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Table of Contents
Julia and Python are [pretty similar programming languages – open-source, dynamically typed, support for data science and machine learning, and easy to learn and use. Most beginner programmers will find both languages friendly. However, determining which languages work best for you is a matter of preference and your objectives.
Python has been around for 30 years; that makes it a very mature language. It can offer a lot of features that modern-day languages such as Julia cannot provide. Nonetheless, the creation of Python was meant to solve problems at that time. But its developers are always in touch with current needs; thus, they invest in libraries and tools relevant to the current challenges.
Over the past three decades, Python users have hailed it for better performance than other similar languages. That’s why Python is rated among the top popular programming languages. Currently, Python has over 8 million developers worldwide, and it has been used at almost all levels of coding.
The popularity alongside powerful features does not make Python an all-around language, however. There have been speed issues. Python is much slower though it produces clean code. Speed is critical in programming – the demand for efficient code production at high speed is increasing. Besides, it goes hand in hand with quality.
To address the problem of speed in Python, Julia developers targeted the development of a language that has a higher speed than Python but can cater to the needs of Python users, especially those in research and data science. Julia was born about 10 years ago.
Julia has a speed comparable to C. Typically, programmers term it a memory hog.
So, is Julia faster than Python? Thanks to its JIT compilation and type declarations from the stats, Julia has a higher speed than Python.
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Julia programming language
Julia was designed to excel at numerical and scientific computing. This open-source, high-level, reproducible, disposable dynamically typed programming language has excellent application in specialized domains like machine learning, visualization, and data science. Nevertheless, the language can also be used in general-purpose programming.
Julia gives near-C performance and feels like Ruby or Python with optional type annotations – you can feed these notations to one of the two static analysis tools. Moreover, Julia has excellent support for macros and adequate support for FP. Also, Julia supports parallelism without the need for third-party tools. Three parallelism levels exist in Julia – green threading (Julia coroutines), multi-threading, and distributed/ multi-core processing.
Julia has a unique twist – the development convenience of a dynamic language, and it offers the performance of a compiled statically typed language, partly due to the Just-in-time (JIT) compiler based on LLVM, which generates native machine codes. Another reason type stability implemented via multiple dispatches, making it easy to compile to efficient code.
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The creation of Julia was to eliminate the trade-off among Matlab, Python, Perl, Ruby, Mathematica, Lisp, R, and C. So, they wanted one programming language that could be great at scientific computing, data mining, machine learning, parallel computing, large-scale linear algebra, and distributed computing.
Therefore, Julia aims to bring into light a combination of efficiency, power, and ease of use in one Language. The standard Julia features include Multiple dispatches, built-in package manager, Lisp-like macros, strong shell-like abilities, support for parallel and distributed computing, support for Unicode, faster and compact user-defined types built-ins, and support for parallel and distributed computing.
The official Julia distribution features an interactive command-line read–eval–print loop (REPL) featuring a tab-completion, searchable history, and dedicated help & shell modes, which test and experiment codes faster. REPL allows access to the system shell and to help mode.
Is Julia easier than Python?
Choosing between Python and Julia can be difficult because both languages offer unique features to enable developers to accomplish their tasks. So each Language has its advantage and disadvantages.
While choosing a programming language depends on the programmer’s needs, using some languages can be challenging, especially if they are hard to grasp. Newbies will want a pretty straightforward language, usually categorized as high-level languages. Even so, high-level languages are within different categories.
Python and Julia are both high level languages implying that they are somewhat easy to learn. But what are the real features of an easy-to-learn programming language?
- Clarity and simplicity – all languages have a conceptual framework for planning algorithms and expressing them. Thus, it should offer straightforward concepts. Ideally, it has a minimum number of needed concepts and rules for combining simple and clear – conceptual integrity.
- An appropriate Programming Environment –adds extra utility and eases the process of implementing the program.
- A reliable, efficient, and accurate documentation
- Ease verification of program (Reusability)
- Portability of the developed programs, i.e., easy to transfer a program from one computer to another
Do Python and Julia offer these features?
High-Level Language: both languages are high-level languages, so developers don’t need to memorize the system architecture or manage memory.
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Dynamically Typed Language: both are dynamically types – this feature implies that variable types are determined at run time; thus, developers need not specify the types variables. This makes the entire coding process much easier.
So, which is easier: Python or Julia? Both languages are easy. However, your coding experience comes into play.
Julia vs. Rust
Rust and Julia are potent languages that can be put to various uses. However, they have different features. Rust is a multi-paradigm, statically typed language focused on performance and safety, more so concurrency. Additionally, it offers memory safety without employing garbage collection. Its syntax is similar to C++. The bulk of Rust applications is in systems and application development, which needs excellent performance alongside safety.
On the other hand, Julia is a high-level, high-performance dynamically typed Language. It employs a JIT compiler, is garbage-collected, and offers an interactive REPL environment. Though a general-purpose language, Julia is tailored for numerical analysis and computational science.
How similar are these languages?
