We wanted to thank all Julia users and well wishers for the support and for being part of the Julia Community, and to give an update on some exciting developments for 2017:
Julia Joins the Petaflop Club: Celeste is the first application written in a dynamic high-level language to exceed 1 petaflop per second
JuliaRun: new and vastly improved for deployment and scaling with Julia v0.6
JuliaFin: updated with JuliaDB and Julia v0.6
Julia v0.6 and JuliaPro v0.6.0.1
JuliaCon 2017
Julia Computing Funding and Grant Announcements
Julia and Julia Computing in the News
Julia Case Studies
Contact Us
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Julia Joins the Petaflop Club: Celeste joins the rarified list of applications to exceed 1 petaflop per second performance, and is the first to do so in a dynamic high-level language. The Celeste research team processed 55 terabytes of visual data and classified 188 million astronomical objects in just 15 minutes, resulting in the first comprehensive catalog of all visible objects from the Sloan Digital Sky Survey. This is one of the largest problems in mathematical optimization ever solved. The Celeste team, which includes researchers from UC Berkeley, Lawrence Berkeley National Laboratory, National Energy Research Supercomputing Center, Intel, Julia Computing and the Julia Lab at MIT, used 9,300 Knights Landing (KNL) nodes on the NERSC Cori Phase II supercomputer to execute 1.3 million threads on 650,000 KNL cores.
JuliaRun allows you to run and deploy Julia applications in production at scale, including parallel and distributed computing on private or public clusters. JuliaRun works seamlessly with AWS and Microsoft Azure, and can be configured to run with any private cloud. You can start a JuliaRun instance today with just a few minutes of setup time. Write to us at <info@juliacomputing.com> for an evaluation version.
JuliaFin is a suite of Julia packages that simplify the workflow for quantitative finance including storage, retrieval, analysis and action. These include: Miletus, a domain specific language (DSL) for defining financial contracts; JuliaDB, a high performance in-memory database, with best performance time series analytics, in-memory and out-of-core analytics; integration with Bloomberg, Excel and other proprietary systems. Click here for details and to download for evaluation.
Julia v0.6 and JuliaPro v0.6.0.1 were released last month with the following upgrades: Highlights of Julia v0.6:
New type system capabilities that make Julia even more expressive and accurate
Automatic broadcasting and loop fusion for all operators and functions
Significantly faster strings
Improved inter-task communications using channels
Significant improvements in the standard library
For more details, please see the [release notes of Julia
0.6](https://github.com/JuliaLang/julia/blob/release-0.6/NEWS.md) Highlights of JuliaPro v0.6.0.1:
Supports Julia v0.6
Updated Atom to the latest version (1.18.0), updated all Atom packages
Updated all bundled Julia packages to Julia v0.6
Added the following packages
In the interest of getting this release out in as timely a fashion as possible, this release unfortunately does not include Gallium or MXNet. Updating both of these packages for Julia 0.6 is in progress and they will be included in the next release of JuliaPro.
JuliaCon 2017 was the biggest and most successful JuliaCon yet. It featured more than 300 participants and presenters, including presentations on how Julia is being used for deep learning, quantitative finance, energy, astrophysics, agriculture, medicine and more. Presentation videos are available on YouTube.
Julia Computing Funding and Grant Announcements: Julia Computing has announced completion of our first round of seed funding and a significant grant from the Sloan Foundation which includes dedicated funding to promote diversity in the Julia community.
Julia and Julia Computing in the News: There has been a huge increase in Julia and Julia Computing news mentions so far this year, consistent with significant increases in Julia adoption.
Julia Case Studies: Several exciting new Julia case studies are available on the Julia Computing Website including:
San Jose Semaphore – When Science Meets Art: High school math teacher Jimmy Waters from Powell, Tennessee used Julia to solve a Silicon Valley cryptography stumper that had gone unsolved for nearly 5 years.
Milk Output Optimizer (MOO): Oscar Dawson from the University of Auckland Electric Power Optimization Centre (EPOC) is using Julia to optimize dairy farming.
Mapping Global Genetic Diversity: Researchers are using Julia to conduct analysis and mapping of global genetic diversity. Their results have been published in Science.
Cancer Genomics: UK scientists used Julia to model tumor growth, informing interpretation of cancer genomes. Their results have been published in Nature Genetics.
Contact Us: Please contact us at <info@juliacomputing.com> if you wish to:
Purchase or obtain license information for Julia products such as JuliaPro, JuliaRun, JuliaFin or JuliaBox.
Obtain pricing for Julia consulting projects for your enterprise.
Schedule Julia training for your organization.
Share information about exciting new Julia case studies or use cases.
About Julia and Julia Computing
Julia is the fastest modern high performance open source computing language for data, analytics, algorithmic trading, machine learning and artificial intelligence. Julia combines the functionality and ease of use of Python, R, Matlab, SAS and Stata with the speed of C++ and Java. 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. With more than 1 million downloads and +161% annual growth, Julia is one of the top 10 programming languages developed on GitHub and adoption is growing rapidly in finance, insurance, energy, robotics, genomics, aerospace and many other fields.
Julia users, partners and employers hiring Julia programmers in 2017 include Amazon, Apple, BlackRock, Capital One, Comcast, Disney, Facebook, Ford, Google, Grindr, IBM, Intel, KPMG, Microsoft, NASA, Oracle, PwC, Raytheon and Uber.
Julia is lightning fast. Julia provides speed improvements up to 1,000x for insurance model estimation, 225x for parallel supercomputing image analysis and 10x for macroeconomic modeling.
Julia provides unlimited scalability. Julia applications can be deployed on large clusters with a click of a button and can run parallel and distributed computing quickly and easily on tens of thousands of nodes.
Julia is easy to learn. Julia’s flexible syntax is familiar and comfortable for users of Python, R and Matlab.
Julia integrates well with existing code and platforms. Users of C, C++, Python, R and other languages can
easily integrate their existing code into Julia.
Elegant code. Julia was built from the ground up for mathematical, scientific and statistical computing. It
has advanced libraries that make programming simple and fast and dramatically reduce the number of lines of code required – in some cases, by 90% or more.
Julia solves the two language problem. Because Julia combines the ease of use and familiar syntax of Python, R
and Matlab with the speed of C, C++ or Java, programmers no longer need to estimate models in one language and reproduce them in a faster production language. This saves time and reduces error and cost.
Julia Computing was founded in 2015 by the creators of the open source Julia language to develop products and provide support for businesses and researchers who use Julia.