When comparing Assembly vs Julia, the Slant community recommends Julia for most people. In the question“What is the best programming language to learn first?” Julia is ranked 12th while Assembly is ranked 49th. The most important reason people chose Julia is:
Julia runs almost as fast as (and in fact in some cases faster than) C code.
Specs
Ranked in these QuestionsQuestion Ranking
Pros
Pro Low Level - it's how the computer works
One of the best ways to learn how a computer actually works is to work your way up from lower level abstractions. Assembly, being only a level above machine code, is low enough level to make it clear how the computer is actually performing a computation, including code flow and loops, but high enough level to not be excessively tedious for the type of small projects that a student would do at the beginning of their first programming class. Use of an assembler with macros can stretch this even a bit further.
Pro Naturally creates fast and small programs
Because of its natural syntax and low-level nature, assembly language programs are typically really small and fast.
Unlike other programming languages, in assembly language it is really hard to create a slow and over-bloated program.
Pro You must look into it if you really want to understand what computers do
There is no other way to understand a processor, so dig in.
Pro Useful for embedded systems
A curriculum that involves an embedded component, such as an Arduino or a Raspberry Pi, can encourage students by allowing them to immediately connect their work with 'real systems'. Assembly is the ideal language for getting started with and understanding these devices, and since Assembly can be called from C, the code will still be useful if students move on to C later in the program.
Pro Uniform syntax
Assembly language syntax is relatively uniform, and so there's less room for a student to get confused by obscure characters, or miss some meaning implied by structure, such as with scoping rules, or call-by-name/value/reference semantics. While there may be a lot of mnemonics to look up, most work involves only a very small subset of them.
Pro Almost as fast as C
Julia runs almost as fast as (and in fact in some cases faster than) C code.
Pro Strong dynamic typing
Dynamic and high level, but does not isolate the user from properly thinking about types. Can do explicit type signatures which is great for teaching structured thinking.
Pro Great standard REPL
Out of the box Julia has a very good Read-Eval-Print-Loop, which both completes functions and types, as well as completion based on history of previous statements. It integrates well with the shell and has an excellent online help system.
Pro Nice regular syntax
Julia code is easy to read and avoid a lot of unnecessary special symbols and fluff. It uses newline to end statements and "end" to end blocks so there is no need for lots of semicolons and curly braces. It is regular in that unless it is a variable assignment, function name always comes first. No need to be confused about whether something is a method on an object or a free function.
Unlike Python and Ruby, since you can annotate the types a function operates on, you can overload function names, so that you can use the same function name for many data types. So you can keep simple descriptive function names and not have to invent artificial function names to separate them from the type they operate on.
Pro Written in itself
The Julia language is written in itself to a much larger extent than most other languages, so a budding programmer can read through the depths of the standard library and learn exactly how things work all the way down to the low-level bit-twiddling details, which can be englightening.
Pro Powerful n-dimensional arrays
Julia has built in n-dimensional arrays similar in functionality as Python's numpy.
Pro Function overloading
You can have multiple functions with the same name, but doing different things depending on function arguments and argument types.
Pro Amazing learning curve
Julia requires no boilerplate code – a beginner needs to write only the parts they care about. This combined with the REPL provides the best learning experience available.
Pro High-level code
Julia provides a high level of abstraction, making platform-independent programming trivial and easing the learning curve.
Pro Function and operator broadcasting
You can perform operations on scalars, for example 2^2 or [1, 2, 3].^2.
Pro Strong Metaprogramming
Julia allows you to edit Julia code in the language itself and write powerful macros. It is a great introduction to metaprogramming features
Pro REPL-based
The Julia REPL allows quickly testing how some code behaves and gives access to documentation and package management immediately in the REPL.
Cons
Con Difficult learning curve
Starting off as a beginner with assembly language could prove very daunting. I suggest learning a high level language first (e.g. C) to get a good grasp of programming - especially dealing with bits, bytes, numbers, accessing memory with pointers (which is why I suggest C).
Then once that person is comfortable writing C (or whatever high level language) programs, they would find moving to assembler a little less of a "What the hell?!!!" experience.
Con Rarely a requirement or used in professional employment
(except for experts, which will outperform you in assembly language and execution speed on any day of the week, simply because they have full control of the processor.)
Con Not very portable
The instruction set may change depending on what CPU architecture is being used. Also, there will be some differences in implementations due to different assemblers being used, such as with changes in OS.
Con Language for those sadists that like pain
Not recommended as a first language. However, in small doses to show how higher level code is executed, can be of some value. Is also a language that will take a longer time to learn or finish projects with, so usually not for those who are in a hurry to get anything in particular done.
Con Hyperspecific syntax isn't a good first step to learning other modern languages
Con Young language with limited support
Julia was released in 2012. Due to its short existence, there is a limited amount of support for the language. Very few libraries exist as of yet, and the community is still quite small (though growing quickly).
Con 1-based array and column major
This design probably comes from Matlab, but makes it unnatural to interface C and C++ and python.