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Newsletter March 2022 - JuliaSim: Simulating Reality

8 March 2022 | Julia Computing

JuliaSim - Simulating Reality: Julia Computing has released a new 16 minute video introducing JuliaSim. This new video includes the following:

  • Introduction to Julia Computing

  • How JuliaSim Solves Industrial Modeling Challenges

  • Real-World Success with JuliaSim

  • Example Model

  • Launching JuliaSim’s Functional Mock Up (FMU) Accelerator

  • Surrogatizing the Example Model

  • Analyzing the Surrogate Model via Diagnostic Dashboard

  • Workflow Integration Review

  • JuliaSim as a Fully-Featured Simulation Platform

  • Cost vs. Benefit

‘Why We Created Julia’ Turns Ten Years Old: The Julia community celebrated the 10th anniversary of ‘Why We Created Julia’, the announcement that introduced Julia to the world on February 14, 2012. This celebration includes a new retrospective, ‘Why We Use Julia, Ten Years Later’. One hundred Julia users from fields as diverse as data science, finance, robotics, pharmaceuticals, neuroscience, entomology, quantum physics, astronomy, environmental science and energy each contributed a short paragraph (or more) about how they came to use Julia, why they use Julia to replace Python, C, C++, Fortran, MATLAB, SAS and R, what they have accomplished using Julia and why they continue to love the language.

Highlights include:

  • “Come for the speed, stay for the community.” Elliot Saba

  • “The code was so clean and nice compared to the horrible Python version! … Julia could easily run 50x faster than almost identical code in Python. From then on I was hooked.” David Sanders

  • “I stumbled into Julia while trying to do a crazy data transformation in R that was taking forever. I was instantaneously hooked! The syntax is so easy …” Jose Storopoli

  • “I am tremendously grateful to have come across the language, as well as its community and all the wonderful people that have helped me on this journey. It is not an exaggeration to say that much of my career successes I owe to the language.” George Datseris

  • “I remember working on an R script that needed to loop through 33 million rows of data, doing a complicated … computation that would take 18 hours to run. Literally during one of these 18 hour runs, I saw the Julia announcement post and was immediately desperate for the kind of simple performance it promised. I read the initial manual over a weekend, rewrote my script the following Monday morning, and it ran in 5 minutes. I thought for sure I had made some kind of early termination mistake, but no, it was really just that fast.” Jacob Quinn

  • “Best programming decision I ever made :)” Simon Danisch

  • “I would not be surprised if, within the next 10 years, Julia is dubbed ‘the language of science’.” Ranjan Anantharaman

  • “Using Julia is one of the best choices I made concerning my numerics and code development.” Ronny Bergmann

  • “Without the Julia community it is unlikely I would have completed my PhD (and I certainly wouldn't have my current job).” Frames Catherine White

  • “This language is awesome.” Tobias Knopp

  • “I was hooked. Almost 3 years later I've found that Julia has been my go-to language for nearly everything. The ease of use, speed and community keep bringing me back for more.” Matt Brzezinkski

  • “Julia is the language I was longing for.” Cristóvão Sousa

  • “In 2020, I switched my work code to Julia, and have never looked back since.” Jakob Nissen

  • “Julia is the perfect language for scientific computing and beyond.” Mark Kittisopikul

  • “The performance is awesome and amazing!” Qingyu Qu

SciMLCon 2022: The first annual SciMLCon takes place online March 23. Participation is free but registration is required. Click here for more information and to register.

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

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

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

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

Webinar Presenter Length of Webinar Date Time Registration Link Cost
Single Instruction, Multiple Data (SIMD) in Julia Kristoffer Carlsson, Julia Computing 1 hour Thu Mar 24 12 noon - 1 pm Eastern (US) Register Free
Performance Optimization of Graphics Processing Unit (GPU) Applications in Julia Dr. Tim Besard, Julia Computing 1 hour Thu Mar 31 12 noon - 1 pm Eastern (US) Register Free

Free Julia Computing Webinars Available Online: Nearly 40 free Webinars from Julia Computing are available to watch online, including Accelerating Functional Mock Up (FMU) Models with Jacob Vaverka and End-to-End Machine Learning Workflow Using Julia with Jeremie Bouchard. These Webinars highlight how companies are using JuliaHub. Click here to watch.