Both languages use a low-level virtual machine (LLVM); thus, compilation performance is similar. But Julia includes an interactive REPL for comfortable experimenting, a feature absent in Rust.
What are the differences?
Features a robust ecosystem for scientific computing and data analytics. Developers get lots of libraries for scientific computing and graphical visualization.
Mainly used for systems programming in areas where stability and memory safety are critical.
It uses a JIT compiler
Utilizes an ahead-of-time compiler
Has a garbage collector
It lacks a garbage collector
Its focus is on the ease of use
Focus is preventing segfaults and offering thread-safety
What are the benefits of Julia?
- Its ecosystem is tailored for scientific computing
- It has multiple dispatches alongside duck typing, allowing for very composable code.
- Very interactive REPL
- Highly expressive
- Excellent package management and reproducible builds
- Compiling a single executable is straightforward
- Arrests problems already at compile time
- More stable with fewer breaking changes
Both languages are general-purpose; however, your choice should be guided by the job at hand. You can use Rust for data analysis and Julia for system programming, but that needs dedication. Else, choose the right tool for each job.
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Julia vs. C++
C++ is an object-oriented programming language featuring imperative and generic programming features ideal for creating large-scale applications and is a superset of C language. C++ also offers facilities for low-level memory manipulation and compiles directly to a machine’s native code. This makes C++ a faster language if well optimized.
C++ is an intermediate language, considering that it had features of both high and low-level programming languages.
Contrarily, it is designed for technical computing – its high level, high performing, and dynamic. Its syntax is familiar to users who have use languages that support technical computing. Developers in Julia will meet a sophisticated compiler, numerical accuracy, distributed parallel execution, and an extensive mathematical function library. Though a dynamic language, Julia offers performance comparable to statically-typed languages. Its single productive environment is enough for prototyping and efficient for industrial-grade applications.
Comparing C++ and Julia:
C++ uses pointers to allow self- memory management, thus enhancing the execution speed of programs. However, developers must explicitly free up reserved space later. This also applies to Julia, but you can wrap the trivial Julia type around the C pointer, make the Data struct contain pointers to the wrappers, and then perform the pointer.
Approach: C++ has an object-oriented approach; however, developers can code in C style or either way, making it a hybrid language. Contrarily, Julia is not an object-oriented Language as you can’t attach methods to Julia’s objects. However, types are very similar to objects.
High performance: C++ developers manipulate processors on a lower level, making them faster than advanced languages like Julia and Python. However, that will much depend on the type of task at hand. For graphic performance, C++ is better.
Speed: Julia has a much higher speed than C++. Test results show that Julia is twice faster than C++.
Libraries: C++ has a rich library compared to Julia because it has been around longer than Julia.
Python vs. Julia 2021
Though a recently released language, Julia is experiencing massive adoption across the globe. This open-source Language with roots at MIT attracts users from all spheres but more so from the research and scientific world. According to statistics, the new users of Julia in 2020 exceeded 24 million – this was an 87% jump from the previous year. Does that mean that Julia is the next real deal?
The many downloads show that Julia is growing. With time, it is thought that Julia can replace the famous Python, C, and C++ in scientific and technical computing – but this will happen only when it matures (it’s much younger than these languages).
The creators of Julia intended to provide an easy-to-use yet powerful Language for scientific computing. The twist is; Julia is dynamic, a paradigm associated with lesser performance, unlike static languages that offer higher performance. Nonetheless, high-level languages emphasize the speed of development, abstraction, and portability – there is tension.
In 2021, Python will mark three decades of existence. Over the years, it has experienced a massive 456% growth – no other language has experienced this. In 2020, Python was selected by leading tech companies, including Google and Reddit, Instagram, Facebook, Netflix, PayPal, and Dropbox – this is a considerable achievement. These techs chose Python because it’s a multi-purpose language that can handle many tasks – scientific computing, mobile and web app development, game development, and system administration.
The high rate of Julia adopting is indeed encouraging for the Julia developers. The developers are looking into the shortcomings to ensure that they create a high-performing program across the board. The idea here is to make Julia a self-contained ecosystem rather than a stand-alone program.
Julia attempts to solve problems that arise from other dynamic languages. This is seen as its departure from such languages – for instance, automatic code generation, an approach of statically compiled languages, and multi-dispatch features.
Is Julia as fast as C?
C is the fastest programming language. Typically, it is a
systems programming language, which is an alternative to assembly; thus, it is designed to be compilable to swift machine code -C is unique as most CPU architectures have a design that supports running the C language.
C is designed to be faster. However, if you don’t know how to use it, you can create programs that run much slower than the other programming languages. C allows developers to do lots of developments allowed in low-level programs – this helps the compiler optimize the code.
Compared to Julia: Julia is faster, but its focus is more n productivity – a developer’s time is more expensive than machine time. Thus, there is a lot of sense in minimizing the time coders spend on creating codes and debugging programs rather than the running time of the program.,
Julia is a compiled language, primarily written in its base. However, it differs from other compiled languages like C because it’s compiled at runtime – others are compiled before execution. While Julia is fast -comparable to C, it can run much faster than C if it is well written. Note that it uses the Just In Time (JIT) compiler, which compiles incredibly fast.
