julia | source | downloads | docs | blog | community | teaching | publications | gsoc | juliacon | rss 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. The library, largely written in Julia itself, also integrates mature, best-of-breed C and Fortran libraries for linear algebra, random number generation, signal processing, and string processing. In addition, the Julia developer community is contributing a number of external packages through Julia’s built-in package manager at a rapid pace. IJulia, a collaboration between the IPython and Julia communities, provides a powerful browser-based graphical notebook interface to Julia. Julia programs are organized around multiple dispatch; by defining functions and overloading them for different combinations of argument types, which can also be user-defined. For a more in-depth discussion of the rationale and advantages of Julia over other systems, see the following highlights or read the introduction in the online manual. A Summary of Features
High-Performance JIT CompilerJulia’s LLVM-based just-in-time (JIT) compiler combined with the language’s design allow it to approach and often match the performance of C. To get a sense of relative performance of Julia compared to other languages that can or could be used for numerical and scientific computing, we’ve written a small set of micro-benchmarks in a variety of languages: C, Fortran, Julia, Python, Matlab/Octave, R, JavaScript, Go, and Mathematica. We encourage you to skim the code to get a sense for how easy or difficult numerical programming in each language is. The following micro-benchmark results were obtained on a single core (serial execution) on an Intel? Xeon? CPU E7-8850 2.00GHz CPU with 1TB of 1067MHz DDR3 RAM, running Linux:
Figure: benchmark times relative to C (smaller is better, C performance = 1.0).
C compiled by gcc 4.8.1, taking best timing from all optimization levels (-O0 through -O3).
C, Fortran and Julia use OpenBLAS v0.2.8.
The Python implementations of rand_mat_stat and rand_mat_mul use NumPy (v1.6.1) functions; the rest are pure Python implementations. These benchmarks, while not comprehensive, do test compiler performance on a range of common code patterns, such as function calls, string parsing, sorting, numerical loops, random number generation, and array operations.
It is important to note that these benchmark implementations are not written for absolute maximal performance (the fastest code to compute To give a quick taste of what Julia looks like, here is the code used in the Mandelbrot and random matrix statistics benchmarks:
The code above is quite clear, and should feel familiar to anyone who has programmed in other mathematical languages.
The Julia implementation of Designed for Parallelism and Cloud ComputingJulia does not impose any particular style of parallelism on the user. Instead, it provides a number of key building blocks for distributed computation, making it flexible enough to support a number of styles of parallelism, and allowing users to add more. The following simple example demonstrates how to count the number of heads in a large number of coin tosses in parallel.
This computation is automatically distributed across all available compute nodes, and the result, reduced by summation ( Here is a screenshot of a web-based interactive IJulia session, using Gadfly to produce various plots with D3 as a rendering backend in the browser (SVG, PDF, PNG and various other backends are also supported): This paves the way for fully cloud-based operation, including data management, code editing and sharing, execution, debugging, collaboration, analysis, data exploration, and visualization. The eventual goal is to let people stop worrying about administering machines and managing data and get straight to the real problem. Free, Open Source and Library-FriendlyThe core of the Julia implementation is licensed under the MIT license. Various libraries used by the Julia environment include their own licenses such as the GPL, LGPL, and BSD (therefore the environment, which consists of the language, user interfaces, and libraries, is under the GPL). The language can be built as a shared library, so users can combine Julia with their own C/Fortran code or proprietary third-party libraries. Furthermore, Julia makes it simple to call external functions in C and Fortran shared libraries, without writing any wrapper code or even recompiling existing code. You can try calling external library functions directly from Julia’s interactive prompt, getting immediate feedback. See LICENSE for the full terms of Julia’s licensing. |
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