JuliaCon 2024: JuliaCon 2024 will take place in Eindhoven, Netherlands July 9-13. Workshops will take place on Tuesday July 9, presentations will take place Wednesday July 10-Friday July 12 and there will be a hackathon on Saturday July 13. Stay tuned for the call for proposals and other information.
JuliaCon Local Eindhoven 2023: Eindhoven will also host the first ever JuliaCon local event on December 1, 2023. Click here for tickets and more information.
JuliaHub Welcomes Brad Carman as Director of Consulting Services: JuliaHub is pleased to welcome Brad Carman as Director of Consulting Services. Brad has over two decades of experience in model-based innovation and design. Please click here for more.
JuliaHub Consulting Services: Would your organization benefit from a 100x increase in simulation speeds? JuliaSim might be the solution you need. Click here for more information about JuliaSim, and to contact us to learn how JuliaSim can help your business succeed.
Free Upcoming Webinars from JuliaHub: JuliaHub provides free Webinars covering a range of Julia topics. The Webinars are free but advance registration is required and space is limited. Please click the links below to register.
Free JuliaHub Webinar Archive: JuliaHub provides free access to more than 70 of our past Webinars. Recent Webinars include:
JuliaHub Policies and Private Registry Solutions: JuliaHub Policies and Private Registry Solutions is a new blog post from Bill Burdick (JuliaHub Senior Software Developer) and Deep Datta (JuliaHub Product Director). It describes new JuliaHub features including Package Analytics and Package Policies.
JuliaHub at American Conference on Pharmacometrics (ACoP): PumasAI, a JuliaHub partner, presented two workshops using JuliaHub at the American Conference on Pharmacometrics (ACoP) this month in National Harbor, MD. Click here to learn more: ACoP14 Showcases Pharmacometric Tools on JuliaHub in November Workshops.
CUDA.jl 5.1: CUDA.jl 5.1 – Unified Memory and Cooperative Groups is a new blog post in which Dr. Tim Besard (JuliaHub Software Engineer) explains new features and benefits available using CUDA.jl 5.1. Click here to learn more.
New Podcast Episode with Dr. Chris Rackauckas, JuliaHub VP Modeling and Simulation: Dr. Chris Rackauckas, JuliaHub VP Modeling and Simulation, discusses Computational Chemistry with Catalyst in a 30 minute podcast episode. “Chris break[s] down the ambitions and insights of his paper, and spill[s] the beans on how Catalyst is shaking up the status quo in chemical modeling…. Chris guide[s] us through Catalyst’s synergy with other Julia packages, crafting a comprehensive toolkit for researchers. And because we all love a good teaser, Chrisl share[s] a glimpse into the horizon for Catalyst’s evolution.”
Generalizing Scientific Machine Learning and Differentiable Simulation Beyond Continuous Models: Dr. Chris Rackauckas, JuliaHub VP Modeling and Simulation, presented Generalizing Scientific Machine Learning and Differentiable Simulation Beyond Continuous Models. This one-hour seminar is available for free online. This seminar is part of a data-driven physical simulation series at Lawrence Livermore National Laboratory.
JuliaHub v6.3.0: JuliaHub v6.3.0 is now available with a number of new features. Release notes are available here and below.
- v6.3.0 adds the capability to build a sysimage to go along with your job run. The sysimage is built before the job starts. After the sysimage build completes the sysimage is mounted to every Julia process the job utilizes (main and workers). JuliaHub users can choose to create a SYSIMG by checking the “Build SYSIMG” checkbox during job submission. More information on “What a SYSIMG is?” can be found in following link: https://julialang.github.io/PackageCompiler.jl/dev/sysimages.html#sysimages
- We now have simple caching based on the pre-built SYSIMG’s, this feature ensures that additional runs of a job with the same manifest will reuse the already built sysimage.
- We’re excited to introduce a fresh and modern user interface for JuliaHub. This update brings a host of usability improvements and a cleaner design, making user interactions smoother and ensures faster loading. UI improvements includes a major overhaul to Notifications, Registrator and Projects features.
- We have added a new grouping for shared datasets for easy distinction, using these groupings; end-users can easily distinguish between shared datasets based on who shared them; without going into details.
- Users can now create folders or directories in File explorer UI, this will help users to organize there files in an efficient way
- We now have a new search filter for dependencies & dependents in the packages UI, using this feature, end-users can now search for direct and indirect dependencies & dependents for a particular package.
