And I’d like people to also think a little bit about systems like Julia and Jupyter notebooks as really the interface to the computers, rather than thinking about programming and languages based on things like C/C++ or Fortran. So really, I’m going to be advocating for a much higher level of abstraction, which is not to say that some of us won’t still be programming at a much lower level.” (Katherine Yelick, UC Berkeley and Lawrence Berkeley National Laboratory)

Sanofi has licensed technology from Julia Computing and others.

Julia ... is another specialized language that is specifically designed for computations and numerical analysis. Although purpose-built, Julia provides versatility and supports both parallel and distributed computing, and is incredibly fast. The primary feature of Julia is fast performance; hence, it is perfect for data visualization, numerical analysis, deep learning, or interactive computing.

Famous cases include Julia Computing and Pfizer jointly simulating new drugs, and AZ to create toxicity prediction AI, in addition to solving compliance issues with Aviva, a major European insurance company, and creating ML security solutions with Cisco. Recently, Julia has also expanded into the semiconductor field.

JuMP is an open-source modeling language that allows users to express various optimization problems in high-level algebraic syntax. It takes the help of the advanced features of Julia to offer exceptional functionality and achieve an at-par performance with the commercial modeling tools available.

Julia is another popular language that is in rising demand. It is a multi-purpose programming language that is created for numerical analysis and for scientific computing. And due to this very reason, many high-profile businesses are focusing on time-series analysis, space mission planning, and risk analysis. Even though Julia is a dynamically typed language, it is capable of being used as a low-level programming language if needed.

[Pumas-AI] developed its software through a partnership with Julia Computing, an MIT spinout which seeks to bring products to the world that use the Julia programming language and recently raised $24 million. It effectively makes the startup an entry point for healthcare into using Julia.

JuliaSim is a cloud-based simulation platform built on top of the Julia open source stack, including SciML and ModelingToolkit, which we explored in depth here and here, respectively. These were just the base of JuliaSim, which aims to change the way the industry does modeling and simulation with powerful acceleration and integration within a complete ecosystem.

Viral Shah is CEO of Julia Computing, a cloud-software company that makes tools for programmers who build AI and related systems. His customers range from universities working on better batteries for electric vehicles to pharmaceutical companies searching for new drugs.

The latest funding will help scale Julia Computing’s production of Julia solutions and new product development for pharmaceuticals, energy, finance, and other sectors.

Julia Computing raised $24 million in a funding round led by venture capital firm Dorilton Ventures and ... Bob Muglia, former chief of software provider Snowflake Inc, [will] join the computing solutions company's board.

The Julia Computing team has rocked this world by building JuliaHub, a modern platform for technical and scientific modeling, he added. JuliaHub is poised to advance scientific computing and enable solutions that will deliver new generations of products and services that we cannot even imagine today.

More than 10,000 companies across the globe use the language, including AstraZeneca PLC (AZN.L), BlackRock (BLK.N) and Microsoft Corp (MSFT.O), Julia Computing said. NASA, the Federal Aviation Administration (FAA) and the Federal Reserve Bank of New York also use Julia.

Julia is the fourth language in the world which has been run on supercomputers to achieve petaflops performance. That is why it is being used e.g. for the next generation climate models, by e.g. CliMA as well as for astronomy projects like Celeste.

The new version of Julia makes good progress toward eliminating some of the latency that led to these complaints. It significantly reduces pre-compilation time, is faster at downloading package files, and reduces the frequency of run-time JIT compilation delays. Due to the factors discussed above, latency in Julia can never be completely eliminated, but the current version creates a noticeably snappier interactive experience. Pre-compilation times in 1.6 are reduced mainly through the use of all available cores to carry out concurrent compilation of modules. Therefore, the more cores available, the greater the reduction in pre-compilation latency (to a point, of course).

Julia’s main characteristic is that it was built from the ground up for high-performance programs that demand faster data processing, such as machine learning and scientific computing.

Julia and Spectrum Instrumentation make a perfect fit as the Spectrum products are ideal for acquiring or generating the fast electronic signals found in AI applications or robotics. The company offers the most extensive range of digitizers for the acquisition of analogue or digital signals, in the DC to GHz frequency range, with high precision and dynamic range.

DataFrames.jl provides a set of tools for working with tabular data in Julia. Its design and functionality are similar to those of pandas (in Python) and data.frame, data.table and dplyr (in R), making it a great general purpose data science tool, especially for those coming to Julia from R or Python. DataFrames.jl plays a central role in the Julia Data ecosystem, and has tight integrations with a range of different libraries.

In a project with MIT Professor Raffaele Ferrari and research scientist Andre Souza, MIT junior Adeline Hillier is exploring how deep learning solutions can be used to improve or replace physical models of the uppermost layer of ocean, which drives the rate of heat and carbon uptake. In the course of the project, Hillier learned how to code in the programming language Julia. She also got a crash course in fluid dynamics. “You’re trying to model the effects of turbulent dynamics in the ocean,” she says. “It helps to know what the processes and physics behind them look like.”

