Porting C++ SDL Application To Android

In this article I will describe his experience porting 3D editor prototype Cube Art Project on Android.
First, look at the result, the emulator running the editor with 3D cursor red cube:

To build a successful have to do the following:

  1. Install the latest Android SDK and NDK (National Palace of Culture version of the fresher the better).
  2. Download the code SDL2, take out a template to build an application for android.
  3. Add SDL Image, SDL Mixer for assembly.
  4. Add the library of my game engine and toolkit, their dependencies (GLM, JSON for Modern C++)
  5. Adapt the assembly files for Gradle.
  6. Adapt the C ++ code to be compatible with Android, changes were platform specific components (OpenGL ES, the graphics context initialization)
  7. Assemble and test the project on the emulator.

Project Template

Load the source code SDL, SDL Image, SDL Mixer:
The docs folder contains detailed instructions on working with project android template; copy the directory android-project in a separate folder, make a symlink or copy the SDL folder in android-project / app / jni.
Substitute the correct identifier for avd flag, run the emulator from the Android Sdk directory:

cd ~/Android/Sdk/emulator
./emulator -avd Pixel_2_API_24

Specify the path to the script, pick a project:

rm -rf app/build || true
export ANDROID_HOME=/home/demensdeum/Android/Sdk/
export ANDROID_NDK_HOME=/home/demensdeum/Android/android-ndk-r21-beta2/
./gradlew clean build
./gradlew installDebug

Should meet SDL project template with the code in C from a file



Download the source code archives for SDL_image, SDL_mixer:

Load depending on your project, for example, my shared library:

All of this was discharged into app / jni, each “module” in a separate folder, such as app / jni / FSGL. Next, you have the option to find workers generators Application.mk and Android.mk files, I have not found, but maybe there’s a simple solution based on CMake. Click on a link and start to get acquainted with the format of the build files for Android NDK:


You should also read about different APP_STL implementation NDK:

After reviewing create for each “module” file Android.mk, further example of an assembly file shared library Cube-Art-Project:

LOCAL_PATH := $(call my-dir)
include $(CLEAR_VARS)

APP_STL := c++_static
APP_CPPFLAGS := -fexceptions
LOCAL_MODULE := CubeArtProject
LOCAL_C_INCLUDES := $(LOCAL_PATH)/src $(LOCAL_PATH)/../include $(LOCAL_PATH)/../include/FlameSteelCommonTraits/src/FlameSteelCommonTraits

define walk
  $(wildcard $(1)) $(foreach e, $(wildcard $(1)/*), $(call walk, $(e)))

ALLFILES = $(call walk, $(LOCAL_PATH)/src)
FILE_LIST := $(filter %.cpp, $(ALLFILES))
$(info CubeArtProject source code files list)
$(info $(FILE_LIST))

LOCAL_SHARED_LIBRARIES += FlameSteelCommonTraits
LOCAL_SHARED_LIBRARIES += FlameSteelEngineGameToolkit

LOCAL_LDFLAGS := -static-libstdc++

CMake any experienced user realizes this configuration with the first string formats are very similar in Android.mk no GLOB_RECURSIVE, so we have to recursively search for source files with walk functions.

Change Application.mk, Android.mk with-but to build C++ instead of C code:

APP_ABI := armeabi-v7a arm64-v8a x86 x86_64
APP_STL := c++_static
APP_CPPFLAGS := -fexceptions

Rename YourSourceHere.c -> YourSourceHere.cpp, grep for entry, change the path in the assembly, for example:

app/jni/src/Android.mk:LOCAL_SRC_FILES := YourSourceHere.cpp

Next, try to build the project, if you see an error from the compiler about the absence of Heather, check the correctness of the ways in Android.mk; if mistakes are kind of linker “undefined reference”, check the correctness of indications of source code files in assemblies ottreysit lists possible by specifying $ (info $ (FILE_LIST)) in Android.mk file. Do not forget the double-linking mechanism, with modules in LOCAL_SHARED_LIBRARIES key and correct linking through the LD, for example FSGL:


Adaptation And Launching

I had to change some things, for example to remove GLEW of assemblies for iOS and Android, to rename part of OpenGL calls, adding a suffix EOS (glGenVertexArrays -> glGenVertexArraysOES), include macro missing modernistic features debug, the cherry on the cake is the implicit include vulnerability GLES2 Heather indicating macro GL_GLEXT_PROTOTYPES 1 :

#include "SDL_opengles2.h"

Also, a black screen in the first starts with an error type “E / libEGL: validate_display: 255 error 3008 (EGL_BAD_DISPLAY)”, has changed initialize SDL window, GL profile initialization and it worked:

SDL_DisplayMode mode;
int width = mode.w;
int height = mode.h;

window = SDL_CreateWindow(


On the default emulator application is installed with the “Game” icon SDL and name.

