Newsletter December 2021 - Julia Computing Launches Cedar Electronic Design Automation (EDA)

Julia Computing Launches Cedar Electronic Design Automation (EDA): Analog circuit design hasn’t changed much since the 1980s when analog designers were pushing buttons and turning knobs in a GUI. Cedar Electronic Design Automation (EDA) from Julia Computing addresses the modern needs of analog circuit designers for better tools to create optimal and robust designs in today’s complex technology nodes. CedarSim is the first analog circuit simulator that is fully differentiable and CedarWaves makes it easy for analog designers to write custom measurements so that specs can be reliably measured and automatically checked. Cedar users can increase productivity dramatically using thousands of high quality open source tools to generate models that leverage data, optimization, machine learning, big data, trend analysis, data sheet generation and more. To book a consultation, please visit Cedar-EDA.com.

JuliaHub on the Red Carpet: If you are a Formula One racing fan, you may have seen the JuliaHub logo on the Williams F1 car at the Hungarian Grand Prix. Film fans may also have noticed the JuliaHub logo at the Dec 6 Leicester Square (London, UK) movie premiere for The King’s Man. Look for the JuliaHub logo in the photo below.

Julia 1.7: Julia 1.7 is now available. Click here to download. Julia 1.7 Highlights include:

• New RNG (reproducible RNG in tasks)

• New threading capabilities

• Package manager

• Better path printing for standard libraries in errors

• Inference improvements

• Libblastrampoline + MKL.jl

• Escaping newlines inside strings

• Multidimensional array literals

• Property destructuring

• Support for Apple Silicon

The release of Julia 1.7 received coverage in English, German, French, Japanese, Czech and Portuguese media.

• Analytics India: Programming Language Julia Releases Version 1.7

• Market Research Telecast: Programming Language: Julia 1.7 Expands Multithreading Capabilities

• SD Times: Julia 1.7 Released

• InfoQ: Julia 1.7 Extends its Threading Capabilities, Improves Type Inference, and More

• I Programmer: Julia 1.7 - Better Performance On Several Fronts

• Heise: Programmiersprache: Julia 1.7 Baut die Multithreading-Fähigkeiten Aus

• Developpez: La Version 1.7 du Langage Julia Est Disponible, Elle Apporte l'Installation Automatique de Paquets

• CodeZine: プログラミング言語「Julia 1.7」がリリース

• DevClass: Julia 1.7 Makes It Across the Finish Line

• Root: Vyšla Julia 1.7

• SempreUpdate: Linguagem de Programação Julia 1.7 Liberada com Recursos Aprimorados de Segmentação

DTable: Krystian Guliński has published a new blog post on DTable, a new distributed table implementation built using Dagger.jl. More information is available here.

SciML’s Stance on Long-Term Support (LTS): The SciML community has published a new blog post explaining SciML’s Stance on Long-Term Support (LTS). The blog post is available here.

New Blog Posts from Logan Kilpatrick, Julia Community Manager: Logan Kilpatrick, Julia Community Manager, has published several new blog posts that may be especially useful for new Julia users or those seeking to persuade others to try Julia out.

• Why You Should Invest in Julia Now, as a Data Scientist

• Why You Should Learn Julia, as a Beginner / First-Time Programmer

• So You Want To Do Google Summer of Code with the Julia Language

• The Most Underrated Feature of the Julia Programming Language, the Package Manager

Free Webinar from Julia Computing: Register today to participate in a free one hour Webinar from Julia Computing.

Julia Used to Analyze Black Holes: Lawrence Livermore National Laboratory scientists Brendan Keith, Akshay Khadse and Scott Field published Learning Orbital Dynamics of Binary Black Hole Systems from Gravitational Wave Measurements in Physical Review Research. They used DiffEqFlux in Julia to “introduce a gravitational waveform inversion strategy that discovers mechanical models of binary black hole (BBH) systems.” More information is available from Phys.org. The paper is available here and the code can be found here.

Type Stability in Julia: Wondering how Julia compilers optimize so much more than Python and R? Type Stability in Julia: Avoiding Performance Pathologies in JIT Compilation helps to explain.

