Julia in the News
[Julia] provides an early glimpse into what programming languages might look like in the not too distant future.
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.
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.
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.
26 JAN 2016 | Douglas Eadline | THE NEXT PLATFORM
Delivering Hadoop style parallelism, ... [Julia] is destined to make a major impact.
01 JAN 2016 | Mark Trego | CODING DOJO
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.
31 MAR 2016 | Sam | OCEANOGRAPHER’S CHOICE
Julia is poised to become one of the leading tools deployed by developers and programmers at banks, hedge funds, regulators and vendors
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.
14 MAY 2014 | Anna Nicolaou | FAST COMPANY
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.
03 DEC 2015 | Smith | FEDERAL RESERVE BANK OF NY
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.
AUG 2016 | Matthew Mayo | KDnuggets
[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.
07 MAY 2014 | Lee Phillips | Ars Technica
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.
03 JUN 2016 | Nicholas Diakopoulos | IEEE SPECTRUM
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.
20 JAN 2017 | J Vignesh et al | Economic Times
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