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Newsletter April 2022 - 2021 Turing Award Winner Jack Dongarra Says Julia Is ‘Much Better’ Than Other Languages and Should Perhaps Take Over

By Andrew Claster | Apr 08, 2022

2021 Turing Award Winner Jack Dongarra Says Julia Is ‘Much Better’ Than Other Languages and Should Perhaps Take Over: The winner of the 2021 Turing Award, often referred to as the ‘Nobel Prize of Computing’, is Jack Dongarra. Dongarra says Julia is ‘much better’ than other languages and should perhaps take over. According to ZDNet:

“While hardware speeds up matrix multiplication, [Jack] Dongarra is, again, mindful of the needs of the scientists and the software writer. ‘I grew up writing FORTRAN, and today we have much better mechanisms’ such as the Julia programming language and Jupyter Notebooks. What's needed now, he said, are more ways to ‘express those computations in an easy way,’ meaning linear algebra computations such as matrix multiplications. Specifically, more tools are needed to abstract the details. ‘Making the scientist more productive is the right way to go,’ he said. Asked what software programming paradigm should perhaps take over, Dongarra suggested the Julia language is one good candidate …”

SciMLCon 2022: The first annual SciMLCon was a tremendous success, with more than 16 thousand views on YouTube so far. Individual SciMLCon presentations are available here, including the State of SciML by Chris Rackauckas, Julia Computing Director of Modeling and Simulation. The full 8.5 hour conference is available here.

Differentiable Programming in Julia Wins Best Poster Award at Neural Information Processing Systems (NeurIPS) Conference: Julia Computing’s Chris Rackauckas and co-authors Frank Schaefer (University of Basel), Mohamed Tarek (Pumas-AI, University of New South Wales) and Lyndon White (Invenia) were awarded Best Poster at the Neural Information Processing Systems (NeurIPS) Conference for AbstractDifferentiation.jl: Backend-Agnostic Differentiable Programming in Julia. Click here for more.

JuliaHub Logo Featured in Netflix TV Show Formula 1: Drive to Survive: If you’ve been watching Formula 1: Drive to Survive on Netflix, you may have noticed a familiar logo on the Williams team racecar. Click here to watch.

Julia Computing and Its Flagship Product JuliaHub Are Now SOC 2 Type 1 Compliant: Many Julia Computing enterprise customers have asked for more robust security systems and processes around our products. We are happy to announce that Julia Computing is now compliant with System and Organization Controls (SOC) 2 Type 1 as defined by the American Institute of Certified Public Accountants (AICPA). This means that all of our product architecture and development activities and processes are SOC 2 Type 1 compliant. We will continue to invest heavily in security and we have a road map to become more and more robust. Contact Julia Computing for more information.

JuMP 1.0 Released: “Nearly 10 years in the making, the release of JuMP 1.0 represents a major milestone in the history of JuMP. JuMP is a modeling language and collection of supporting packages for mathematical optimization problems in Julia. JuMP makes it easy to formulate and solve a range of problem classes, including linear programs, integer programs, conic programs, semidefinite programs, and constrained nonlinear programs. JuMP is one of the 10 most popular Julia packages, and it has tens of thousands of users in countries worldwide, with over 50 thousand downloads in the last six months.” More information, including the release announcement, release notes, documentation and tutorials, is available at jump.dev.

JuliaCon 2022: JuliaCon 2022 will be free and online July 27-29.

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

JuliaSim: Model Predictive Control (MPC) and Proportional-Integral-Derivative (PID) Control

  • When: Wed April 27, 12 noon - 1 pm Eastern (US)

  • Presenter: Dr. Fredrik Bagge Carlson (Julia Computing)

  • Registration: Link

JuliaSim is the fastest, most efficient and most productive modeling and simulation platform, combining the latest scientific machine learning (SciML) techniques with equation-based digital twin modeling and simulation. JuliaSim is cloud-based and can accelerate modeling and simulation 500x or more. Click here to register. This Webinar will include control design for a multiple-input, multiple-output (MIMO) system with constraints on both states and inputs. We will design progressively more advanced controllers, starting with autotuning a Proportional-Integral-Derivative (PID) controller and culminating with nonlinear Model Predictive Control (MPC). The Webinar is led by Julia Computing's Dr. Fredrik Bagge Carlson. Fredrik received his MSc and Ph.D. from Dept. Automatic Control at Lund University. He has a background in robotics and an interest in developing software tools for control, identification, and simulation, which he is pursuing as part of the simulation team at Julia Computing.

Watch Recent Free Webinars from Julia Computing: There are currently 40 free Julia Computing Webinars available online. Click here to watch.