I needed to explore the possibility of automatically generating assembly files based on CMake, or else migrate assembly for all platforms on Gradle; CMake but remains the de facto choice for the current development in C++.

Source Code





Flipped World

To develop a new project Cube Art Project has adopted a methodology for the development of Test Driven Development. In this approach, implemented first test for a particular functional application, and then implemented this functionality. A great advantage in this approach, I believe the final implementation of the interfaces, the most uninitiated in the details of implementation, prior to the development of the functional. With this approach, the test dictates the further implementation, added to all the advantages of contract programming when interfaces are contracts for the implementation.

Cube Art Project – 3D editor in which the user builds the shape of cubes, not so long ago, this genre was very popular. Since this graphic application, I decided to add tests to the validation of screenshots.

For screenshot validation you need to get them from OpenGL context first, it is done with the help of glReadPixels function. Description of function arguments are simple – the starting position, width, height, format (. RGB / RGBA / etc.), a pointer to the output buffer, anyone working with SDL or having experience with data buffers in C will simply substitute the correct arguments. However consider it necessary to describe an interesting feature of the output buffer glReadPixels, the pixels stored therein upwards and in SDL_Surface all basic operations taking place downwards.

That is, the reference by uploading a screenshot from the png file, I could not compare the two buffers in the forehead, as one of them upside down.

To flip the output buffer of OpenGL you need to fill it up taking away the height of the screenshot to coordinate Y. However, cost to take into account that there is a chance to go beyond the buffer limits, if not take the unit to fill the time, which will lead to memory corruption.

Since I’m all over the place trying to use OOP paradigm “programming interfaces”, instead of a direct C-like memory access at the sign, when you try to write data outside of the buffer object I reported it to the method of borders through validation.

Final code method of obtaining screenshots in the style of top-down:

    auto width = params->width;
    auto height = params->height;

    auto colorComponentsCount = 3;
    GLubyte *bytes = (GLubyte *)malloc(colorComponentsCount * width * height);
    glReadPixels(0, 0, width, height, GL_RGB, GL_UNSIGNED_BYTE, bytes);

    auto screenshot = make_shared(width, height);

    for (auto y = 0; y(redColorByte, greenColorByte, blueColorByte, 255);
            screenshot->setColorAtXY(color, x, height - y - 1);




Source Code



Longest Common Substring

In this article I will describe an algorithm for solving the longest common substring. Suppose we try to decipher the encrypted binary data, first try to find the common patterns by searching the largest substring.
The input string for example: adasDATAHEADER??jpjjwerthhkjbcvkDATAHEADER??kkasdf
We are looking for a line repeated twice: DATAHEADER??


To begin write method for comparing prefixes of two rows, let returns the resulting string in which a left prefix symbols are the symbols of the right prefix.
For example, for strings:

        val lhs = "asdfWUKI"
        val rhs = "asdfIKUW"

The resulting string – asdf
Example of Kotlin:

fun longestPrefix(lhs: String, rhs: String): String {
        val maximalLength = min(lhs.length-1, rhs.length -1)
        for (i in 0..maximalLength) {
            val xChar = lhs.take(i)
            val yChar = rhs.take(i)
                if (xChar != yChar) {
                    return lhs.substring(0, i-1)
        return lhs.substring(0,maximalLength)

Brute Force

When it is impossible for a good, should resort to brute force. Using the method longestPrefix pass on row two cycles, the first line takes from i to the end of the second i + 1 to the end, transmits them to the search for the largest prefix. The time complexity of the algorithm is approximately equal to O (n ^ 2) ~ O (n * ^ 3).
Example of Kotlin:

fun searchLongestRepeatedSubstring(searchString: String): String {
        var longestRepeatedSubstring = ""
        for (x in 0..searchString.length-1) {
            val lhs = searchString.substring(x)
            for (y in x+1..searchString.length-1) {
                val rhs = searchString.substring(y)
                val longestPrefix = longestPrefix(lhs, rhs)
                if (longestRepeatedSubstring.length < longestPrefix.length) {
                    longestRepeatedSubstring = longestPrefix
        return longestRepeatedSubstring