One of the most critical features of Julia is extensibility. Meaning, the users can add new methods to the previously defined functions and use the previously defined methods on new types. Sometimes, these new entities force this language to recompile code to account for the changes in dispatch. In version 1.6, the scheme for invalidating old code has been made more accurate and selective. The outcome is a faster version of Julia, far more resilient to method invalidation, more responsive, and agile in interactive sessions.

JuliaSim is a next-generation cloud-based modelling and simulation platform that combines the latest techniques from scientific machine learning with equation-based digital twin modelling and simulation. The modern ML-based techniques accelerate simulation by up to 500x, changing the paradigm of what is possible with computational design.

In version 1.6, Julia and its standard libraries have been given “a thorough makeover” to help type inference arrive at a concrete answer more often, according to the Julia team. The result is said to be a leaner, faster Julia that is less prone to method invalidation, and feels considerably more responsive and nimble in interactive sessions.

As far as new customer wins, we learned … Julia Computing has been awarded funding by the US Defense Advanced Research Projects Agency (DARPA) to accelerate simulation of Analog and Mixed-Signal circuit models using state of the art machine learning and artificial intelligence techniques.

Julia is one of the high-performance languages of choice for data science, artificial intelligence, and modelling and simulation applications. According to sources, the sophisticated designs of the Boston-based quantum computing startup stretch the boundaries of traditional simulation tooling, making a significant acceleration in simulation performance all the more crucial. Julia Computing intends to make these capabilities available to the larger industry in the near future.

“Julia paid off for us better than they would have imagined,” Dr. Schneider admits.

Especially in the context of Julia, differentiable programming capabilities are enabling new science. Approaches such Physics Informed Neural Networks, Universal Differential Equations, Echo State Networks and much more are available through the Julia SciML organization and work effortlessly within the Julia ecosystem

Julia is a high-level, efficient, and dynamic programming language. While it is a general-purpose language and can be used to write any application, many of its features are well-suited for numerical computing that’s required by AI.

I would be happier if Julia was the main language [for AI]. Python’s starting to have type declarations now but they don’t quite take them seriously. Julia does a much better job of that and Julia was written to be more efficient from the start.

"The home energy management system we proposed utilizes a classical model predictive control algorithm that derives optimal flows of a building under the assumption of perfect information," Langer said. "Due to the rolling horizon implementation of the system in Julia JuMP, we are able to analyze a whole year with a time resolution of 1h in just a couple of seconds."

I based my assessment on Julia’s unique combination of convenient syntax with uncompromising performance. At the time, although Julia was still in pre-1.0 status, there was already plenty of excited chatter. Julia seemed to have solved the “two-language problem”—a conundrum often facing Python programmers, as well as users of other expressive, interpreted languages

What that means is Pumas 1.0 takes the data a drug company has and simulates the success rate or possible outcome of a drug. Creating models and simulations through the algorithms in the code will allow researchers to manage risk in drug development.

[Julia] researchers have published a paper showing the use of universal differential equations for COVID-19 epidemiology.

In a well-attended keynote, presented by Viral B. Shah, Shah explained how Julia will become the language of the future and how differentiable programming helps in accomplishing complex computational programs.

Julia is a popular language for machine learning thanks to simplicity. Developers can accomplish more in Julia with fewer lines of code, Bakshi said, and write an app entirely in that language -- no need to write components in C, for example.

Caltechs’ Climate Modeling Alliance recently announced that Julia would be the language for the next round of its multimillion dollar climate model.

[One] of the distinct features of Julia is the parametric polymorphism, which is dynamic and can facilitate multiple dispatches. Hence, it is an AI programming language that collects garbage, evaluates, and dynamic libraries are included to float calculations, linear algebra, number generation, and express matching.

Applied mathematics Professor Alan Edelman has been selected to receive the 2019 IEEE Computer Society Sidney Fernbach Award. Edelman was cited “for outstanding breakthroughs in high performance computing, linear algebra, and computational science and for contributions to the Julia programming language.”

Julia continues to bring novel technologies to computational scientists. One of its most exciting new capabilities is differentiable programming (∂P).

Pumas is a copyrighted, comprehensive platform based on the Julia programming language that contains multiple modules designed to meet the needs of analysts in the pharmaceutical industry, while also working to advance therapeutic innovation in the clinic setting.

[Julia] supports parallelism out of the box, offering three main levels of parallelism which are categorized as Julia coroutines (green threading), multi-threading (currently experimental), and multi-core or distributed processing.

Researchers often find themselves coding algorithms in one programming language, only to have to rewrite them in a faster one. An up-and-coming language could be the answer.

[Julia] provides an early glimpse into what programming languages might look like in the not too distant future.

From guiding self-driving vehicles to analyzing images from deep space, US and Bengaluru-based Julia Computing has developed a unique programming language.

Data hackers get giddy when talking about [Julia’s] potential to oust R and Python from their thrones. Julia is a high-level, insanely fast and expressive language. It’s faster than R and potentially even more scaleable than Python, and fairly easy to learn.

HPC is still committed to its lower level tools and that will remain the case with domain scientists dabbling in Python until it fails to scale. This seems to clear the way for either Julia or Chapel.