Julia Programming - A Hands-On Tutorial: Martin Maas has published Julia Programming: A Hands-on Tutorial which includes introductory material about Julia, focusing on its use in science and engineering.

Differentiable Programming in Julia for Earth System Modeling: DJ4Earth is a project from researchers at MIT (Alan Edelman, Chris Rackauckas, Christopher Hill), UT Austin (Karen Willcox, Patrick Heimbach), Dartmouth (Mathieu Morlighem), U Chicago (Michel Schanen, Sri Hari Krishna Narayanan) and CU Boulder (Nora Loose). DJ4Earth enables differentiable programming in scientific machine learning for Earth system modeling.

Animated Unicode Plots with Julia: David Neuzerling has published Animated Unicode Plots with Julia using UnicodePlots.jl.

Connectivity and Hydrology Modeling with Julia: SpatialGraphs.jl builds on Circuitscape.jl to “establish types and constructors for spacially-referenced graphs to enable connectivity and hydrology modeling in Julia.” More information is available here.

FluxML Partners with NumFOCUS: FluxML announced a new partnership with NumFOCUS to “further the cause of open and reproducible science and [grow] the adoption of the FluxML ecosystem. Flux has always had the mission of being a simple, hackable and performant approach to machine learning, which is extended to a number of domains in science by means of differentiable programming.” FluxML is a core part of the Julia scientific machine learning (SciML) ecosystem and NumFOCUS is a longtime sponsor and supporter of Julia and other open source projects.

A Tour of High-Frequency Finance Via the Julia Language and QuestDB: Dean Markwick “has written an excellent tutorial that provides an introduction to high frequency time series using the Julia programming language and covers commonly-used techniques for constructing trading models of financial assets.” Click here for more information.

Numerical Linear Algebra with Julia: Eric Darve and Mary Wooters have published Numerical Linear Algebra with Julia. The book is available from SIAM or Google Books.

Bayesian Statistics with Julia in Japanese: Suyama Atsushi has published Bayesian Statistics with Julia in Japanese.

Introduction to Data Science Using Julia: Karthikeyan A K has updated Introduction to Data Science - Learn Julia Programming, Math & Datascience from Scratch to include Struct, Predictive and Descriptive Analysis and Machine Learning, Artificial Intelligence and Data Science.

Compressing Atmospheric Data Using Julia: Nature Computational Science has published Compressing Atmospheric Data into Its Real Information Content by Milan Klöwer, Miha Razinger, Juan J. Dominguez, Peter D. Düben and Tim N. Palmer. “The software that was developed for this study is available in the published Julia packages BitInformation.jl (v0.2), LinLogQuantization.jl (v0.2) and ZfpCompression.jl (v0.2).”

Julia Computing’s Keno Fischer “Daydreaming of Tangent Bundles” at NeurIPS Conference: Keno Fischer (Julia Computing) presented Daydreaming of Tangent Bundles (or Implementation Concerns for Higher Order Forward-Mode AD or Forward-Mode AD in Diffractor.jl) as part of the differentiable programming workshop at the NeurIPS Conference. The presentation is available here.

Careers at Julia Computing: Julia Computing is a fast-growing tech company with fully remote employees in 11 countries on 4 continents. Click the links below to learn more about exciting careers and internships with Julia Computing.

• Cloud Engineer

• EDA Core System & Compiler Engineer

• Julia Application Developer

• Security Engineer

• Director of Marketing

• Product Manager

• Sales Engineer

• Semiconductor EDA Intern

Please click here for more information and to apply.

Pumas - Enhanced and In the Cloud: Pumas-AI has launched an enhanced version of Pumas that is readily accessible in the cloud. Pumas is the revolutionary advanced healthcare analytics platform that facilitates quantitative capabilities across the drug development cycle. Designed from the ground up in Julia, Pumas allows users to scale, integrate and accelerate their quantitative scientific activities all under one umbrella. Pumas is a product of Pumas-AI and deployed through the JuliaHub platform from Julia Computing to leverage JuliaHub's ease of use and scalability. Julia Computing is a technology partner and exclusive reseller of Pumas. Click here for more information.