Suffix array

For a more elegant solution, we need a tool - a data structure called "suffix array." This data structure is an array of substrings filled in a loop, where every substring starts the next character string to the end.
For example for a row:


Suffix array looks like this:


Solve sorting

Sort the suffix array, and then go through all the elements of the cycle where the left hand (lhs) the current item on the right (rhs) and calculate the next longest prefix using longestPrefix method.
Example of Kotlin:

fun searchLongestRepeatedSubstring(searchString: String): String {
    val suffixTree = suffixArray(searchString)
    val sortedSuffixTree = suffixTree.sorted()

    var longestRepeatedSubstring = ""
    for (i in 0..sortedSuffixTree.count() - 2) {
        val lhs = sortedSuffixTree[i]
        val rhs = sortedSuffixTree[i+1]
        val longestPrefix = longestPrefix(lhs, rhs)
        if (longestRepeatedSubstring.length < longestPrefix.length) {
            longestRepeatedSubstring = longestPrefix
    return longestRepeatedSubstring

Time complexity O (N log N), which is much better than brute force algorithm.



Source Code



Insertion Sort, Merge Sort

Insertion Sort

Insertion sort – each element is compared to the previous item in the list and inserted in place of more. Since the items are sorted from first to last, then each successive element is compared with pre-sorted list that *might* reduce the total time. Time complexity O(n^2), that is identical to the bubble sort.

Merge Sort

Merge sort – the list is divided into groups of one element, then the group “merge” in pairs with simultaneous comparison. In my implementation at the merge of pairs elements to the left compared with the right elements, and then moved to the result list, if the items in left are over, then right list elements added to resulting list (their extra comparison is unnecessary, since all the elements in the groups are sorted by iterations)
The operation of this algorithm is very easy to parallelize, pairs merging step can be performed in threads, with thread dispatcher wait.
Algorithm output for single-threaded performance:

["John", "Alice", "Mike", "#1", "Артем", "20", "60", "60", "DoubleTrouble"]
[["John"], ["Alice"], ["Mike"], ["#1"], ["Артем"], ["20"], ["60"], ["60"], ["DoubleTrouble"]]
[["Alice", "John"], ["#1", "Mike"], ["20", "Артем"], ["60", "60"], ["DoubleTrouble"]]
[["#1", "Alice", "John", "Mike"], ["20", "60", "60", "Артем"], ["DoubleTrouble"]]
[["#1", "20", "60", "60", "Alice", "John", "Mike", "Артем"], ["DoubleTrouble"]]
["#1", "20", "60", "60", "Alice", "DoubleTrouble", "John", "Mike", "Артем"]

Algorithm output for multithreaded execution:

["John", "Alice", "Mike", "#1", "Артем", "20", "60", "60", "DoubleTrouble"]
[["John"], ["Alice"], ["Mike"], ["#1"], ["Артем"], ["20"], ["60"], ["60"], ["DoubleTrouble"]]
[["20", "Артем"], ["Alice", "John"], ["60", "60"], ["#1", "Mike"], ["DoubleTrouble"]]
[["#1", "60", "60", "Mike"], ["20", "Alice", "John", "Артем"], ["DoubleTrouble"]]
[["DoubleTrouble"], ["#1", "20", "60", "60", "Alice", "John", "Mike", "Артем"]]
["#1", "20", "60", "60", "Alice", "DoubleTrouble", "John", "Mike", "Артем"]

Time complexity O (n * log (n)), which is slightly better than O (n ^ 2)



Source Code



Bubble Sort in Erlang

Bubble sort is quite boring, but it becomes more interesting if you try to implement it in a functional language for telecom – Erlang.

We have a list of numbers, we need to sort it. bubble sort algorithm runs through the whole list, iterating, and comparing the number of pairs. Upon verification of the following occurs: a smaller number is added to the output list, or change the number of places in this list if the right is smaller bust continues with the next iteration number. This bypass is repeated as long as the list will no longer be replaced.