Rajshekar Behar, marketing leader at Julia Computing– a rapidly rising startup specializing in AI solutions– says: I believe we keep mixing HPC with AI. AI is an application that needs high performance computing. When you start solving a problem, you reach a point where you want to delegate the decision making to a system, because you think it's going to take better decisions. And that's when you implement AI with HPC, he explains.

I do think that, at a business level, as well as at a national level, we have a lot of catching up to do... says Viral Shah, co-founder of Julia Computing. He adds that Indian firms today have a lot of data but the skill in asking questions on what can be done with data for effectively building AI models is missing.

Apache Zeppelin joins a growing list of data science notebooks that include Databricks Cloiud, Jupyter (the successor to the iPython Notebook), R Markdown, Spark Notebook and others. Backends to multiple languages include Python, Julia, Scala, SQL and others.

Julia is poised to become one of the leading tools deployed by developers and programmers at banks, hedge funds, regulators and vendors.

Julia Computing was selected as one of five startups to present to Jeff Immelt, then CEO of GE. … Nandan helped us make it strategic – how can a company like GE benefit from open source technology and approach? That really resonated with them.

Julia Computing, provider of open-source language for data science, machine learning and scientific computing.

[Julia] reads like Python or Octave, but performs as well as C. It has built-in primitives for multi-threading and distributed computing, allowing applications to scale to millions of cores. In addition to HPC, Julia is also gaining traction in the data science community.

The focus is to upskill the workforce in IoT with workshops on new technologies like LoRa and Julia scientific computing.

Julia has been used in such applications as image analysis and linear algebra research.

In addition to improving R and Python, the group hopes its work will also improve the user experience in other open-source programming languages like Java and Julia.

Julia is another great language with an open and active community. They are currently investing in machine learning techniques, and even have good interoperability with Python APIs. The Julia community shares many common values as with our project, which they published in a very like-minded blog post after our project was well underway. We are not experts in Julia, but since its compilation approach is based on type specialization, it may have enough of a representation and infrastructure to host the Graph Program Extraction techniques we rely on.

Julia Computing ... has developed a unique and high performing programming language with rich applications in AI and Machine Learning

Jupyter is the successor to the iPython notebook, and as such is closely aligned with Python, but it also supports R, Scala, and Julia.

While Julia is not yet among the most in-demand programming languages on Wall Street, its growth rate is impressive and hedge funds are among the early-adopters

Intel India has established deep industry collaborations on the lines of existing partnerships with Hewlett Packard Enterprise, Wipro, Julia Computing and Calligo Technologies, while also acquiring companies that can accelerate its AI solution development capabilities.

Until recently, it’s been difficult to take these theoretical ideas and bring them to the real world. But now, anybody can bring it to life. This is what we are doing with Julia.

Julia’s JIT compilation and type declarations mean it can routinely beat pure, unoptimized Python by orders of magnitude.

Julia is a JIT (just-in-time) compiled language, which lets it offer good performance. It also offers the simplicity, dynamic-typing and scripting capabilities of an interpreted language like Python. Julia was purpose-designed for numerical analysis. It is capable of general purpose programming as well. Many users of the language cite [readability] as a key advantage.

Julia is a free, open-source computer programming language that is gradually becoming a popular alternative to more established languages such as MATLAB and Python. Envisioned as a way to avoid the difficulties of using slower, older languages for today’s more advanced analytical applications, without compromising on ease of use, Julia has found fans around the world, drawing a sizable community of users since its launch in 2012.

Julia Computing focuses on building products at the intersection of machine-learning and big data to solve problems in areas such as algorithmic trading, self-driving vehicles, astrophysics, drug discovery and augmented reality.

Open-source libraries allow researchers to create free Plotly graphics from R, MATLAB, Python and Julia code.

Machine learning expert Alan Edelman expressed his keenness to train Bangladeshi young programmers in developing new technologies through Artificial Intelligence.

Julia will support the developers to implement their innovation in programming.

Julia Computing has been developing next-generation solutions to meet many of these requirements.

Julia is a fast-maturing programming language developed to be simple to learn, highly dynamic, operational at the speed of C, and ranging in use from general programming to highly quantitative uses such as scientific computing, machine learning, data mining, large-scale linear algebra, and distributed and parallel computing.

Viral Shah, CEO and co-founder of Julia Computing said, 'For industry the focus is on robustness whereas in academia it's all about experimentation'.

The performance of productivity languages such as Python, Julia, Java, R and more is increasing by leaps and bounds.

It’s a shame that R doesn’t have a shorter anonymous function syntax, given that it’s a functional programming language. I’d also love to have Julia’s triple quoted strings and non-standard string literals.

Disney, Uber, Apple and Amazon are all keenly interested in a relatively new startup company called Julia Computing . . . The young company is behind a programming language that’s catching on like wildfire.

He searched online for a way to turn numbers into sound and found a script using the programming language, Julia.

Julia Computing has been granted $910,000 by the Alfred P. Sloan Foundation to support open-source Julia development, including $160,000 to promote diversity in the Julia community.

One effective approach to addressing climate change is contributing to the development of Julia. Julia is a modern technical language, intended to replace Matlab, R, SciPy, and C++ on the scientific workbench … it has beautiful foundations, enthusiastic users, and a lot of potential. ...I’m also happy to endorse Julia because, well, it’s just about the only example of well-grounded academic research in technical computing.

