension. For more information please read the changes document. Packages are available for 64-bit Linux as well as 64-bit and 32-bit Windows systems.

For more information, visit the PCRaster 4.0.2 download page:

Enjoy!

Virtual Globe: PCRaster web simulations of processes on the land surface

The PCRaster team, in particular Koko Alberti who recently joined our team, has developed prototype software to run PCRaster models as web simulations, at a very high (~90 m) resolution, for almost any location on earth! The current facility includes prototype models for sea level change, snow cover, and water erosion. The web simulations are available here (login required). For information and to request a login, please email info@pcraster.nl.

Dr Oliver Schmitz !

 

Yesterday, Oliver Schmitz, one of our team members, received his PhD at Utrecht University. The title of his PhD thesis is “Integrating environmental component models. Development of a software framework”. You can reach him at o.schmitz@uu.nl if you would like to receive his PhD thesis (or to congratulate him!). A short description of his thesis research is here.

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PCRaster-4.0.1 final version released

We are glad to announce the final release of PCRaster-4.0.1! We fixed several bugs, some of them might affect model outcomes.  Please read the changes document carefully. In addition, this is the first release fully supporting 64-bit Windows systems.

For more information, visit the PCRaster 4.0.1 download page:

Enjoy!

New online courses

We have updated our course material. Have a look at the Courses section for two new distance learning courses on PCRaster Python; without tutor support the courses are free of charge.

Improving performance on Linux

It is possible that your PCRaster models are not executing as fast as the could on your Linux system. That can happen because the default memory allocation and de-allocation rules that are in effect on Linux are optimized for system wide efficiency, instead of raw performance. For more information about this, see this document.

You can tune the memory allocation and de-allocation logic using environment variables (note the trailing underscore):

export MALLOC_MMAP_MAX_=0
export MALLOC_TRIM_THRESHOLD_=-1

 

You may want to check if these values help you to squeeze a bit of extra performance out of your models.