JuliaHub from Julia Computing: JuliaHub is the entry point for all things Julia: explore the ecosystem, build packages and deploy a supercomputer at the click of a button. JuliaHub also allows you to develop Julia applications interactively using a browser-based IDE or by using the Pluto notebook environment and then scale workloads to thousands of cores . Version 5 features a brand new user interface, reduced app startup latency, and many more usability enhancements. JuliaHub is the easiest way to start developing in Julia or share your work using dashboards and notebooks.

More information is available in these two presentations from Dr. Matt Bauman (Julia Computing):

• JuliaHub - The Best Way to Run Large Scale Computing in the Cloud

• GUI Front Ends for Julia Programs on JuliaHub

JuliaSim: JuliaSim is a next generation cloud-based modeling and simulation platform, combining the latest techniques from scientific machine learning with equation-based digital twin modeling and simulation. More information about JuliaSim is available in this presentation from Dr. Chris Rackauckas.

Converting from Proprietary Software to Julia: Are you looking to leverage Julia’s superior speed and ease of use, but limited due to legacy software and code? Julia Computing and our partners can help accelerate replacing your existing proprietary applications, improve performance, reduce development time, augment or replace existing systems and provide an extended trusted team to deliver Julia solutions. Leverage experienced resources from Julia Computing and our partners to get your team up and running quickly. For more information, please contact us.

Julia and Julia Computing in the News

• Analytics India: Programming Language Julia Releases Version 1.7

• Market Research Telecast: Programming Language: Julia 1.7 Expands Multithreading Capabilities

• SD Times: Julia 1.7 Released

• InfoQ: Julia 1.7 Extends its Threading Capabilities, Improves Type Inference, and More

• I Programmer: Julia 1.7 - Better Performance On Several Fronts

• Heise: Programmiersprache: Julia 1.7 Baut die Multithreading-Fähigkeiten Aus

• Developpez: La Version 1.7 du Langage Julia Est Disponible, Elle Apporte l'Installation Automatique de Paquets

• CodeZine: プログラミング言語「Julia 1.7」がリリース

• DevClass: Julia 1.7 Makes It Across the Finish Line

• Root: Vyšla Julia 1.7

• SempreUpdate: Linguagem de Programação Julia 1.7 Liberada com Recursos Aprimorados de Segmentação

• Analytics India: MLDS 2022: Major Highlights From Previous Years

Julia Blog Posts

• DTable – An Early Performance Assessment of a New Distributed Table Implementation (Krystian Guliński)

• Julia 1.7 Highlights (Jeff Bezanson, Jameson Nash, Ian Butterworth, Kristoffer Carlsson, Shuhei Kadowaki, Elliot Saba, Viral B Shah, Mosè Giordano, Simeon Schaub, Nicholas Bauer, Keno Fischer)

• SciML's Stance on Long Term Support (LTS)

• New Features in DataFrames.jl 1.3: Part 1 (Bogumił Kamiński)

• Choosing How to Store Your Strings (Bogumił Kamiński)

• Clip Your Data with ClipData.jl (Bogumił Kamiński)

• Welcome to DataFramesMeta.jl (Bogumił Kamiński)

• One Thousand and One Stories (Bogumił Kamiński)

• Mutability, Scope, and Separation of Concerns in Library Code (Mathieu Besançon)

• Linear Mixed Effect Models (Oisín Fitzgerald)

• Plotting in Julia with Makie (DSB)

• Analyzing Graphs with Julia (DSB)

• Why You Should Invest in Julia Now, as a Data Scientist (Logan Kilpatrick)

• Why You Should Learn Julia, as a Beginner / First-Time Programmer (Logan Kilpatrick)

• So You Want To Do Google Summer of Code with the Julia Language (Logan Kilpatrick)

• The Most Underrated Feature of the Julia Programming Language, the Package Manager (Logan Kilpatrick)

• Animated Unicode Plots with Julia (David Neuzerling)

• Artificial Neural Networks as Universal Function Approximators (Julien Pascal)

• A Tour of High-Frequency Finance via the Julia Language and QuestDB (Dean Markwick)

• Julia Programming: A Hands-on Tutorial (Martin Maas)