If you use a programming language (R, Python, Julia, F#, etc) to script your analyses then the path taken should be clear - as long as you avoid any manual steps.

mxnet: a deep learning framework capable of distributed computing with a large selection of language bindings, including C++, R, Scala and Julia.

Pitted against R for example, Julia is a lot faster and less quirky as a programming language.

Julia (Top 25 Most Loved Programming Languages 2017).

Julia, a high-level dynamic language for technical programming, is gaining a foothold among developers. For the first time, the language has cracked the top 50 in this month's Tiobe Index of language popularity, rising to 47th place.

$160,000 will be used to create a new position—designated as full-time director of diversity initiatives.

The latest version of Julia has been released with what has been described as a sweeping overhaul of the type system and numerous improvements to syntax and to the standard library.

Julia ist eine dynamisch typisierte, funktionale Programmiersprache, die vor allem auf technische Berechnungen und damit wissenschaftliche Projekte zielt, gleichzeitig aber auch als General Purpose Language zum Einsatz kommen soll.

Julia is an open-source language for high-performance technical computing and data science.

Julia delivers dramatic improvements in simplicity, speed, capacity and productivity. Julia provides parallel computing capabilities out of the box and unlimited scalability with minimal effort.

Julia Computing provides a high performance open source computing language for data, analytics, algorithmic trading, machine learning and artificial intelligence.

‘Why we created Julia’ ... went viral on Reddit and Hacker News, getting more than 200,000 hits in a day. It was a big tipping point.

Julia now has 250,000 users around the world and is doubling every nine months.

Julia Computing is working on an AI-powered software capable of diagnosing diabetic retinopathy, a degenerative condition that affects eyesight. The developers have trained a neural network using a huge dataset of images showing infected eyes and those free from the condition. In this way, the technology could theoretically be applied in a patient’s home, using a high quality smartphone camera. This way, the patient can self-diagnose for the condition before visiting a doctor, and even track the advancement of his condition.

Predictive analytics and machine learning are the future of tech, so I would focus on math, statistics, and behavioral psychology, says Jill Witty, VP of Talent at Entelo. Regarding programming languages and back-end tech I would emphasize R, Python, Java, JavaScript, Julia, Scala, and Hadoop, among others.

The TIOBE Index expects that in 2017, the favored candidates for programming language of the year will include Apple's Swift, Julia, the Microsoft-created TypeScript, and the ever-popular C++.

Will Julia turn into one of the popular languages for machine learning and data science? Time will tell.

Julia has specific features built into the core language that make it particularly suitable for working with the real-time streams of Big Data industry wants to leverage these days, such as parallelization and in-database analytics. The fact that code written in Julia executes very quickly adds to its suitability here.

Julia is a high-level, high-performance dynamic programming language for technical computing, with syntax that is familiar to users of other technical computing environments. It provides a sophisticated compiler, distributed parallel execution, numerical accuracy, and an extensive mathematical function library. Julia’s Base library, largely written in Julia itself, also integrates mature, best-of-breed open source C and Fortran libraries for linear algebra, random number generation, signal processing, and string processing.

What I like about Julia is that it is very easy to use, and despite being as easy to use as Python, you still have a lot of low level control.

The benefits of using Julia over other languages for scientific work include: its low barrier to entry for scientists and mathematicians, its flexibility and high performance (comparable to C), its graphics and visualization capabilities, and its ability to handle large computational problems efficiently.

A number of [corporations] across the world are now finding real-world use for Julia, which can be deployed in … areas such as data science and recommendation engines, especially in sectors such as ecommerce, finance and engineering.

Overall, Julia is a welcome addition [to the High Performance Computing (HPC)] community. …The future looks bright for Julia. New and existing HPC coders will appreciate a dirt-simple on-ramp to the HPC superhighway.

Delivering Hadoop style parallelism, ... [Julia] is destined to make a major impact.

Julia should be on the radar of everyone from traders and operations executives to IT managers, developers and data scientists and really anyone who wants to expand their job options as electronic trading takes over and the industry as a while becomes more technology-centric.

Julia is a really well-thought-out language. …it has a rigorous but infinitely flexible type system …. When these features are combined with the built-in just-in-time (JIT) compiler, they let code … run as fast as C or Fortran. But the real killer is that you can do this with code as concise and expressive as Python. … So why not make your next language Julia? It might even be the last one you need to learn.

Programming language Julia made it to a list of the top 50 programming languages in the world last month.

“Traditionally, HPC software development uses languages such as C, C++, and Fortran, which compile to unmanaged code. This offers the programmer near bare-metal performance at the expense of safety properties that a managed runtime would otherwise provide. Julia, on the other hand, combines novel programming language design approaches to achieve high levels of productivity without sacrificing performance while using a fully managed runtime.”

Improved multithreading is one of the most important goals that the Julia development team has been working on for several versions. An important milestone was reached with the stabilization of the multithreading APIs in Julia 1.5. Since then, the team has taken care of tackling numerous race conditions in runtime and fixing synchronization errors. Developers also have more options for distributing workloads in their programs across multiple threads.

