- Getting Started with Julia
- Ivo Balbaert
- 522字
- 2021-07-23 20:34:05
The scope of Julia
The core designers and developers of Julia (Jeff Bezanson, Stefan Karpinski, and Viral Shah) have made it clear that Julia was born out of a deep frustration with the existing software toolset in the technical computing disciplines. Basically, it boils down to the following dilemma:
- Prototyping is a problem in this domain that needs a high-level, easy-to-use, and flexible language that lets the developer concentrate on the problem itself instead of on low-level details of the language and computation.
- The actual computation of a problem needs maximum performance; a factor of 10 in computation time makes a world of difference (think of one day versus ten days), so the production version often has to be (re)written in C or FORTRAN.
- Before Julia, practitioners had to be satisfied with a "speed for convenience" trade-off, use developer-friendly and expressive, but decades-old interpreted languages such as MATLAB, R, or Python to express the problem at a high level. To program the performance-sensitive parts and speed up the actual computation, people had to resort to statically compiled languages such as C or FORTRAN, or even the assembly code. Mastery on both the levels is not evident: writing high-level code in MATLAB, R, or Python for prototyping on the one hand, and writing code that does the same thing in C, which is used for the actual execution.
Julia was explicitly designed to bridge this gap. It gives you the possibility of writing high-performance code that uses CPU and memory resources as effectively as can be done in C, but working in pure Julia all the way down, reduces the need for a low-level language. This way, you can rapidly iterate using a simple programming model from the problem prototype to near-C performance. The Julia developers have proven that working in one environment that has the expressive capabilities as well as the pure speed is possible using the recent advances in Low Level Virtual Machine Just in Time (LLVM JIT) compiler technologies (for more information, see http://en.wikipedia.org/wiki/LLVM).
In summary, they designed Julia to have the following specifications:
- Julia is open source and free with a liberal (MIT) license.
- It is designed to be an easy-to-use and learn, elegant, clear and dynamic, interactive language by reducing the development time. To that end, Julia almost looks like the pseudo code with an obvious and familiar mathematical notation; for example, here is the definition for a polynomial function, straight from the code:
x -> 7x^3 + 30x^2 + 5x + 42
Notice that there is no need to indicate the multiplications.
- It provides the computational power and speed without having to leave the Julia environment.
- Metaprogramming and macro capabilities (due to its homoiconicity (refer to Chapter 7, Metaprogramming in Julia), inherited from Lisp), to increase its abstraction power.
- Also, it is usable for general programming purposes, not only in pure computing disciplines.
- It has built-in and simple to use concurrent and parallel capabilities to thrive in the multicore world of today and tomorrow.
Julia unites this all in one environment, something which was thought impossible until now by most researchers and language designers.
The Julia logo
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