By: SciML

Re-posted from: https://sciml.ai/2019/11/07/ParallelStiff.html

This release covers the completion of another successful summer. We have now
completed a new round of tooling for solving large stiff and sparse differential
equations. Most of this is covered in the exciting….

New Tutorial: Solving Stiff Equations for Advanced Users!

That is right, we now have a new tutorial added to the documentation on
solving stiff differential equations.
This tutorial goes into depth, showing how to use our recent developments to
do things like automatically detect and optimize a solver with respect to
sparsity pattern, or automatically symbolically calculate a Jacobian from a
numerical code. This should serve as a great resource for the advanced users
who want to know how to get started with those finer details like sparsity
patterns and mass matrices.

Automatic Colorization and Optimization for Structured Matrices

As showcased in the tutorial, if you have jac_prototype be a structured matrix,
then the colorvec is automatically computed, meaning that things like
BandedMatrix are now automatically optimized. The default linear solvers make
use of their special methods, meaning that DiffEq has full support for these
structured matrix objects in an optimal manner.

Implicit Extrapolation and Parallel DIRK for Stiff ODEs

At the tail end of the summer, a set of implicit extrapolation methods were
completed. We plan to parallelize these over the next year, seeing what can
happen on small stiff ODEs if parallel W-factorizations are allowed.

Automatic Conversion of Numerical to Symbolic Code with Modelingtoolkitize

This is just really cool and showcased in the new tutorial. If you give us a
function for numerically computing the ODE, we can now automatically convert
said function into a symbolic form in order to compute quantities like the
Jacobia and then build a Julia code for the generated Jacobian. Check out the
new tutorial if you’re curious, because although it sounds crazy… this is
now a standard feature!

GPU-Optimized Sparse (Colored) Automatic and Finite Differentiation

SparseDiffTools.jl and DiffEqDiffTools.jl were made GPU-optimized, meaning that
the stiff ODE solvers now do not have a rate-limiting step at the Jacobian
construction.

DiffEqBiological.jl: Homotopy Continuation

DiffEqBiological got support for automatic bifurcation plot generation by
connecting with HomotopyContinuation.jl. See the new tutorial

Greatly improved delay differential equation solving

David Widmann (@devmotion) greatly improved the delay differential equation
solver’s implicit step handling, along with adding a bunch of tests to show
that it passes the special RADAR5 test suite!

Color Differentiation Integration with Native Julia DE Solvers

The ODEFunction, DDEFunction, SDEFunction, DAEFunction, etc. constructors
now allow you to specify a color vector. This will reduce the number of f
calls required to compute a sparse Jacobian, giving a massive speedup to the
computation of a Jacobian and thus of an implicit differential equation solve.
The color vectors can be computed automatically using the SparseDiffTools.jl
library’s matrix_colors function. Thank JSoC student Langwen Huang
(@huanglangwen) for this contribution.

Improved compile times

Compile times should be majorly improved now thanks to work from David
Widmann (@devmotion) and others.

Next Directions

Our current development is very much driven by the ongoing GSoC/JSoC projects,
which is a good thing because they are outputting some really amazing results!

Here’s some things to look forward to:

• Automated matrix-free finite difference PDE operators
• Jacobian reuse efficiency in Rosenbrock-W methods
• Native Julia fully implicit ODE (DAE) solving in OrdinaryDiffEq.jl
• High Strong Order Methods for Non-Commutative Noise SDEs
• Stochastic delay differential equations

By: Júlio Hoffimann

Re-posted from: https://juliohm.github.io/science/geostats-release/

I’m happy to release GeoStats.jl v.0.10. This release comes with some new exciting features
including:

• Integration with the MLJ.jl framework
• New spatial operations (e.g. partitioning, weighting)
• Faster variography routines

Check the updated user guide for
a tutorial on geostatistical learning.

Julia Computing at the American Conference on Pharmacometrics: Julia Computing presented Pumas at the American Conference on Pharmacometrics (ACoP). Pumas is an artificial intelligence engine for pharmaceutical modeling and simulation in Julia. Julia Computing’s Chris Rackauckas and University of Maryland, Baltimore (UMB)’s Vijay Ivaturi were the winners of the International Society of Pharmacometrics (ISoP) Mathematical and Computational Sciences (MCS) Special Interest Group (SIG) Poster Award for Bayesian-Koopman Techniques for Optimization of Intervention With Respect to Uncertainty in PuMaS.jl.

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Julia Enterprise Users: Enterprise use of Julia continues to grow. Please contact us for more information about Julia Computing enterprise solutions including JuliaSure, JuliaTeam and JuliaRun.

Julia Computing’s Alan Edelman Wins Institute of Electrical and Electronics Engineers (IEEE) Sidney Fernbach Award: Alan Edelman won the Sidney Fernbach Award for “outstanding breakthroughs in high-performance computing, linear algebra, and computational science and for contributions to the Julia programming language.” Alan is co-creator of Julia, co-founder and Chief Scientist at Julia Computing, director of the Julia Lab at MIT and Professor of Applied Mathematics at MIT. Alan will accept the award at SC19 in Denver, Colorado on November 19.