Much has been made of Julia’s swift ascendancy in data science circles over the last few years — and for good reason. It’s now a top 20 language in the IEEE Spectrum rankings, and it also cracked the top 20 of the influential TIOBE index last year. (As of writing, it stands at #28).

Julia is a data science coding language designed specifically for high-performance numerical methods and computational research. It has the ability to swiftly apply mathematical principles such as linear algebra. It’s also a fantastic language for working with matrices. Julia’s API may be incorporated in applications that can be used for various back-end and front-end developments.

In terms of AI capabilities, Julia is great for any machine learning project. Whether you want premade models, help with algorithms, or to play with probabilistic programming, a range of packages await, including MLJ.jl, Flux.jl, Turing.jl, and Metalhead.

JuliaHub.com also offers solutions for corporate and other customers. For example, we have developed a product called Pumas for pharmaceutical companies. There is also a product called JuliaSim that allows engineers to use complex systems to simulate designs... And there are also products such as JuliaSPICE for circuit simulation.

“We were all using Python, C, and Java to execute our ideas or run simulations, but the library dependency of these languages made them terrible. The world back then had accepted that you could not have high-level language and easy-for-production language together. With Julia, we wanted to solve this problem and we created something that was way faster than any other language,” explains Viral.

Open-source software (OSS), software that is free to access, use, and change without restrictions, plays a central role in the development and use of artificial intelligence (AI). Across open-source programming languages such as Python, R, C++, Java, Scala, Javascript, Julia, and others, there are thousands of implementations of machine learning algorithms.

Julia Computing was founded by the creators of Julia —the fastest and easiest high-performance computing language for artificial intelligence, machine learning, analytics, data science, modeling, and simulation. Julia is used by more than 10,000 companies worldwide, including AstraZeneca, BlackRock, Google, Intel, Microsoft, Moderna, Pfizer, as well as NASA, the Federal Aviation Administration and the Federal Reserve Bank of New York.

Tim Tully, partner at Menlo Ventures concurs, Investing in companies building best-in-class cloud technology is our strategic focus, and we feel that the opportunity to support the fantastic team and products from Julia Computing is a natural fit for our portfolio. We see several startups building their businesses using Julia, not just in the scientific computing community, but also more broadly across a wide range of enterprises and industries. We look forward to great things from the Julia Computing team and we are excited to be involved in their future successes.

If you don’t know about Julia, I’ll help. High-level programming languages ​​allow developers to “speak” with computers, making coding easier. Julia is one such language that aims to solve one of the biggest problems currently affecting the technology industry. Programs are usually initially coded in a language like Python, which makes it easier to develop new products. However, in the future, the same program will be rewritten in C or C ++ for performance and scalability. Julia aims to solve this problem by offering a product that has the best of both worlds.

Since data scientists and AI specialists deal with lots of mathematical problems, Julia is the winner for them. And even upon critical scrutiny, Julia has upsides that Python can’t beat.

Julia is a very new language that competes head-on with Python. It fills the gap of large-scale technical computations: Usually, one would have used Python or Matlab, and patched the whole thing up with C++ libraries, which are necessary at a large scale. Now, one can use Julia instead of juggling with two languages.

Programming languages, such as Python and Julia, which are now being taught to Earth science students, will accompany the transition to these new methods and will be used in interactive environments such as the Jupyter Notebook. Julia was shown recently to perform well as compiled code for machine learning algorithms in its most recent implementations, such as the ones using differentiable programming, which reduces computational resource and energy requirements.

'[The Julia language] takes advantage of this new compilation technology to create a programming experience as intuitive or as easy as Python, but at the same time as high-performance as a language like C,' said Bakshi. 'So your code can be compiled on the fly.'

Spectrum Instrumentation in Germany has added support for a new programming language called Julia to its test systems.

The fight against the coronavirus does not only take place in medical laboratories, but also in computing. Handling the huge amounts of data for vaccine development requires advanced tools: Julia, a relatively new software language, has surged in popularity. It delivers comparable speed and functionality to programming in C while also allowing scientific and numerical computing. As a leading manufacturer of advanced scientific test and measurement equipment, Spectrum Instrumentation is excited to announce that it has created a Software Development Kit (SDK) for programming its full range of over 200 different digitizers, generators and digital I/O products using Julia.

Fons van der Plas, a software engineer in Berlin, created Pluto, a reactive notebook platform for the programming language Julia. Henri Drake, a graduate student of climate physics at the Massachusetts Institute of Technology in Cambridge, uses Pluto to demonstrate concepts in climate science. “Coding it up as an interactive Pluto notebook makes it a way more engaging experience for a first-time user,” Drake says, “and can really help people understand the models that I’m building.”

Much has been made of Julia’s swift ascendancy in data science circles over the last few years — and for good reason. It’s now a top 20 language in the IEEE Spectrum rankings, and it also cracked the top 20 of the influential TIOBE index last year.

JuliaSim allows you to directly import models from its Model Store into your Julia environment, making it easy to build large complex simulations. Pre-trained machine learning models leveraging SciML are seamlessly integrated into the engineer’s workflow, saving both model development and simulation time. Design your physical product right and reduce iterations by creating high-fidelity designs, automatically transforming them into accelerated versions, and searching through vast parameter spaces.