It’s worth noting that high-level programming languages run much slower than low-level programming languages (assembly languages). C is a mid-level language that features these two languages’ characteristics, making it slower than assembly languages. But since it runs on Java Virtual Machine (JVM) in the background, it is much faster than other programming languages.
So, is Julia as fast as C? Julia can give performances comparable to C if it is well written. However, C remains the fastest Language as it gives a coder absolute freedom to accomplish the task.
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Is Julia faster than NumPy?
Julia developers’ promise to its fans is that it offers the ease of a dynamic language like Python and a speed near that of a compiled language like C. So, Julia will perform better than Python in some aspects and better than C in other aspects.
The design that Julia’s JIT allows developers to write functions without type information makes it a pretty efficient language. Calling a function for the first time during program execution prompts the compiler to inspect the function types arguments and compile a particular version for those specific types directly to the native code. The following calls with the same types use the already compiled version.
Python/NumPy version is much faster than its equivalent Julia function.
This Python version, 57.8 ms, was significantly faster than an equivalent Julia version, 104.7 ms. Nevertheless, you can speed up the operation by unwrapping the lop to avoid creating a temporary array x * y before summing. Julia allows this operation without requiring compilation extension, and it yields 36 ms. However, for Python, developers compile with a C extension using a tool such as Cython, yielding 14.5 ms, which is 2.5 faster than the best Julia version.
The release of the latest Julia version, however, sells out some admirable improvement. The current version is about 1.4 x slower than Python. The exciting part is performance scales with arrays.
For arrays up to 1000 elements (small arrays), Julia is much faster than NumPy. However, for arrays up to 100 000 elements NumPy surpasses the Julia performance. Julia’s performance is about 4X slower than NumPy.
But for the largest arrays, Julia catches up with Python again.
Julia and NumPy have similar performance for many tasks ideally – the bottleneck is heavy lifting accomplished by the underlying algebra libraries. Since Julia supports loop functions, it a bit faster in some functions.
Is Julia language dead?
Julia programming language isn’t dead. However, users rep[ort some issues with using Julia. Julia can sometimes be too slow to generate the first plot besides a slow compile time for most users. Again, most users complain that its packages aren’t mature enough. This is an aspect that sets it apart from the python ecosystem. Also, coders cannot generate self-contained binaries or libraries.
Can these issues cause the death of Julia?
Julia was created to revolutionize the technical computing and data science field. Its creative and versatile design was to run like language C and seamlessly integrate with other languages such as Fortran and do things that R does any clunkiness. Besides, Julia is to feel like Python but to be homoiconic like Lisp.
Sounds like too much for one Language. Ideally, this means that developers can use a single language to perform many tasks, which would end the programming language wars – a perfect lingua franca.
In the TIOBE index, Julia has risen from position 47 to position 23 and is expected to break into the top 20 in 2021. For languages developed on GitHub, Julia ranks position 7 in stars and position 9 in forks. Besides, Julia ranks position 24 in the PYPL Index and position 19 in the IEEE Spectrum ranking. This performance is impressive, considering that Julia has been around for only a few years.
Julia fills the small gap in data science where Python and R are very slow while C++ is too cumbersome. This is where Julia gets its life. While not dead, Julia still doesn’t fulfill the promises its developers said it would confer to users when they first released it.
Good thing; there are so many committed Julia developers who work significantly harder to expand the Julia ecosystem. Besides, it will need more time to develop into a superb language.
Speed is an essential aspect of programming. Nonetheless, for a language to have a faster speed, it is a combination of several factors. Julia is a compiled language and isn’t interpreted. That implies that its programs are executed directly as executable code. All Julia codes are executable with C, R, Python, etc., providing efficient, impressive, and rapid results with no need for optimization and native profiling techniques. However, some optimization with Julia cannot be used in Python.
Julia has a faster runtime performance as it uses the JIT compiler using the LLVM compiler framework. When perfectly compiled, Julia has a speed comparable to C. C is much faster than Python. So, when comparing Julia and Python in terms of speed, Julia is much faster. Note that compiled languages are much faster than interpreted languages because they are directly converted into machine code that a processor can execute.
That’s why the time taken to execute complex and significant codes in Julia is much faster than in Python. While Python might take the same time to implement codes, it requires multiple optimization methods and third-party libraries. Speed was the reason Julia was created, and it doesn’t disappoint in that aspect.
Besides, Julia has a math-friendly syntax making it ideal for MATLAB users, Mathematica, Octave, R, etc., and environments. Since Julia has its native machine learning libraries, it’s expected to attract more data scientists in the future. Although Julia has a community, it isn’t as massive as Python’s; the members are dedicated. The community is, however, expected to grow in the future.
Julia is attracting many developers to ensure that it has the best tools and libraries to handle scientific challenges better. This is an area in that Python has moves leaps forward, thanks to its three-decade service.
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