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The difference in TTL arises because the system image can safely skip all the code-validation checks that are necessary when loading packages. At the time of Julia 1.9's release, only a small fraction of the package ecosystem has adopted PrecompileTools. As these new tools become leveraged more widely, users can probably expect ongoing improvements in TTFX. Methodology A demonstration workload was
Exactly ten years ago today, we published "Why We Created Julia", introducing the Julia project to the world. At this point, we have moved well past the ambitious goals set out in the original blog post. Julia is now used by hundreds of thousands of people. It is taught at hundreds of universities and entire companies are being formed that build their software stacks on Julia. From personalized me
24 March 2021 | Jeff Bezanson, Ian Butterworth, Nathan Daly, Keno Fischer, Jameson Nash, Tim Holy, Elliot Saba, Mosè Giordano, Stefan Karpinski, Kristoffer Carlsson Julia version 1.6 has been released. Most Julia releases are timed and hence not planned around specific features, but this release was an exception since it is likely to become the next long-term support (LTS) release of Julia. Becaus
Julia version 1.5 has been released. Releases are timed and hence not planned around specific features, but this time we seem to have gotten lucky: quite a few major developments came together to make 1.5 particularly exciting. Let's walk through some highlights. Struct layout and allocation optimizations This release brings a major, long-desired optimization that can significantly reduce heap all
Julia 1.5 Feature Preview: Time Traveling (Linux) Bug Reporting The Julia project, like any large open source project, gets a large number of bug reports every day. As the developers of the language, we try our best to be as responsive as possible and to triage, investigate and fix any bugs as quickly as possible. For some bugs, this is easy. If the bug report is well written and the problem is ev
23 July 2019 | Jeff Bezanson (JuliaHub), Jameson Nash (JuliaHub), Kiran Pamnany (Intel) Software performance depends more and more on exploiting multiple processor cores. The free lunch from Moore's Law is still over. Well, we here in the Julia developer community have something of a reputation for caring about performance. In pursuit of it, we have already built a lot of functionality for multi-p
The authors are pleased to announce the release of a fully-featured debugger for Julia. You can now easily debug and introspect Julia code in a variety of ways: Step into functions and manually walk through your code while inspecting its state Set breakpoints and trap errors, allowing you to discover what went wrong at the point of trouble Interactively update and replace existing code to rapidly
3 December 2018 | Mike Innes, James Bradbury, Keno Fischer, Dhairya Gandhi, Neethu Mariya Joy, Tejan Karmali, Matt Kelley, Avik Pal, Marco Concetto Rudilosso, Elliot Saba, Viral Shah, Deniz Yuret Since we originally proposed the need for a first-class language, compiler and ecosystem for machine learning (ML), there have been plenty of interesting developments in the field. Not only have the trade
Translations: Simplified Chinese, Traditional Chinese, Spanish The much anticipated 1.0 release of Julia is the culmination of nearly a decade of work to build a language for greedy programmers. JuliaCon 2018 celebrated the event with a reception where the community officially set the version to 1.0.0 together. The release was accompanied by a talk: A brief history and wild speculation about the f
14 March 2017 | Simon Byrne, Luis Benet and David Sanders Image courtesy of Cormullion, code here. This post is available as a Jupyter notebook here. π in Julia by Simon Byrne Like most technical languages, Julia provides a variable constant for π. However Julia's handling is a bit special. pi π = 3.1415926535897... It can also be accessed via the unicode symbol (you can get it at the REPL or in a
After a lengthy design process and preliminary foundations in Julia 0.5, Julia 0.6 includes new facilities for writing code in the "vectorized" style (familiar from Matlab, Numpy, R, etcetera) while avoiding the overhead that this style of programming usually imposes: multiple vectorized operations can now be "fused" into a single loop, without allocating any extraneous temporary arrays. This is b
To follow along with the examples in this blog post and run them live, you can go to JuliaBox, create a free login, and open the "Julia 0.5 Highlights" notebook under "What's New in 0.5". The notebook can also be downloaded from here. Julia 0.5 is a pivotal release. It introduces more transformative features than any release since the first official version. Moreover, several of these features set
Julia Academy Do you learn best by watching instructor led videos on programming? Check out JuliaAcademy which was prepared by core Julia developers in collaboration with JuliaHub. Exercism Julia Track Prefer to learn by doing exercises and getting feedback from a team of welcoming mentors? Check out the Julia Track on Exercism.org. The Manual Want to just give it a shot and dive right into the Ju
Note: updated December 2018 for Julia 1.1 Note: updated April 2020 for clarity Julia makes it easy to write elegant and efficient multidimensional algorithms. The new capabilities rest on two foundations: an iterator called CartesianIndices, and sophisticated array indexing mechanisms. Before I explain, let me emphasize that developing these capabilities was a collaborative effort, with the bulk o
A PL designer used to be able to design some syntax and semantics for their language, implement a compiler, and then call it a day. – Sean McDirmid In the few years since its initial release, the Julia language has made wonderful progress. Over four hundred contributors – and counting – have donated their time developing exciting and modern language features like channels for concurrency, a native
JSoC 2015 project: Efficient data structures and algorithms for sequence analysis in BioJulia Participant: Kenta Sato (@bicycle1885) Mentor: Daniel C. Jones (@dcjones) Thanks to a grant from the Gordon and Betty Moore Foundation, I've enjoyed the Julia Summer of Code 2015 program administered by the NumFOCUS and a travel to the JuliaCon 2015 at Boston. During this program, I have created several p
We are pleased to announce the release of Julia 0.4.0. This release contains major language refinements and numerous standard library improvements. A summary of changes is available in the NEWS log found in our main repository. We will be making regular 0.4.x bugfix releases from the release-0.4 branch of the codebase, and we recommend the 0.4.x line for users requiring a more stable Julia environ
Once installed julia will be available via the command line interface. This will install the Juliaup installation manager, which will automatically install julia and help keep it up to date. The command juliaup is also installed. To install different julia versions see juliaup --help. Please star us on GitHub. If you use Julia in your research, please cite us. If possible, do consider sponsoring u
Spawning a pipeline of connected programs via an intermediate shell — a.k.a. "shelling out" — is a really convenient and effective way to get things done. It's so handy that some "glue languages," like Perl and Ruby, even have special syntax for it (backticks). However, shelling out is also a common source of bugs, security holes, unnecessary overhead, and silent failures. Here are the three reaso
14 February 2012 | Jeff Bezanson Stefan Karpinski Viral B. Shah Alan Edelman Jeff Bezanson Stefan Karpinski Viral B. Shah Alan Edelman In short, because we are greedy. We are power Matlab users. Some of us are Lisp hackers. Some are Pythonistas, others Rubyists, still others Perl hackers. There are those of us who used Mathematica before we could grow facial hair. There are those who still can't g
Fast Julia was designed for high performance. Julia programs automatically compile to efficient native code via LLVM, and support multiple platforms. Dynamic Julia is dynamically typed, feels like a scripting language, and has good support for interactive use, but can also optionally be separately compiled. Reproducible Reproducible environments make it possible to recreate the same Julia environm
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