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Circuitscape is an open-source program that uses circuit theory to model connectivityin heterogeneous landscapes. Its most common applications include modeling movement and gene flowof plants and animals, as well as identifying areas important for connectivity conservation.

Circuitscape has now been rewritten inJulia for better performance and scalability.This work is based on the Python implemention inCircuitscape.py.

The new Circuitscape is built entirely in the Julia language, a newprogramming language for technical computing. Julia is built from theground up to befast. As such, this offers anumber of advantages over the previous version, and these are detailed below.

We benchmarkedCircuitscape.jl (v0.1.0) with the Python version (v4.0.5) to obtain thefollowing results. We started up Circuitscape with 16 parallel processes,and used benchmark problems from the standard Circuitscapebenchmark suite.

These benchmarks were run on a Linux (Ubuntu) server machine with the following specs:

• Name: Intel(R) Xeon(R) Silver 4114 CPU
• Clock Speed: 2.20GHz
• Number of cores: 20
• RAM: 384 GB

From the benchmark, we see that the new version is upto4x fasteron 16 processes. However, the best performing bar in the chart isJulia-CHOLMOD, which is a new feature introduced.

Julia-CHOLMOD is a new solver mode used in the new Circuitscape. It performs acholeskydecomposition on the graphconstructed, and performs a batched back substitutionto compute the voltages. It plugs into theCHOLMOD library,which is part of the SuiteSparse collection of high performance sparsematrix algorithms.

To use the this new mode, include a line in your CircuitscapeINI file:

solver = cholmod

The cholesky decomposition is a direct solver method, unlike the algebraicmultigrid method used by default in both the old and the new version.The advantage with this new direct method is that it can be much faster thanthe iterative solution, within a particular problem size.

Word of caution: The cholesky decomposition is not practicalto use beyond a certain problem size because of phenomenon calledfill-in, which results in loss of sparsity and large memory consumption.

The old Circuitscape had limited support for parallelism, which worked on Mac andLinux, but didn't work on Windows.

Julia as a programming language is built from the ground up to be parallel,and as a result the new Circuitscape natively supports parallelism on all threeplatforms.

The new Circuitscape introduces the ability to run problems insingle precision as opposed to the standard double precision.

Single precision usually takes much less memory, but trades offagainst solution accuracy.

Use this new feature by including a line in your config file:

precision = single

1. You will need toinstall Julia on your system first.

2. Once you start Julia, install Circuitscape by:

julia>using Pkgjulia> Pkg.add("Circuitscape")

If you want the latest development version, you can additionally do:

julia> Pkg.add(PackageSpec(name="Circuitscape", rev="master"))

Check if all the tests are passing by doing the following:

julia> Pkg.test("Circuitscape")

The current interface to Circuitscape is through the Julia terminal.

julia>using Circuitscape# loads the package into your environmentjulia>compute("path/to/config/file.ini")

If you have encounter any issues or would like to ask a question, please filea reporthere.Contributions in the form ofpull requests are also welcome!

Circuitscape takes as input INI files, which contain paths to the raster map, sources, grounds,and other inputs, as well as flags for each run. If you're using theGUIthe INI file will automatically be generated for you and then fed into Circuitscape. But if you'reusing the Julia prompt, then you must write one yourself. The easiest way to do this is to copyan INI filefrom the tests and then modify it depending on your problem.

A preprint is available here:https://proceedings.juliacon.org/papers/10.21105/jcon.00058. You can also use the following BibTeX entry to cite this package:

@article{Anantharaman2020,doi ={10.21105/jcon.00058},url ={https://doi.org/10.21105/jcon.00058},year ={2020},publisher ={The Open Journal},volume ={1},number ={1},pages ={58},author ={Ranjan Anantharaman and Kimberly Hall and Viral B. Shah and Alan Edelman},title ={Circuitscape in Julia: High Performance Connectivity Modelling to Support Conservation Decisions},journal ={Proceedings of the JuliaCon Conferences}}