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Tetrahedra

Steve Kelly sjkelly

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Tetrahedra
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View threadingpush.jl
using Base.Threads
function long_computation()
a = rand()
b = rand()
c = rand()
d = rand()
max(log(sqrt(a^6*b^6)), log(sqrt(c^6*d^6)))
end
@sjkelly
sjkelly / generate_project.jl
Created Jun 23, 2019
Generate Project.toml Julia (for old Pkg)
View generate_project.jl
# https://discourse.julialang.org/t/convert-require-to-project-toml-and-manifest-toml/17775/2
"""
generate_project_toml([name::String])
Generate Project.toml file for the existing current project at `\$PWD`.
It activates the generated Project.toml and then adds packages based on
REQUIRE file.
"""
View Tetrahedra-based Volumetric Meshes.txt
Below I collected relevant links and papers more or less pertaining to the subject of tetrahedral meshes.
It's an ever-growing list.
------------------------------
Relevant links:
http://en.wikipedia.org/wiki/Types_of_mesh
http://en.wikipedia.org/wiki/Tetrahedron
http://en.wikipedia.org/wiki/Simplicial_complex
View gist:4b0e7e23b9d295126976

#[JULIA] Faster than CGAL Delaunay

####TL;DR

DelaunayJL is an incremental 2D Delaunay triangulation algorithm implemented in Julia, it is robust and ~20% faster than CGAL, the C++ de-facto industry standard. And it's MIT Licensed! all links to code are below

####Show me the data!

The figure below shows how much time it takes to run a benchmark on my computer, an Intel Core i7-4800MQ CPU @ 2.7Ghz, 16GB RAM, Win8 64bit. The benchmark consists of inserting a number of points uniformly distributed. The benchmark is run 5 times for each number of points once for CGAL and once for Julia. The numbers of points used are 10K, 100K, 1M, and 10M. CGAL v4.4 was compiled with VS2013 64bit release mode, Julia is of version 0.3.0 Commit 7681878 (2014-08-20 20:43 UTC) x86_64-w64-mingw32 the delaunay code is here (see other gists of mine for complementing files... I'll compile this all into a library when I have the time)

View Fast, robust geometrical predicates
# GPREDv0.1.3
#
# Fast, robust 2D and 3D geometrical predicates on generic point types.
# Implementation follows algorithms described in http://arxiv.org/abs/0901.4107
# and used (for e.g.) in the Illustris Simulation
# http://www.illustris-project.org/
#
# Author: Ariel Keselman (skariel@gmail.com)
# License: MIT
# Bug reportss welcome!
@sjkelly
sjkelly / term_grepped.txt
Last active Aug 29, 2015
Julia Build #8215
View term_grepped.txt
This file has been truncated, but you can view the full file.
steve:~$ cd Software/julia/
steve:~/Software/julia(master)$
steve:~/Software/julia(master)$
steve:~/Software/julia(master)$ git pull origin master
remote: Counting objects: 275, done.
remote: Compressing objects: 100% (182/182), done.
remote: Total 275 (delta 148), reused 152 (delta 92)
Receiving objects: 100% (275/275), 507.99 KiB | 925.00 KiB/s, done.
Resolving deltas: 100% (148/148), done.
View julia #8093
julia> Base.runtests()
From worker 2: * linalg1
From worker 3: * linalg2
From worker 2: * linalg3
From worker 2: * linalg4
From worker 2: * core
From worker 2: * keywordargs
From worker 2: * numbers
From worker 3: * strings
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