From Tree Leaves to Galaxies: Keno Fischer, Julia Computing co-founder and CTO (Tools) discussed Julia and Julia Computing with Robin.ly. The full video interview is available here.

JuliaTeam on AWS Marketplace: JuliaTeam is available for purchase on Amazon Web Services (AWS) Marketplace. JuliaTeam is an enterprise solution from Julia Computing that makes it easy for enterprise users to install and manage public and private packages, adhere to enterprise governance policies, deploy and scale applications, manage licenses and set up continuous integration.

Julia Day in New York: Julia Computing hosted a successful Julia Day in New York at Google’s New York headquarters on October 7. More than 50 attendees joined a discussion with Julia co-creators and Julia Computing co-founders Viral Shah and Stefan Karpinski, Conning Managing Director David Weiss and Elton Pereira from State Street / BestX.

Julia Computing Webinars: Julia Computing invites you to join two upcoming Webinars. Each Webinar is one hour in length, including question and answer, and led by Julia Computing’s Dr. Matt Bauman. There is no cost to participate.

Webinar	Schedule	Registration
Machine Learning with Julia Dr. Matt Bauman, Julia Computing	Tues Nov 19 12:00 pm – 1:00 pm ET	Click here to register
Private Package Management and Governance with Julia Dr. Matt Bauman, Julia Computing	Thurs Nov 28 12:00 pm – 1:00 pm ET	Click here to register

Other Upcoming Julia Computing Events

Julia Computing will be participating in a number of upcoming conferences. Please contact us if you would like to meet with us at any of these events.

1. Denver: SC19 with Alan Edelman (Julia Computing) Nov 17-22
2. London: Open Data Science Conference (ODSC) with Avik Sengupta (Julia Computing) Nov 19-22
3. Montreal: Node + JS Interactive with Jameson Nash (Julia Computing) Dec 11-12

JuliaCon 2020: JuliaCon 2020 will take place July 27-31 at ISCTE – Instituto Universitário de Lisboa (ISCTE-IUL) in Lisbon, Portugal. Stay tuned for more details.

JuliaBox (Free) Ends – JuliaBox (Paid) Starts at Just $7 per Month for Academic Users: In January, we notified the Julia community that Julia’s growth was making free JuliaBox unsustainable, and that we would sunset the free version of JuliaBox. As a result, the free version of JuliaBox ended on Oct 31, 2019. We are grateful to all JuliaBox users – especially our paid users. If you want to continue using JuliaBox, please sign up for the paid version. For academic users, the cost starts at just $7 per month. If you think your organization, academic department or university should purchase JuliaBox, please contact the decision-maker at your organization, academic department or university, and explain to that person how and why JuliaBox is important to your work. Pricing information is available online, including the 50% academic discount. For other questions about purchasing a paid subscription to JuliaBox, including setting up a new account for your organization and invoicing, please contact us.

Julia and Julia Computing in the News

• InsideHPC: Julia Computing Chief Scientist Alan Edelman Wins Prestigious IEEE Sidney Fernbach Award
• MIT News: Alan Edelman recognized with 2019 IEEE Computer Society Sidney Fernbach Award
• HPCWire: JuliaTeam Available on Amazon Web Services Marketplace
• DevClass: AWS Tempts Open Source Projects with Promo Credit Sweeteners
• HostReview: Which Machine Learning Frameworks To Try In 2019-2020
• SIAM News: Scientific Machine Learning – How Julia Employs Differentiable Programming to Do it Best

Julia Blog Posts

• The Emergent Features of JuliaLang: Part I (Lyndon White)
• High-Performance GPU Computing in the Julia Programming Language (Tim Besard)
• Constraint Solver Part 12: Documentation and Small Changes (Ole Kröger)
• Constraint Solver Part 11: Graph Coloring Part 2 (Ole Kröger)
• Recent Advancements in Differential Equation Solver Software (Chris Rackauckas)
• Checking Bounds Checking in Julia (Vlad Roubtsov)
• A Basic RNN (Michele Pratusevich)
• Constraint Solver Part 10: Graph Coloring Part 1 (Ole Kröger)
• Julia v1.2 (Michele Pratusevich)
• Constraint Solver Part 9: Recap Video (Ole Kröger)
• A Collection of Jacobian Sparsity Acceleration Tools for Julia (Chris Rackauckas)
• Developing Julia Packages (Chris Rackauckas)
• Constraint Solver Part 8: UI Refactoring (Ole Kröger)
• Why I Love Julia (Kolia Sadeghi)
• Ejemplo con Julia (Arturo Erdely)