Recent Papers Featuring Julia Computing Authors

• Differential Methods for Assessing Sensitivity in Biological Models (Mester R, Landeros A, Rackauckas C, Lange K)

• Physics-Enhanced Deep Surrogates for PDEs (Raphaël Pestourie, Youssef Mroueh, Chris Rackauckas, Payel Das, Steven G. Johnson)

• Generalized Physics-Informed Learning Through Language-Wide Differentiable Programming (Christopher Rackauckas, Alan Edelman, Keno Fischer, Mike Innes, Elliot Saba, Viral B Shah, Will Tebbutt)

• Platooning for Improved Safety and Efficiency of Semi-Trucks (PISES–III) (Venkatasubramanian Viswanathan, Varun Shankar, Gavin D Portwood, Arvind T Mohan, Peetak P Mitra, Dilip Krishnamurthy, Christopher Rackauckas, Lucas A Wilson, David P Schmidt)

• Implications of Delayed Reopening in Controlling the COVID-19 Surge in Southern and West-Central USA (Raj Dandekar, Emma Wang, George Barbastathis, Chris Rackauckas)

• Validation and Parameterization of a Novel Physics-Constrained Neural Dynamics Model Applied to Turbulent Fluid Flow (Varun Shankar, Gavin D. Portwood, Arvind T. Mohan, Peetak P. Mitra, Dilip Krishnamurthy, Christopher Rackauckas, Lucas A. Wilson, David P. Schmidt, and Venkatasubramanian Viswanathan)

• AbstractDifferentiation. jl: Backend-Agnostic Differentiable Programming in Julia (Frank Schäfer, Mohamed Tarek, Lyndon White, Chris Rackauckas)

• Julia for Biologists (Elisabeth Roesch, Joe G Greener, Adam L MacLean, Huda Nassar, Christopher Rackauckas, Timothy E Holy, Michael PH Stumpf)

• Composing Modeling and Simulation with Machine Learning in Julia (Chris Rackauckas, Ranjan Anantharaman, Alan Edelman, Shashi Gowda, Maja Gwozdz, Anand Jain, Chris Laughman, Yingbo Ma, Francesco Martinuzzi, Avik Pal, Utkarsh Rajput, Elliot Saba, Viral B Shah)

• Stiff Neural Ordinary Differential Equations (Suyong Kim, Weiqi Ji, Sili Deng, Yingbo Ma, Christopher Rackauckas)

• A Comparison of Automatic Differentiation and Continuous Sensitivity Analysis for Derivatives of Differential Equation Solutions (Yingbo Ma, Vaibhav Dixit, Michael J Innes, Xingjian Guo, Chris Rackauckas)

• A Campus Simulation Model Supporting the Safe Blues Experiment (Thomas Graham, Azam Asanjarani, Shane G. Henderson, Yoni Nazarathy, Keng Chew, Marijn Jansen, Christopher Rackauckas, Kirsty Short, Peter G Taylor, Aapeli Vuorinen, Ilze Ziedins)

• Stably Accelerating Stiff Quantitative Systems Pharmacology Models: Continuous-Time Echo State Networks as Implicit Machine Learning (Ranjan Anantharaman, Anas Abdelrehim, Anand Jain, Avik Pal, Danny Sharp, Utkarsh Utkarsh, Christopher Vincent Rackauckas)

• Hybrid Mechanistic + Neural Model of Laboratory Helicopter (Christopher Rackauckas, Roshan Sharma, Bernt Lie)

• ModelingToolkit: A Composable Graph Transformation System for Equation-Based Modeling (Yingbo Ma, Shashi Gowda, Ranjan Anantharaman, Chris Laughman, Viral Shah, Chris Rackauckas)

• High-Performance Symbolic-Numerics via Multiple Dispatch (Shashi Gowda, Yingbo Ma, Alessandro Cheli, Maja Gwozdz, Viral B Shah, Alan Edelman, Christopher Rackauckas)

• Collocation Based Training of Neural Ordinary Differential Equations (Elisabeth Roesch, Christopher Rackauckas, Michael PH Stumpf)