The open-source programming language Julia is particularly interesting for writing and building applications for data visualization, data science applications, scientific environments, parallel computing and machine learning. The code can also be easily combined with other programming languages, such as Python, R, C / Fortran, C ++ and Java.

In barely 10 years of existence, the Julia programming language has established itself as one of the best languages ​​used for scientific computing and high performance computing (HPC). The language has seen a significant increase in its community over the past few years and in 2020 alone it saw an 87% increase in its downloads. Julia shines again, because DARPA has just granted funding to Julia Computing to provide more performance, particularly in terms of speed, in the field of electronic circuit simulation. The speed should be multiplied by 1000.

Julia is a preferred language for data science, artificial intelligence, and modeling and simulation applications. The language combines the ease of use of Python and R with the speed of C++, while providing parallel computing capabilities out of the box.

“Julia’s performance and differentiable programming capabilities give us a unique advantage in creating novel tools for modeling and simulation,” says project PI and Julia Computing CTO Keno Fischer. “Using newly developed surrogate architectures, such as our Continuous Time Echo State Network (CTESN) architecture, we have already been able to demonstrate acceleration in excess of 100x by employing these techniques in multi-physics simulations and are excited to bring this technology to the electronics simulation space.”

One prominent Julia user, Rick Stevens, associate director of Argonne National Laboratory, told HPCwire, “I saw the 87 percent increase and think it is wonderful to see Julia growing. I think that Julia has great potential to replace C/C++/Python (and of course Fortran) in scientific and technical computing as it matures.

The Julia Language community fostered co-presence for their daylong hackathon event where people could work alongside each other and feel a sense of aligned productivity. Other folks noted the remarkable ability to have “some serious discussion” after research presentations in Gather without the lurking feeling that a handful of participants might monopolize the conversation as in Zoom calls.

Gonciulea says Julia is a good replacement for Python because it has a scripting side and a plot module that allows it to interface with Python plot libraries. I generally like having types, and generics, and Julia has both, he sayd. It does not have covariant and contravariant types like Scala, whose type system is a language in itself, but having generics is a big plus. Like in Python, types can be skipped when it make sense. It has a lot of libraries, the plotting is great, and it is fast.

Julia users are at the forefront of high-performance computing, from basic research to financial modeling, from pharmaceuticals to energy to aerospace. Julia’s combination of clear and expressive syntax and its relentless focus on performance and scalability make it an ideal platform for our era of exponential growth in data volume. We’re thrilled to be able to offer the same flexible, interactive Dash experience that Python and R users know and love to this new community

“I’m very excited that Julia, already fast becoming the language of scientific machine learning, and a great tool for collaborative software, can play a key role in space weather applications,” says Edelman.

As a consequence, Julia is the language we now tend to pick for new projects and generally recommend.

Pumas-AI, a University of Maryland, Baltimore (UMB) startup company, has been granted worldwide, exclusive rights to Lyv, a cutting-edge clinical decision support system [in Julia] designed to help health care professionals personalize treatment trajectories for patients in real-time.

Julia is comparable to Python for simple machine learning tasks and better for complex ones. Julia achieves performance that is orders of magnitude better than other dynamic languages for technical computing. Native Julia programs are often 10x-100x faster than similar programs in R, Python, Matlab, etc.

Nearly 300 students join an open course that applies data science, artificial intelligence, and mathematical modeling using the Julia language to study Covid-19.

I’d like to work with Julia. It is a high-performant, general-purpose programming language with a focus on numerical analysis. I’d like to see more support via library bindings to help drive adoption. This is because the language implements some cool concepts to achieve high performance in a very Python-esque writing style. There’s a demand to squeeze more performance out of software. So having a language that allows you to write in an intuitive and simple manner — while remaining performant — can open the doors to more efficient and increased throughput of data processing and analysis techniques.

Julia is making a run at the top and [Alan] Edelman won last year’s IEEE Sidney Fernbach Award presented at SC19 for, among other things, his work on Julia.

Julia ... has become the new de-facto for machine learning. Julia offers best-in-class support for modern ML-frameworks like TensorFlow and MXNet, making it easy to adapt to existing workflows.

JuliaTeam is an enterprise solution from Julia Computing that makes it easy and safe for developers, data scientists and IT managers 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 Computing CEO Viral] Shah says, "The open source community members help newcomers to the community, answer questions on Stack Overflow, organise meetups [and] mentor students. The key is to recognize that you have benefited greatly and it is important to give back."

Julia is the programming language of choice for prominent researchers who work on projects at the cutting edge of machine learning as well as in differential equations research

[Julia] enables machine learning developers and data scientists to enjoy the speed of C with the dynamism of Ruby, usability of Python, statistical ability of R, and mathematical power of MATLAB.

Julia ... can be used by those who aren't programmers by training

Julia has been used by organizations large and small to calculate regulatory capital, value portfolios and design trading strategies. In this article, we will demonstrate one such example use case, calculating the arbitrage opportunities in FX cross rates. We will show how a complex optimization problem can be implemented in a few lines of Julia code.