Upcoming Julia Events

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• Denver: SC19 with Alan Edelman (Julia Computing) Nov 17-22
• London: Open Data Science Conference (ODSC) with Avik Sengupta (Julia Computing) Nov 19-22
• Sydney: Data Science & Analytics using Python, R, Julia + More with Kale Temple and PyData Sydney at Amazon Web Services – Nov 20
• London: Julia – A Fresh Approach to Numerical Computing with Avik Sengtupta (Julia Computing) and Open Source Specialist Group at the British Computer Society London Nov 21
• Berlin: Julia Users Group Fall Workshop with David Higgins and Simon Christ (OpenTechSchool) Nov 23
• Montreal: Node + JS Interactive with Jameson Nash (Julia Computing) Dec 11-12

Recent Julia Events

• Boulder: Julia Meetup with Boulder Data Science, Machine Learning and AI at Galvanize Boulder Oct 2
• Sydney: Data Science & Analytics Using Python, R, Julia + More with PyData Sydney Oct 2
• Cambridge, MA: Hacktoberfest Hack Night with Cambridge Area Julia Users Network at MIT Oct 7
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• Berlin: Julia for Python with Simon Danisch at PyConDE and PyDataBerlin Oct 9-11
• Bucharest: Introduction to Deep Learning with Julia with Bucharest Julia Users Group Oct 9
• Rio de Janeiro: Julia Users in Rio Evento with Rio de Janeiro Julia Meetup at PUC-Rio Oct 10
• Mendoza: Taller de Programación en Julia with Ciencia de Datos en Mendoza Oct 15
• Paris: Meetup with Michael Herbst and François Févotte Oct 17
• Athens: Association for Computing Machinery (ACM) Special Interest Group on Programming Languages (SIGPLAN) Conference on Systems, Programming Languages and Applications: Software for Humanity (SPLASH) with Alan Edelman, Stefan Karpinski and Jeff Bezanson (Julia Computing) Oct 20-25
• Orlando: American Conference on Pharmacometrics (ACoP10) with Viral Shah and Andreas Noack (Julia Computing) Oct 20-23
• San Jose:LLVM Developers Meeting with Jameson Nash (Julia Computing) Oct 22-23
• Dublin: Manipulating DataFrames with Julia and R with Paulito Palmes (IBM Research) Oct 29
• Sydney: Data Science & Analytics Using Python, R, Julia + More with PyData Sydney Nov 5
• Vienna: Multithreading in Julia 1.3 + Editor Tips & Tricks with Rene Donner and Vienna Julia Meetup at WeAreDeveloper Nov 6

Julia Meetup Groups: There are 37 Julia Meetup groups worldwide with 8,725 members. If there’s a Julia Meetup group in your area, we hope you will consider joining, participating and helping to organize events. If there isn’t, we hope you will consider starting one.

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Julia Jobs, Fellowships and Internships

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There are more than 300 Julia jobs currently listed on Indeed.com, including jobs at Accenture, Airbus, Amazon, AstraZeneca, AT&T, Barnes & Noble, BlackRock, Capital One, CBRE, Charles River Analytics, Citigroup, Comcast, Conde Nast, Cooper Tire & Rubber, Disney, Dow Jones, Facebook, Gallup, Genentech, General Electric, Google, Huawei, Ipsos, Johnson & Johnson, KPMG, Lockheed Martin, Match, McKinsey, NBCUniversal, Netflix, Nielsen, Novartis, OKCupid, Opendoor, Oracle, Pandora, Peapod, Pfizer, Raytheon, Spectrum, Wells Fargo, Zillow, Brown, BYU, Caltech, Dartmouth, Emory, Harvard, Johns Hopkins, Louisiana State University, Massachusetts General Hospital, MIT, Penn State, Princeton, UC Davis, University of Chicago, University of Delaware, University of Kentucky, UNC-Chapel Hill, USC, University of Virginia, Argonne National Laboratory, Federal Reserve Bank, Lawrence Berkeley National Laboratory, Los Alamos National Laboratory, National Renewable Energy Laboratory, Oak Ridge National Laboratory, Pacific Northwest National Laboratory, State of Wisconsin and many more.

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About Julia and Julia Computing

Julia is the fastest high performance open source computing language for data, analytics, algorithmic trading, machine learning, artificial intelligence, and other scientific and numeric computing applications. Julia solves the two language problem by combining the ease of use of Python and R with the speed of C++. Julia provides parallel computing capabilities out of the box and unlimited scalability with minimal effort. Julia has been downloaded more than 11 million times and is used at more than 1,500 universities. Julia co-creators are the winners of the 2019 James H. Wilkinson Prize for Numerical Software. Julia has run at petascale on 650,000 cores with 1.3 million threads to analyze over 56 terabytes of data using Cori, one of the ten largest and most powerful supercomputers in the world.

Julia Computing was founded in 2015 by all the creators of Julia to develop products and provide professional services to businesses and researchers using Julia.