• Accelerating Simulation of Stiff Nonlinear Systems using Continuous-Time Echo State Networks (Ranjan Anantharaman, Yingbo Ma, Shashi Gowda, Chris Laughman, Viral Shah, Alan Edelman, Chris Rackauckas)

• Safe Blues: The Case for Virtual Safe Virus Spread in the Long-Term Fight Against Epidemics (Raj Dandekar, Shane G Henderson, Hermanus M Jansen, Joshua McDonald, Sarat Moka, Yoni Nazarathy, Christopher Rackauckas, Peter G Taylor, Aapeli Vuorinen)

• Opening the Blackbox: Accelerating Neural Differential Equations by Regularizing Internal Solver Heuristics (Avik Pal, Yingbo Ma, Viral Shah, Christopher Rackauckas)

• NeuralPDE: Automating Physics-Informed Neural Networks (PINNs) with Error Approximations (Kirill Zubov, Zoe McCarthy, Yingbo Ma, Francesco Calisto, Valerio Pagliarino, Simone Azeglio, Luca Bottero, Emmanuel Luján, Valentin Sulzer, Ashutosh Bharambe, Nand Vinchhi, Kaushik Balakrishnan, Devesh Upadhyay, Chris Rackauckas)

• Efficient Precision Dosing Under Estimated Uncertainties via Koopman Expectations of Bayesian Posteriors with Pumas (Christopher Vincent Rackauckas, Vaibhav Dixit, Adam R Gerlach, Vijay Ivaturi)

• Forecasting Virus Outbreaks with Social Media Data via Neural Ordinary Differential Equations (Matías Núñez, Nadia Barreiro, Rafael Barrio, Christopher Rackauckas)

Upcoming Julia Events

• Virtual Meetup: Julia Meetup with Boulder Data Science, Machine Learning and AI Dec 16

• Webinar: Building Kubernetes Integrated Julia Applications with Kuber.jl with Tanmay Mohapatra (Julia Computing) Dec 21

• Virtual Meetup: Coffee Meeting (Online) with Julia Gender Inclusive Jan 16

• Virtual Meetup: Coffee Meeting (Online) with Julia Gender Inclusive Feb 27

• Virtual Meetup: Coffee Meeting (Online) with Julia Gender Inclusive Apr 12

• Virtual Meetup: Coffee Meeting (Online) with Julia Gender Inclusive May 29

Recent Julia Online Events

• Virtual Conference: American Conference on Pharmacometrics with Julia Computing and Pumas-AI Nov 8-12

• Virtual Conference: PackagingCon with Julia Computing Nov 9-10

• Virtual Meetup: Monte Carlo Tree Search with Boulder Data Science, Machine Learning and AI Nov 11

• Webinar: TXA PKPD in Pregnant Women: Model-Informed Dose Optimization with Pumas-AI Nov 15

• Webinar: Performance Benchmarking in Julia with Jameson Nash (Julia Computing) Nov 30

• San Francisco: Design Automation Conference with Julia Computing Dec 5-9

• Virtual Conference: NeurIPS Conference with Keno Fischer (Julia Computing) Dec 6-14

Contact Us: Please contact us if you wish to:

• Purchase or obtain license information for products such as JuliaHub, JuliaSim or Pumas

• Obtain pricing for Julia consulting projects for your organization

• Schedule online Julia training for your organization

• Share information about exciting new Julia case studies or use cases

• Spread the word about an upcoming online event involving Julia

• Partner with Julia Computing to organize a Julia event online

• Submit a Julia internship, fellowship or job posting

About Julia Computing and Julia

Julia Computing's mission is to develop products that bring Julia's superpowers to its customers. Julia Computing's flagship product is JuliaHub, a secure, software-as-a-service platform for developing Julia programs, deploying them, and scaling to thousands of nodes. It provides the power of a supercomputer at the fingertips of every data scientist and engineer. In addition to data science workflows, JuliaHub also provides access to cutting-edge products such as Pumas for pharmaceutical modeling and simulation, JuliaSim for multi-physics modeling and simulation, and Cedar for electronic circuit simulation, combining traditional simulation with modern SciML approaches.

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 by users at more than 10,000 companies 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 and the 2019 Sidney Fernbach Award. 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.