- You can now remove a project viewer’s access by setting the resource’s general access level to “No Access”
Enterprise
- Job time limits can now be made optional by an admin on enterprise installs, if this option is enabled, end-users on the JuliaHub instance can start jobs with no time limits.
Applications
- Julia version has been updated to v1.9.2 in Julia IDE and batch jobs
- We have added following R packages to WindowsWorkstation app:- ggplot, ggPMX, xpose, xpose4 and vpc
- End-users can now access JuliaHub Datasets through RStudio app
- R package “units” is now part of the RStudio app
- End-Users can now install TinyTex packages in RStudio server and all the newly installed TinyTex packages will automatically go to persistent storage, hence, these packages can be loaded across the sessions without installing them again.
JuliaSim v0.30.0: JuliaSim v0.30.0 is now available with new capabilities and features. Release notes are available here and below. If interested in running JuliaSim on juliahub.com or locally, please do contact us.
Features
- Accept environment path for info
- Drop PDESurrogates from JuliaSim
- Upgrade to Julia v1.9.3
- When creating new Pluto notebooks on JuliaHub with JuliaSim, the cells necessary to make a notebook work with JuliaSim are automatically added to the notebook
JuliaSimBatteries
- Add support for time- and state-varying experimental control inputs
- Reduce the package compilation times
- Improve documentation landing page and model comparisons.
JuliaSimControl
- Improve fixed step integrator providing additional features
- Improve documentation structure, including video tutorials
- Reduce complexity of various APIs
JuliaSimModelOptimizer
- Add support for multiple models in the same InverseProblem
- Add support for Prediction Error Method which is very useful for unstable systems
- Improvements in performance, stability and correctness of Collocation Methods
- Overall stability in the API for generic use cases
Julia for Differential Equations Using Graphics Processing Units (GPUs): Automated Translation and Accelerated Solving of Differential Equations on Multiple GPU Platforms is a new paper co-authored by JuliaHub’s Yingbo Ma (Modeling & Numerics Team Lead), Tim Besard (Software Engineer), Alan Edelman (Co-Founder and Chief Scientist) and Chris Rackauckas (VP Modeling & Simulation). Click here for more.
Julia for Solving Inverse Problems: A Tutorial on the Bayesian Statistical Approach to Inverse Problems is a new paper from 3 Oregon State researchers using Turing.jl’s No-U-Turn Sampler (NUTS), a Markov Chain Monte Carlo (MCMC) algorithm to solve inverse problems.
Julia for Fish: A Bayesian Inverse Approach to Identify and Quantify Organisms from Fisheries Acoustic Data is a new paper in the ICES Journal of Marine Science. Researchers from the National Oceanic and Atmospheric Administration (NOAA) Alaska Fisheries Science Center and the University of Washington Applied Physics Lab use sonar with ProbabilisticEchoInversion.jl to identify and count fish. According to co-author Sam Urmy: “This work was inspired by Celeste.jl, and would not be possible without Julia’s automatic differentiation capabilities and the Turing.jl ecosystem.”
Julia for Epidemics and Spin Dynamics: Matrix Product Belief Propagation for Reweighted Stochastic Dynamics Over Graphs is a new paper in the Proceedings of the Natural Academy of Sciences using Julia for two applications: “inference of infection probabilities from sparse observations within the SIRS epidemic model and the computation of both typical observables and large deviations of several kinetic Ising models.” Click here for the paper and source code.
Julia for Net-Zero Hydrogen Production: A Cost Comparison of Various Hourly-Reliable and Net-Zero Hydrogen Production Pathways in the United States is a new paper in Nature Communications. The authors “build a model that enables direct comparison of the cost of producing net-zero, hourly-reliable hydrogen from various pathways … For the United States (California, Texas, and New York), model results indicate next-decade hybrid electricity-based solutions are lower cost ($2.02-$2.88/kg) than fossil-based pathways with natural gas leakage greater than 4% ($2.73-$5.94/kg). These results also apply to regions outside of the U.S. with a similar climate and electric grid.” Furthermore, the authors “use the Gurobi Linear Optimizer in the Julia mathematical programming tool (JuMP) to size system components such that we minimize the LCOH from electricity-based pathways.”
