http://julialang.org/

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.

Julia’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. The source code for the various implementations can be found here: C, Fortran, Julia, Python, Matlab/Octave, R, and JavaScript. 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 are from a MacBook Pro with a 2.53GHz Intel Core 2 Duo CPU and 8GB of 1066MHz DDR3 RAM:

FortranJuliaPythonMatlabOctaveRJavaScriptGCC 4.5.112b1d5a72.7.3R2011a3.42.14.2V8 3.6.6.11fib0.281.9746.031587.032748.74275.632.09parse_int9.221.7225.29846.677364.87353.482.55quicksort1.651.3769.20133.463341.94708.764.95mandel0.761.4534.8874.61988.74184.717.62pi_sum1.001.0033.641.46457.26253.451.12rand_mat_stat2.231.9529.017.7131.0412.665.53rand_mat_mul1.141.001.751.081.939.5845.82Figure: benchmark times relative to C (smaller is better).

C compiled by Clang 4.0, taking best timing from all optimization levels (-O0 through -O3).The Python implementations of rand_mat_stat and rand_mat_mul use NumPy (v1.5.1) functions; the rest are pure Python implementations.