This is the sixth post of a serie where I’ll talk about some new programming languages with a nice future ahead. During this series I’ll explore deeper the Ceylon, Dart, Elixir, Rust and Swift 2 languages.
The plan is to make a post a week until December 15 (or maybe a little more) and spend 2 weeks exploring each one of them. In the first week I’ll explore the general aspects of the language and make some comparisons with other very known and established languages. In the post of the second week I’ll go deeper inside each one of the languages and explore the individual advantages on use them.
All the posts
- Ceylon Introduction
- Ceylon Usage
- Dart Introduction
- Dart Usage
- Elixir Introduction
- Elixir Usage
- Rust Introduction
- Rust Usage
There are hundred of programming languages out there. Which one should we use? Which help do we have to choose well? How do they compare to each other? This document is an attempt to provide some answers to these questions. Naturally, it would not be possible to provide complete answers: as I mentioned, there are too many programming languages. Nevertheless, we chose five languages with a potential to grow in importance in the coming years. These programming languages are Elixir, Rust, Dart, Swift and Ceylon. During this project, we shall be talking about each one of them. These discussions will be in breath, not in depth. Their goal is to provide the reader with the minimum of information necessary to compare them, and who knows, to lure one or other interested person in learning them in a greater level of details. In any case, we hope to contribute a bit to the popularization of these programming languages, which - likely - will be paramount to the development of computer science in the next ten years.
On this usage example, we’ll use most of the concepts on the last article to build a more complex program that explores the concurrent features of Elixir. Starting with a Fibonacci function we’ll increment the program touching concepts that were not in the first article like pipes, processes, comunication between processes and tail call optimization.
Let’s begin with a canonical starter recursive Fobonacci implementation with Elixir. Here is the code:
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Nothing new here, just right to the point. Just write the problem’s definition and we got the right result:
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Many Fibonacci numbers
Let’s improve our last code to generate many numbers passing them as a list to the function. Here is the code:
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The |> symbol used in the snippet above is the pipe operator: it simply takes the output from the expression on its left side and passes it as the first argument to the function call on its right side. It’s similar to the Unix | operator. Its purpose is to highlight the flow of data being transformed by a series of functions. The run function without the pipe operator would be:
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The Enum.map function calls the function on the second parameter for each element in the list passed as the first parameter returning a new list with the transformed elements. The inspect function returns a String witht the list “pretty printed” (written in a human readable format) and we print the list with the IO.puts function. The result of the execution, for whateaver run implementations is:
Parallel Fibonacci numbers
In our last example, we run the function that calculates each Fibonacci’s number sequentially. The execution waits each of the numbers to be calculated before calculate the next number. Running this program for some bigger numbers would be slow. Let’s measure the time to calculate the Fibonacci numbers from 30 to 40:
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Now I’ll rewrite the program to calculate these numbers using data parallelism. Our code will distribute the number calculation across different parallel computer cores possibly making the execution faster. Here is the code:
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Our already existent run function was modified. The Enum.with_index method returns the collection with each element wrapped in a tuple alongside its index. This way we can identify this number later to return them in the correct order. The Enum.map function that used to call the fib function now calls a spawn_run function. At the end of the run function it calls a receive_fibs function.
The spawn_run function just spawn off new processes1 of execution. The Elixir’s spawn function takes a function which it will execute in another process. It returns a PID (process identifier). The spawned process will execute the given function and exit after the function is done. The program uses the send and receive functions to comunicate between different proccesses. The send method receives a PID and a message and sends the message to the proccess with the referred PID. The receive function has one parameter that must be a function that will receive the potentially message sent.
So in our code, the spawn_run is called (for each one of the numbers we want the fibonacci number) and it calls a send_run function. The send_run function sends back a result of our already known fib functions. The receive function is used inside the receive_fibs function. receive_fibs works as a synchronization function that awaits the execution of all parallel fib calls, adding them to a list and returning this list when there is no more proccesses running.
When all the execution is over, the receive method calls the print_fibs function. This function orders the calculated number by its index (mentioned on the run function). This is necessary because the result of the parallel and asynchronous execution times are not deterministic and the order it returns the numbers is unknown. At last, the tuples with indexes are removed and just the result of the Fibonacci numbers are printed on the screen.
Now, this is the execution time of the parallel version of the program:
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The real execution time of the parallel program is almost the half of the execution time for the sequential version of the code. This time would become even better if the machine that executes the program has a processor with a bigger number of execution cores.
Tail call optimization
Tail-call optimization is where you are able to avoid allocating a new stack frame for a function because the calling function will simply return the value that it gets from the called function.
The parallel portion of the implementations just remains the same. The modification is on the fib functions that now has a tail call, avoiding so many recursion calls. Here is the code:
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It might be more ilustrative if shown in a non-functional language, like Python:
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We can see that now, the real execution time of our program is very smaller than our two last versions:
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