Why Jahan.ai Uses Julia: Why We Use Julia in Our AI Startup is a new blog post from Jahan.ai, an AI startup. According to Jahan.ai, “Handling enormous data for retail giants, including forecasts for over 20 million series in near real-time, demands a robust, efficient, and cost-effective language. Julia fits this bill perfectly with its minimal latency and high-performance capabilities. It allows us to scale effortlessly, processing vast datasets quickly thanks to efficient memory usage and parallel processing. This speed is crucial in an industry where trends shift rapidly. Julia’s just-in-time (JIT) compilation and performance, rivaling that of C, enables us to update forecasts multiple times a day. Its unique blend of speed and interpretability, without sacrificing power, helps our clients to react proactively to the ever-changing market demands.”
Oak Ridge National Laboratory (ORNL) Researchers Win SC23 Best Paper Award Using Julia for Exascale Supercomputing: Julia as a Unifying End-to-End Workflow Language on the Frontier Exascale System is a new paper from Oak Ridge National Laboratory (ORNL) scientists using Frontier, the first exascale supercomputer. The authors received the Best Paper award at the SC23 WORKS Workshop. Click here to learn more.
Great Lakes Consulting: Great Lakes Consulting (GLC) is a JuliaHub partner. They use Julia to help solve their customers’ problems. Their Julia blog describes a number of Julia features and capabilities.
Free Compute on JuliaHub (20 hours): In addition to the features JuliaHub has always offered for free – Julia ecosystem search, package registration tools, a dedicated package server – the platform now also gives every user 20 hours of free compute. This allows people to seamlessly share Pluto notebooks and IDE projects with others and let them get their feet wet with computing without having to open up their wallets. Click here to get started or check out Deep Datta’s introductory video, “JuliaHub Is a Free Platform to Start Your Technical Computing Journey”, where he explains how and why to start using JuliaHub for cloud computing.
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? JuliaHub 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 JuliaHub and our partners to get your team up and running quickly. For more information, please contact us.
Careers at JuliaHub: JuliaHub is a fast-growing tech company with fully remote employees in 20 countries on 6 continents. Click here to learn more about exciting careers and internships with JuliaHub.
Julia and JuliaHub in the News
- Automation: The Last Word – The Evolving Language of Automation Engineering
- AIP Publishing: A Tutorial on the Bayesian Statistical Approach to Inverse Problems
- Onrec: Top 10 AI Skills You Need to Land Your Dream Job in 2024
- I-Programmer: Practical Julia: A Hands-On Introduction for Scientific Minds (No Starch)
- Finextra: From RAG to Riches in a GenAI World: Some Jargon Explainers & Current Trends
- Jax: Programing with Chapel: Making the Power of Parallelism and Supercomputers More Accessible
- The Global Recruiter: AI Skills Required
- Proceedings of the National Academy of Sciences (PNAS): Matrix Product Belief Propagation for Reweighted Stochastic Dynamics Over Graphs
- ICES Journal of Marine Sciences: A Bayesian Inverse Approach to Identify and Quantify Organisms from Fisheries Acoustic Data
- HGPU: Julia as a Unifying End-to-End Workflow Language on the Frontier Exascale System
- Nature Communications: A Cost Comparison of Various Hourly-Reliable and Net-Zero Hydrogen Production Pathways in the United States
- Science Direct: Automated Translation and Accelerated Solving of Differential Equations on Multiple GPU Platforms
Julia Blog Posts
Upcoming Julia and JuliaHub Events
Recent Julia and JuliaHub Events
Contact Us: Please contact us if you want to:
- Learn more about JuliaHub, JuliaSim, Pumas, PumasQSP or CedarEDA
- Obtain pricing for Julia consulting projects for your organization
- Schedule Julia training for your organization
- Share information about exciting new Julia case studies or use cases
- Spread the word about an upcoming online or offline event involving Julia
- Partner with JuliaHub to organize a Julia event online or offline
- Submit a Julia internship, fellowship or job posting
About JuliaHub and Julia
JuliaHub is a fast and easy-to-use code-to-cloud platform that accelerates the development and deployment of Julia programs. JuliaHub users include some of the most innovative companies in a range of industries including pharmaceuticals, automotive, energy, manufacturing, and semiconductor design and manufacture.
Julia is a high performance open source programming language that powers computationally demanding applications in modeling and simulation, drug development, design of multi-physical systems, electronic design automation, big data analytics, scientific machine learning and artificial intelligence. 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 prestigious James H. Wilkinson Prize for Numerical Software and the Sidney Fernbach Award.