Julia is fast, … can call C directly without a wrapper, integrates top tier open source C and Fortran code into its Base library, and can easily call Python as well. Julia is built for parallel and cloud computing, and has particular interest from the analytics and scientific computing communities. According to KDnuggets' most recent analytics software poll, Julia placed 8th on the list of most used programming languages.

The Jupyter notebook, as it’s called, is like a Mathematica notebook but for any programming language. You can have a Python notebook, or a C notebook, or an R notebook, or Ruby, or JavaScript, or Julia.

Also in this month’s index, Kotlin and Julia both entered the top 40... Julia, in 37th place ... is used in scientific computing and [the] burgeoning field of machine learning.

The RStudio community plans to provide support to other languages such as Julia and Haskell.

Rajshekar Behar, marketing leader at Julia Computing, a rapidly rising startup specializing in AI solutions, believes that investing in learning holds the key to AI growth. The major barrier I see is that the gap between haves and have-nots keeps increasing. One way to tackle this problem is to invest in the infrastructure of learning. The skillset required for AI is not rocket science, so colleges need to include AI in their curriculum, he adds.

They wanted 'a Goldilocks programming language - one that was high level and low level at the same time, depending on how you used it' said [Julia co-creator Stefan] Karpinski. The Goldilocks ideal gave way to the Julia project: an open-source, dynamic programming language.

Jupyter’s developers wanted to run other languages besides that of IPython, so they collaborated with developers to be able to run Julia.

The inspiration for Julia Computing is the demand for Julia, says Viral Shah.

My science teachers were fantastic but you had to go to the all-boys school next door to take Advanced Placement Physics, says [Julia Computing Director of Diversity and Outreach Jane] Herriman, who completed her bachelors in chemistry from Carnegie Mellon and is now pursuing a PhD in [materials science] from Caltech.

Also in this month’s index, Kotlin and Julia both entered the top 40.... [Julia] is used in scientific computing and the burgeoning field of machine learning.

JuMP [Julia por Optimización Matemática] permite a los usuarios expresar fácilmente complejos problemas de optimización matemática con una notación natural que imita lo que un usuario podría escribir en papel.

Bangladeshi youth are very talented. They will show excellence if they are given proper mentorship and training program. MIT and Julia Computing will support local organizations in this regard.

Julia has been used in such applications as image analysis and linear algebra research.

As a data scientist who has been using the language for 5 years now, Julia is by far the best programming language for analyzing and processing data, one said. Julia, probably not as well-known as R or Python, is described as a high-level, high-performance dynamic programming language for numerical computing.

Python, R, and Scala are the fastest-growing languages for data science, along with Julia, another upcoming language in the space

The Celeste team demonstrated that the Julia language can support both petascale compute and terascale big data analysis on a leadership HPC system plus scale to handle the seven petabytes of data expected to be produced by the Large Synoptic Survey Telescope (LSST) every year.

Intel India has established deep industry collaborations on the lines of existing partnerships with Hewlett Packard Enterprise, Wipro, Julia Computing and Calligo Technologies, while also acquiring companies that can accelerate its AI solution development capabilities.

Julia provides the productivity and performance equivalent to five major programming languages including R, Python, MATLAB, C, and FORTRAN. It further provides a sophisticated compiler, distributed parallel execution, numerical accuracy, and an extensive library of fast mathematical functions.

Julia is a big deal. It’s a free alternative to proprietary tools for doing data science … and it’s more contemporary than open-source languages R and Python.

Julia, for example, recently delivered a peak performance of 1.54 petaflops using 1.3 million threads on 9,300 Intel Xeon Phi processor nodes of the Cori supercomputer at NERSC. The Celeste project utilized a code written entirely in Julia that processed approximately 178 terabytes of celestial image data and produced estimates for 188 million stars and galaxies in 14.6 minutes.

Viral Shah, co-founder of Julia Computing and a co-creator of the Julia programming language, said companies need to decide the level of cyber risks assessing their threat landscapes. Shah said data protection is a three-step process that requires companies to safeguard their hardware with sufficient security protocols,monitor data access and make sure it's encrypted.

This research collaboration of astrophysicists, statisticians and computer scientists from UC Berkeley, Berkeley Lab, MIT, Julia Computing and NERSC developed Celeste, a statistical analysis model designed to dramatically speed up one of modern astronomy’s most time-tested tools: sky surveys.

Julia Computing builds professional software tools to make it easier for organizations, especially in the finance world, to make use of the Julia language, which is particularly good for in-demand tasks like data analytics and machine learning. Asset manager BlackRock and large British insurer Aviva are both Julia Computing customers, for example.

Julia Computing has a chance to redefine the way mathematics and science are practiced. The way Red Hat makes Linux approachable to enterprises, Julia Computing does for the Julia programming language.

Programming is an important element of the master’s. The curriculum incorporates C++, as well as Python and R. In a couple of years’ time, the university may add a new programming language: Julia. “It’s more recent, it’s extremely fast and convenient to program with, and its use is expanding,” says Jacquier.

