Getting started
This course offers lecture, discussion, and hands-on exercises on topics about efficient computing for spatial data models. We encourage you to work along with us on the exercises, which blend C/C++, FORTRAN, and R. To participate fully in the exercises, you’ll need the most recent version of R (3.5.1) and RStudio Desktop (1.1.456) installed. We will make of use of RStutio’s new Terminal feature to simplify iteration with participants using different operating systems.
Additionally, you’ll need to install some development software (e.g., compilers and efficient matrix libraries). Follow the instructions here to install Rtools, XCode, and r-base-dev for Windows, Mac OS X, and Linux, respectively. You do not need the latex libraries for our course.
For Mac OS X users, for some reason even when you install the most
recent XCode via the App Store it does not include the most recent
version of the clang
compiler. So you will need to do the additional step of opening a terminal and typing xcode-select --install
then agreeing to questions in the subsequent install dialog windows.
For Windows users, when installing Rtools please keep all suggested install settings with the addition of checking the box “Add rtools to system Path” in the “Select Additional Tasks” dialog window (it is not checked by default).
We will also make use of the the Rmpi
package. An overview and installation instructions for different operating systems can be found here. Getting Rmpi
to work with MPI can be a bit involved on some machines, so you’ll want to get that sorted out before the course.
Course schedule Materials (download all in one zip or tar.gz)
8:30 - 9:00 Set up, welcome, and overview slides
- 9:00 - 9:45 Calling C/C++ and FORTRAN from
R
and fun with OpenMPR
API and OpenMP slides- Exercise 1a
- Markdown doc
- Code zip or tar.gz
- Exercise 1b
- Markdown doc
- Code zip or tar.gz
9:45 - 10:15
Rmpi
for massive parallelization on distributed systems slides10:15 - 10:45 Break
- 10:45 - 11:15
Rmpi
implementation - 11:15 - 12:00 Threaded BLAS and LAPACK and operations with dense covariance matrices
- Computing for mixed effects models with dense covariance matrices slides
- Leveraging your computer’s CPUs using threaded BLAS and LAPACK
- Exercise 3
- Markdown doc
- Code zip or tar.gz