Recent Webinars from Julia Computing include:

Single Instruction, Multiple Data (SIMD) in Julia: SIMD (Single Instruction, Multiple Data) is when the processor executes the same instruction (like addition) on multiple numbers (data) in one instruction. Recent processor architectures come with the capability of running these SIMD instructions on even larger batches of data, making it quite important to make sure that SIMD is used when possible for best performance. Julia offers many ways to take advantage of SIMD. Sometimes it happens automatically, as an optimization, but it is also possible to manually write SIMD code. This workshop provides an overview of the different ways you can use SIMD in Julia.

Performance Optimization of Graphics Processing Unit (GPU) Applications in Julia: This webinar focuses on how to analyze and improve the performance of Compute Unified Device Architecture (CUDA) GPU applications implemented in Julia. This includes the use of CUDA profiler tools, how to effectively use them with a Julia codebase, as well as Julia-specific optimization guidelines.

Julia Computing at Advanced Research Projects Agency-Energy (ARPA-E) Innovation Summit: Julia Computing will attend and present at the Advanced Research Projects Agency-Energy (ARPA-E) Innovation Summit in Denver, Colorado May 23-25. The presentation will demonstrate the use of artificial intelligence and machine learning for heating, ventilation and air conditioning (HVAC) modeling and simulation using Julia Computing’s JuliaSim platform.

Julia Used to Identify COVID Nanobodies in Immunized Alpacas: Multivariate Mining of an Alpaca Immune Repertoire Identifies Potent Cross-Neutralizing SARS-CoV-2 Nanobodies has been published in ScienceAdvances. The article and code are available online.

Landscape Corridors Using Julia: Julia Computing’s Andreas Noack and co-authors published Accelerating Advances in Landscape Connectivity Modeling with the ConScape Library, demonstrating that the combination of ‘precise spatial data (e.g. high-resolution imagery from remote sensing)’ with ‘Julia’s just-in-time compiler, efficient algorithms and landmarks to reduce computational load’ allow ConScape to compute landscape ecological metrics … for large landscapes.’ Click here for more information.

Talk Julia Podcast: The Talk Julia weekly podcast continued with several new episodes:

  • Episode 10: Deep Neural Networks in Julia with Flux.jl

  • Episode 11: Setting Up VS Code for Julia

  • Episode 12: Easy Input and Beautiful Output With Julia

  • Episode 13: Serving the Julia Community With Logan Kilpatrick

  • Episode 14: Making New Math With Julia and Christy.jl

Learn Julia with Us - Your First Julia Code: Julia Gender Inclusive continues the Learn Julia with Us series with hosts Kyla McConnell and Julia Müller.

Logan Kilpatrick, Julia Language Community Developer Advocate

Intro to Artificial Neural Networks Using Flux.jl: Learn how to build a pure Julia artificial neural network model that can recognize handwritten digits from the MNIST data set. Flux.jl is a package written in pure Julia that provides building blocks and utilities so users can easily create custom Deep Learning models.

Nonlinear Dynamics: George Datseris and Ulrich Parlitz have written Nonlinear Dynamics, ‘a concise introduction interlaced with code’ from the Undergraduate Lecture Notes in Physics book series. The book ‘includes numerous executable code snippets referring to open source Julia software packages.’

Julia for Data Analysis: Bogumił Kamiński has written a new book titled Julia for Data Analysis. Readers will learn to:

  • Read and write data in various formats

  • Work with tabular data, including subsetting, grouping, and transforming

  • Visualize data using plots

  • Perform statistical analysis

  • Build predictive models

  • Create complex data processing pipelines

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.

Sales

Human Resources

Engineering

Product and Program Management

Internships

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):

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 here.

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

  • ZDNet: Jack Dongarra, Who Made Supercomputers Usable, Awarded 2021 ACM Turing Prize

  • Telegraph India: IIT Kanpur’s 3-Day Tech-Fest Techkriti’22 Concludes on a High Note

  • India Education Diary: IIT Kanpur’s Techkriti’22 Explored New Horizons In Technology And Business Eco-System With The Presence Of Nobel Laureates

  • SportsPro: Formula One 2022 Commercial Guide - Every Team, Every Sponsor, All the Major TV Deals

  • Analytics India: Julia’s JuMP 1.0 Released: What’s New?

  • Analytics India: A Guide to Orchest for Building ML Pipelines

  • Analytics Insight: Top 5 Deep Learning Frameworks that Techies Should Learn in 2022

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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.

SIMD in Julia

This video will give an overview of the different ways you can use SIMD in Julia.

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