Julia Computing has revealed a seed funding of $4.6 million from investors General Catalyst and Founder Collective. The firm offers Julia, an open source computing language for data, analytics, algorithmic trading, machine learning and artificial intelligence (AI).

Julia provides the productivity and performance equivalent to five major programming languages including R, Python, Matlab, C, and Fortran. It further provides a sophisticated compiler, distributed parallel execution, numerical accuracy, and an extensive library of fast mathematical functions.

Coming second is Julia Computing with $4.6 million raised.

Julia's advantages over other languages include speed improvements, unlimited scalability, and easy integration with existing code platforms.

Julia Computing seeks to commercialize open-source programming language Julia.

Machine learning expert Alan Edelman will mentor Bangladeshi young programmers to develop new technologies through artificial intelligence (AI).

Machine learning expert Alan Stuart Edelman will mentor Bangladeshi young programmers to develop new technologies through Artificial Intelligence (AI).

Julia delivers dramatic improvements in simplicity, speed, capacity, and productivity to solve massive computational problems quickly and accurately. It is the preferred language for big data and analytics. It is an open source project with a diverse community of almost 500 contributors around the world.

Intel India is collaborating with Hewlett Packard Enterprise, Wipro, Julia Computing, and Calligo Technologies, by enabling them with AI solutions based Intel architecture.

Julia, an open source computing language, caters to data, analytics, algorithmic trading, machine learning and artificial intelligence.

Julia has built a strong following. It has a number of Hadoop style features, which makes it a very useful programming language for developers working on big data projects.

Julia parallel computing tools ... made these speedups possible.

Julia has joined the rarefied ranks of computing languages that have achieved peak performance exceeding one petaflop per second – the so-called Petaflop Club.

All of this makes Julia a one-stop shop for the various stages of a complex machine-learning project.

The team behind the Julia programming language have now ported the language to the Raspberry Pi hardware and have added support for GPIO, the Sense HAT and Minecraft.

TIOBE expects next year's top candidates to be Apple's Swift language, Julia, and the Microsoft-maintained TypeScript.

Julia was added to the list of languages we track in 2015, and in the past year it's moved from rank 40 to 33 ... clearly possessing some momentum in its growth.

Julia Computing was created to commercialize and support the high-performance Julia programming language.

Get up close and personal with newer technologies (Google Go or the dynamic Julia programming language).

Julia is designed to eliminate the need for researchers to use multiple programming languages to perform computational analyses and surrounding tasks.

GitHub data shows that developers are showing a healthy interest in the programming language that was designed to do everything.

The other language that's worth checking out is Julia... Julia's support for parallelism is intriguing.

Check out Julia (www.julialang.org), [Assistant Professor of Genome Sciences at the University of Washington Cole] Trapnell says, an emerging language that combines the syntax of Python, the graphing acumen of R, and the speed of C++. That means the code is easy to write, but super fast.

Julia is described as a high-level, high-performance dynamic programming language for technical computing.

Julia is a high-performance language for processing and visualizing data in cloud environments, Julia is also open source. It was designed with parallelism in mind and includes key elements for distributed computation.

Julia Computing focuses on building products at the intersection of machine-learning and big data to solve problems in areas such as algorithmic trading, self-driving vehicles, astrophysics, drug discovery and augmented reality.

This programming language compiles straight to machine code as it runs, it seems to be a valid alternative to Hadoop and it might follow in the footsteps of C speed wise.

This new language’s popularity is largely due to three things: support, community, and speed.

Julia is ... faster at its core than Python. It also features a growing list of packages, covering not just math and science applications, but also other functionalities associated with Python, like connectivity to data sources on cloud providers.

Julia boasts performance as fast as that of languages like C or Fortran, and is still simple to learn. … We tested our code and found that the model estimation is about ten times faster with Julia than before, a very large improvement.

[I]t’s notable that Julia, a high-level programming language built expressly for use in technical computing, has entered TIOBE’s list …. In industries that prize efficiency, such as finance, Julia has enjoyed rapid adoption by tech professionals and data scientists. In banking and trading, algorithmic traders and quants now rely on Julia because it allows them to push code as quickly as possible to market, without needing to rewrite.

[Julia has] generated an excited buzz in scientific computing circles. Julia may be the first language since Fortran created specifically with scientific number crunching in mind. Julia allows expressive programming using sophisticated abstractions while attaining C … speed in many benchmarks. …Julia has powerful concurrency and networked programming facilities; it can interface seamlessly with Fortran and C library routines … and it’s able to act as shell-like glue code. Julia can be as simple and direct to program in as Python while offering an order of magnitude increase in speed.

Big-data languages, such as Julia, Python, R and Scala … are purpose-built for handling large amounts of numeric data, with stables of packages that can be tapped for quick big-data analytic prototyping.

The new code in Julia is easier to read than the R code because Julia has fewer syntactic quirks than R. More importantly, the Julia code runs much faster than the R code without any real effort put into speed optimization. For the sample text I tried to decipher, the Julia code completes 50,000 iterations of the sampler in 51 seconds, while the R code completes the same 50,000 iterations in 67 minutes — making the R code more than 75 slower than the Julia code.

Julia has almost C-like performance for some of the micro benchmarks and is much faster than languages like R and Python.