In practice it is not necessary to use due to the large time complexity – O (n ^ 2); I implemented it in Erlang language in an imperative style, but if you’re interested you can look for the best options:


startBubbleSort([CurrentHead|Tail]) ->
    compareHeads(CurrentHead, Tail, [], [CurrentHead|Tail]).

compareHeads(CurrentHead, [NextHead|Tail], [], OriginalList) ->   
        CurrentHead < NextHead ->
            compareHeads(NextHead, Tail, [CurrentHead], OriginalList);
        true ->
            compareHeads(CurrentHead, Tail, [NextHead], OriginalList)
compareHeads(CurrentHead, [NextHead|Tail], OriginalOutputList, OriginalList) ->
        CurrentHead < NextHead ->
            OutputList = OriginalOutputList ++ [CurrentHead],
            compareHeads(NextHead, Tail, OutputList, OriginalList);
        true ->
            OutputList = OriginalOutputList ++ [NextHead],
            compareHeads(CurrentHead, Tail, OutputList, OriginalList)
compareHeads(CurrentHead, [], OriginalOutputList, OriginalList) ->
    OutputList = OriginalOutputList ++ [CurrentHead],
        OriginalList == OutputList ->
            io:format("OutputList: ~w~n", [OutputList]);
        true ->
main(_) ->
    UnsortedList = [69,7,4,44,2,9,10,6,26,1],

Install and run

In Erlang Ubuntu install is easy, just enter the following command in a terminal sudo apt install erlang. In this language, each file must be of a module (module), a list of functions that can be used outside – export. The interesting features of the language is the lack of variables, only constants, there is no standard syntax for the PLO (which does not prevent the use of OOP techniques), and of course the parallel computing without locks based on actor model.

Start module can be either through an interactive console erl, running one command after another, either through easier escript bubbleSort.erl; For different cases the file will look different, for example escript necessary to make the main function, from which it will start.




Source Code



Lexicographical comparison

lexicographic comparisons rows algorithm works very simple loop compares the character codes and the result is returned if the symbols are not equal.

An example for the C language can be found here:

Keep in mind that you need to compare the characters in a single static encoding, such as Swift, I used the comparison character by character to UTF-32. Array sorting option using memcmp work exactly for single-byte character, otherwise (variable length coding) may order is incorrect. I do not rule out the possibility of implementation on the basis of a variable-length encoding, but is likely to be much more complicated.

Time complexity at most O(1), the average and worst-O(n)



Source Code



Binary Search

Suppose we need to find out whether the email address “ demensdeum@gmail.com ” belongs to the list of allowed email addresses for receiving letters.

We sort through the entire list from the first to the last element, checking whether the element is equal to the specified address – we implement the linear search algorithm. But will it be long or not?

To answer this question, use the “Temporary complexity of the algorithms”, “O” notation. In the worst case, the linear search operation time is equal to the nth number of array elements, we write this in the “O” notation – O (n). Next, you need to clarify that for any known algorithm, there are three performance indicators – execution time in the best, worst, and average cases. For example, the mail address “ demensdeum@gmail.com ” is in the first index of the array, then it will be found in the first step of the algorithm, it follows that the execution time in at best, O (1); and if at the end of the list, then this is the worst case – O (n)

But what about the details of the software implementation, the performance of the hardware, should they affect big O? Now exhale and imagine that the calculation of time complexity is calculated for some abstract ideal machine, in which there is only this algorithm and nothing more.


Ok, it turns out that the linear search is rather slow, let’s try using the binary search. To begin with, it should be clarified that we will not work with binary data, this name was given to this method because of the features of its work. Initially, we sort the array into lexicographically , then the algorithm takes the range of the entire array, gets the middle element of the range, compares it lexicographically , and depending on the result comparison decides what range to take to search for more – the top half of the current or lower. That is, at each step of the search, a decision is made of two possible ones – binary logic. This step is repeated until either the word is found or not found (the intersection of the lower and upper indices of the range will occur).

The performance of this algorithm is the best case when an element is immediately found in the middle of the O (1) array, the worst case is O (log n)


When implementing binary search, I came across not only the interesting problem of the lack of standardization of lexicographic comparison in programming language libraries, but I even found that there is no single standard for implementing localeCompare inside JavaScript . The ECMAScript standard allows different implementations of this function, because of this, when sorting using localeCompare, absolutely different results can be observed on different JavaScript engines.

Therefore, for the algorithm to work correctly, you must sort and use only the same lexicographic comparison algorithm in your work, otherwise nothing will work. So, for example, if you try to sort an array in Scala, and search using nodejs without realizing your own sorting / sorting of one implementation, then you will not expect anything other than disappointment in humanity.



Source Code



Facade Pattern

Facade belongs to structural design patterns. It provides a single interface that provides interaction between client and complex systems. GoF has a good example of the Facade – a compiler of programming languages ​​that provides different clients with different goals, the ability to build code through a single interface of the compiler facade.