Why Compiled Languages?

This is a very simple experiment to demonstrate the importance of compiled languages over dynamic or interpreted languages when it comes to system programming or high degree of efficiency.

Experiment

A simple loop operation to sum up the numbers from 1 to 1,000,000,000 (one billion).

Execution

Clone the repo, then open src/ directory.

C++ run:

$ g++ cplusplus.cpp -o cpp -O3
$ ./cpp

Java run:

$ javac java.java
$ java Java

C# run: Note: use mono on macOS/linux only.

$ csc csharp.cs
$ mono csharp.exe

Python run:

$ python python.py

Ruby run:

$ ruby ruby.rb

Javascript (node) run:

$ node javascript.js

Go run:

$ go build go.go
$ ./go

Evaluation

To obtain the time spent on execution, measure the last line of each run with time multiple times and get the average.

For example:

$ time java Java
...
real    0m0.395s
user    0m0.361s
sys 0m0.028s

$ /usr/bin/time -l java Java
...
22679552  maximum resident set size

Results

Results breakdown:
(macOS mid 2015, 2.5 GHz Quad-Core Intel Core i7, 16 GB RAM)

Language	Elapsed Time (second)	Memory (MB)
C++	0.015	7.49
Java	0.39	23
C#	1.172	11.88
Ruby	21.77	13.45
Python	17.89	4.66
JS (node)	0.873	22.97
Go	0.284	1.56

Clearly, the way of optimization of this pure calculation logic in compiled versions as we see in static compiled languages outperformed the interperted languages (except nodeJs) drastically in terms of speed. Additionally, Java and C# results show the outstanding optimization made to the compiler and runtime JVM/CLR to perform nearly identical to the low level languages, for such essential looping computing.

The two figures below show the comparison between C++, C#, Java, Ruby, and Python languages in terms of both speed (time) and memory (space).

Contribution

All PRs are welcome for other languages or improvements on Github.

License

MIT.

Contributors

Abdullah Barrak (@abarrak).
Eliot Akira (@eliot-akira).