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README.md

Barnes2016-ParallelFlowAccum

Title of Manuscript: Parallel Non-divergent Flow Accumulation For Trillion Cell Digital Elevation Models On Desktops Or Clusters

Authors: Richard Barnes

Corresponding Author: Richard Barnes (richard.barnes@berkeley.edu)

DOI Number of Manuscript: 10.1016/j.envsoft.2017.02.022

Code Repositories

This repository contains a reference implementation of the algorithms presented in the manuscript above, along with information on acquiring the various datasets used, and code to perform correctness tests.

Abstract

Continent-scale datasets challenge hydrological algorithms for processing digital elevation models. Flow accumulation is an important input for many such algorithms; here, I parallelize its calculation. The new algorithm works on one or many cores, or multiple machines, and can take advantage of large memories or cope with small ones. Unlike previous algorithms, the new algorithm guarantees a fixed number of memory access and communication events per raster cell. In testing, the new algorithm ran faster and used fewer resources than previous algorithms exhibiting ∼30% strong and weak scaling efficiencies up to 48 cores and linear scaling across datasets ranging over three orders of magnitude. The largest dataset tested has two trillion (2 · 10^12) cells. With 48 cores, processing required 24 minutes wall-time (14.5 compute-hours). This test is three orders of magnitude larger than any previously performed in the literature. Complete, well-commented source code and correctness tests are available for download from Github.

Compilation

After cloning this repo you must acquire RichDEM by running:

git submodule init
git submodule update

You must also obtain certain prerequisites:

sudo apt install make openmpi-bin libgdal-dev libopenmpi-dev

If you wish (as I did) to compile the code on XSEDE, certain modules must be loaded:

module load intel/2015.2.164
module load mvapich2_ib

To compile the programs run:

make

The result is a program called parallel_d8_accum.exe.

Running the above compiles the program to run the cache strategy. Using make compile_with_compression will enable the cacheC strategy instead. This strategy is not compiled by default because it requires the Boost Iostreams library. This libary can be installed with:

sudo apt-get install libboost-iostreams-dev

Further details

For further details on testing, layout files, and so on, please see the source code's README.md.

MPI Profiling

Although the program tracks its total communication load internally, I have also used mpiP to profile the code's communication. The code can be downloaded here and compiled with:

./configure --with-binutils-dir=/usr/lib
make shared
make install #Installs to a subdirectory of mpiP

Prerequisites include: binutils-dev.

mpiP can be used to profile any MPI program without the need to compile it with the program. To do so, run the following line immediately before launching mpirun:

export LD_PRELOAD=path/to/libmpiP.so

Although the program tracks its maximum memory requirements internally, I have also used /usr/bin/time to record this. An example of such an invocation is:

mpirun -output-filename timing -n 4 /usr/bin/time -v ./parallel_d8_accum.exe one @offloadall dem.tif outroot -w 500 -h 500

This will store memory and timing information in files beginning with the stem timing.

RichDEM

This code is part of the RichDEM codebase, which includes state of the art algorithms for quickly performing hydrologic calculations on raster digital elevation models. The full codebase is available at https://github.com/r-barnes