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 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 [email protected] for an
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:
Highlights of JuliaPro v0.6.0.1:
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
Julia Case Studies: Several exciting new Julia case studies are available on the Julia Computing Website
Contact Us: Please contact us at [email protected] if you wish to:
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.
Need help with Julia?
We also provide training and consulting services
and build open source or proprietary packages
for our customers on a consulting basis. Mail us:
Julia Computing was founded by all the creators
of the language to provide commercial support
to Julia users. We are based in Boston, New York,
San Francisco, London and Bangalore with
customers across the world.
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