EA poster: Parallel Places
Introducing Parallel Programming in Geographical Information Science
Shannon Kobs Nawotniak
Department of Geosciences
Idaho State University
Pocatello, USA
kobsshan@isu.edu
Abstract—Two Geographic Information Science (GIS) courses
at Idaho State University participated in the 2014-2015
NSF/TCPP Curriculum Guidelines Early Adopter program for
Parallel and Distributed Computing (PDC). Students with little
or no background in programming learned to use PDC to solve
spatial problems in science. Student anxiety and programming
language restrictions resulted in challenges that we were
largely able to overcome. Initial evaluations from students and
a reviewer are overwhelmingly positive.
GIS; parallel; python; fortran90; MPI; openMP, early
adoption; education
I.
INTRODUCTION
Parallel and Distributed Computing (PDC) has become
increasingly fundamental to large data processing required
in Geographic Information Science (GIS), including LiDAR
point cloud and hyperspectral raster processing. However,
PDC computing courses have remained largely beyond the
reach of GIS students, who often come from backgrounds in
Geography, Geology, History, Anthropology, etc., and
typically have no programming experience. In the Idaho
State University GIS minor and graduate programs, we have
designed a 2-course sequence to introduce PDC as part of
the NSF/TCPP Curriculum Guidelines Early Adopter
program. Classes relied strongly on in-class live coding and
outside of class efforts at individualized assistance by the
instructor and TA (funded by the Early Adopter program).
II.
geodatabases of road data and processing volcanic eruption
model outputs for time-slice visualization in ArcGIS (Fig.
1). Instead of an assigned textbook, students use online
resources.
Programming for GIS included 10 students: 3
anthropologists, 3 geologists, 2 engineers, 2 environmental
scientists/ecologists; 6 women, 4 men; 9 graduate, 1
undergraduate. Only one entering student reported
confidence and experience programming in any language.
B. Advanced GIS Programming (GEOL 6628)
The second course, Advanced GIS Programming takes a
more traditional approach to PDC and uses Fortran90 with
MPI and openMP. The students use shared and distributed
memory systems to solve spatial problems beyond what
they can accomplish using ArcGIS tools and libraries. This
includes geothermal anomaly detection (Fig. 2) [1] and
simple landscape evolution using topographic diffusion.
This course uses Pacheco [2] as a text despite its
introductory level, recognizing the hybridized nature of a
graduate level course aimed at novice programmers.
GEOL6628 started at 9 students but dropped to 7 by
midterms due to personal conflicts and overscheduling (no
withdrawals were related to failing grades). The class was
comprised of 3 geologists, 2 anthropologists, 2
environmental science/ecologists, 1 engineer, and 1
mathematician, with 5 women and 4 men. All of the
students reported experience in python, though
THE COURSES
A. Programming for GIS (GEOL 4428/5528)
The first course in the sequence, Programming for GIS,
serves as a first introduction to code development and
parallelism. Students learn python and how to manipulate
the arcpy library for ArcGIS, industry standard software that
they are already familiar with and expect to use
professionally. The students use pool and multiprocessing to
parallelize functions operating on a list of inputs, measure
their speedup and efficiency, and discuss to differences
between shared and distributed memory. Parallel tasks
center around spatial data manipulation, such as exploring
Fig. 1. Example of a time-slice of ash concentration in a volcanic eruption
plume, displayed as part of a sequence in ArcGIS. This was processed as
part of a parallelism exercise in GEOL 4428/5528 to demonstrate use of
python/arcpy parallelism for processing and visualizing a series of files.
only the 6 who continued from GEOL5528 the previous
semester reported experience with parallelism.
III.
CHALLENGES
A. Fear/Anxiety
The first challenge we encountered is the high level of
discomfort many students brought with them to
programming. We tried to offset this by emphasizing
individual assistance outside of class and creating
coffee/cookie breaks during class to promote a relaxed
atmosphere while we discussed conceptual problems.
Students manipulated a LitleFe cluster [3] to demystify
computer hardware. We suspect that technology fears may
have been exacerbated by grade-related anxiety;
participating students were operating outside of their
comfort zones and expected to maintain grades consistent
with graduate level study (B or above). We do not suggest
adopting “soft” grading, but are considering approaches by
which to extend deadlines or allow students to earn points
back without creating unreasonable additional effort on the
part of the professor and TA.
B. Languages
The second major challenge is the switch in languages
that occurs midway through our sequence. Due to employer
expectations for programming in GIS, we are compelled to
continue teaching python for ArcGIS in the course
sequence. This presents a challenge because:

Parallel python is not well-posed for integration
with ArcGIS via arcpy. File locking mechanisms
embedded within standard arcpy library tools
hindered parallelization. As a result, students often
were unable to achieve real speedup on ArcGISintegrated problems.
 Language transition from python to Fortran90
resulted in a slow start in the spring semester.
Several weeks at the beginning of the second
semester were necessary to orient students to
syntax differences, arrays, and various conceptual
differences in language style.
We found an unexpected benefit to these challenges,
however. The slowness of arcpy and its challenges to
parallelization resulted in student frustration with the
limitations of standard software. This strongly motivated
them to enroll in the advanced course; enrollment in GEOL
6628 reached record levels this term. The enforced delay
during the language transition also coincided with a
necessary transition from Windows (required for ArcGIS) to
Linux command line and highlighted lingering
misunderstandings regarding programming logic.
IV.
EVALUATION AND CONCLUSIONS
External evaluation of GEOL 4428/5528 by the ISU GIS
coordinator noted instructor accessibility to students during
Fig. 2. Geothermal anomaly detection in the East African Rift, done using
fortran90 with openMP. ArcGIS was unable to handle the input data.
and after class time; extensive incorporation of local GIS
datasets in order to increase the students’ sense of
familiarity with the problems despite the new approaches;
and frequent use of analogies to illustrate complex
programming concepts. Students anonymously evaluated
the course as very good to excellent, citing in-class coding
and a supportive atmosphere as course strengths. More
tellingly, enrollment rates have already increased in the GIS
programming sequence.
GEOL 6628, the advanced course, is being taught in the
Spring 2015 semester; evaluations are not yet available.
Intended term projects are ambitious and include
collaboration with Idaho National Lab researchers.
Based on available feedback, we consider our early
adoption of the NSF/TCPP Curriculum Guidelines for PDC
education a success. We plan to continue developing and
refining content that will enhance GIS education through
PDC.
ACKNOWLEDGMENT
We thank the NSF/TCPP Curriculum Guidelines Early
Adopter Program for their support; the ISU Department of
Geosciences for allowing us to pursue this work outside of
traditional geology/GIS education; and Meghan Fisher for
her work as the TA in these courses.
REFERENCES
[1]
[2]
[3]
S. Karki, S.K. Nawotniak, H.C. Bottenberg, M. McCurry, J. Welhan,
“Determination of Geothermal Anomalies through Multivariate
Regression of Background Variables at Yellowstone National Park
using Landsat 5 TM Thermal Band Data” Geothermal Resources
Council Transactions, vol 38, pp. 503-510, 2014.
P.S. Pacheco, An Introduction to Parallel Programming, Morgan
Kaufmann: Burlington, MA, 2011.
Littlefe.net