Inside Big Data Features Julia Computing CEO and Co-Founder Dr. Viral Shah: Inside Big Data published How Cloud Computing is Helping to Solve 3 Problems on Everyone’s Mind Right Now by Julia Computing CEO and Co-Founder Dr. Viral Shah. Dr. Shah describes how Julia is used in the cloud to help analyze the spread of COVID-19, develop safe pharmaceutical treatments and route school buses.

Using Julia With Intel’s oneAPI: Intel’s oneAPI is an open ecosystem for developing performant cross-architecture applications for CPUs, GPUs, and other accelerators. Open-source implementations of Julia are now available using oneAPI. Robert Cohn is a Senior Principal Engineer at Intel Development Tools Software. He explains: “Beyond C++, Intel and the community are enabling open-source implementations of Python, Julia, and Java for oneAPI. Intel developed oneAPI accelerated, drop-in replacements for the very popular NumPy, pandas, and scikit-learn packages and enabled writing accelerator kernels directly in Python with Numba. You can write accelerator kernels using Julia thanks to Julia Computing, and in Java through the efforts of University of Manchester’s Advanced Processor Technologies group. The languages all sit on top of the Level Zero runtime. Level Zero provides services for loading and executing programs, allocating memory, etc. Looking forward, we are leveraging our experiences with Python, Julia, and Java to provide better language runtime support in Level Zero.” More information is available here.

Julia Billboard Outside San Jose, California Airport: If you’ve driven near the San Jose, CA airport recently, you may have noticed a familiar logo on a billboard. Thanks to Logan Kilpatrick, Julia Community Manager, for capturing this photo.

New Julia Blog Posts from Logan Kilpatrick, Julia Community Manager: Julia Community Manager Logan Kilpatrick published two new blog posts:

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.

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.

Metaprogramming in Julia: Neuroscientist Federico Claudi (Sainsbury Wellcome Centre) notes, “Metaprogramming in @julialanguage is really powerful!” Click here for more information.

Julia Gender Inclusive Presents Learn Julia With Us Workshop Series: Kyla McConnell and Julia Müller launched the Learn Julia With Us workshop series. This series of free online workshops begins with Getting Started with Julia.

Mesoscale Eddy Fluxes in Julia: Navid Constantinou presented A Data-Driven Approach for Developing and Calibrating a Parameterization for Mesoscale Eddy Fluxes at the Association for the Sciences of Limnology and Oceanography (ASLO) Ocean Sciences Meeting 2022. Click here to watch.

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

10,000 Julia Questions on Stack Overflow: The Julia community is celebrating 10,000 Julia questions on Stack Overflow. “We just crossed 10,000 #JuliaLang questions on @StackOverflow ?! A huge shoutout goes to all those who ask and answer questions there. A special s/o to: Bogumił Kamiński and Przemyslaw Szufel for dedicating so much time to answering questions. They account for 20% of answers ?!”

Julia and Julia Computing in the News

  • HPCWire: Julia Computing Celebrates 10 Years with Retrospective

  • Inside Big Data: How Cloud Computing is Helping to Solve 3 Problems on Everyone’s Mind Right Now: COVID-19 Spread, Pharmaceutical Safety, and School Bus Routing

  • Analytics India: Ten Years of Julia: A Timeline

  • Analytics Insight: Top 10 Coding Apps that Beginners Should Try at Least Once

  • ZDNet: How to Become an AI Engineer

  • Analytics India: How to Use XGBoost for Time-Series Analysis

  • Analytics India: How to Build a Data Science Portfolio in College

  • ZDNet: Developer Jobs and Programming Languages: What's Hot and What's Next

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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.

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