Trying to turn an ARM-64 -based, Android-hosted, prooted Linux Guest System, into a software development platform.

In a preceding posting I described, how I had used an Android app that does not require or benefit from having ‘root’, to install a Linux Guest System on a tablet, that has an ARM-64 CPU, which is referred to more precisely as an ‘aarch64-linux-gnu’ architecture. The Android app sets up a basic Linux system, but the user can use apt-get to extend it – if he chose a Debian 10 / Buster -based system as I did. And then, for the most part, the user’s ability to run software depends on how well the Debian package maintainers cross-compiled their packages to ‘AARCH64′. Yet, on some occasions, even in this situation, a user might want to write and then run his own code.

To make things worse, the main alternative to a pure text interface, is a VNC Session, based on ‘TightVNC’, by the choice of the developers of this app. On a Chromebook, I chose differently, by setting up a ‘TigerVNC’ desktop instead, but on this tablet, the choice was up to the Android developers alone. What this means is, that the Linux applications are forced to render purely in software mode.

Many factors work against writing one’s own code, that include, the fact that executables will result, that have been compiled for the ‘ARM’ CPU, and linked against Linux libraries! :-D

But one of the immediate handicaps could be, that the user might want to program in Python, but can’t get any good IDEs to run. Every free IDE I could try would segfault, and I don’t even believe that these segfaults are due to problems with my Python libraries. The IDEs were themselves written in Python, using Qt5, Gtk3 or wxWidgets modules. These types of libraries are as notorious as the Qt5 Library, for relying on GPU acceleration, which is nowhere to be found, and one reason I think this is most often the culprit, is the fact that one of the IDE’s – “Eric” – actually manages to report with a gasp, that it could not create an OpenGL rendering surface – and then Segfaults. (:3)

 

(Edit 9/15/2020, 13h50: )

I want to avoid any misinterpretations of what I just wrote. This does not happen out of nowhere, because an application developer decided to build his applications using ‘python3-pyqt5′ etc… When I give the command:

 


# apt install eric

 

Doing so pulls in many dependencies, including an offending package. (:1) Therefore, the application developer who wrote ‘Eric’ not only chose to use one of the Python GUI libraries, but chose to use OpenGL as well.

Of course, after I next give the command to remove ‘eric’, I also follow up with the command:

 


# apt autoremove

 

Just so that the offending dependencies are no longer installed.

 

(End of Edit, 9/15/2020, 13h50.)

 

Writing convoluted code is more agreeable, if at the very least we have an IDE in front of us, that can highlight certain syntax errors, and scan includes for code completion, etc. (:2)

Well, there is a Text Editor cut out for that exact situation, named “CudaText“. I must warn the reader though, that there is a learning curve with this text editor. But, just to prove that the AARCH64-ported Python 3.7 engine is not itself buggy, the text editor’s plug-in framework is written in Python 3, and as soon as the user has learned his first lesson in how to configure CudaText, the plug-in system comes to full life, and without any Segfaults, running the Guest System’s Python engine. I think CudaText is based on Gtk2.

Screenshot_20200914-124954_VNC Viewer

This might just turn out to be the correct IDE for that tablet.

 

(Updated 9/19/2020, 20h10… )

Continue reading Trying to turn an ARM-64 -based, Android-hosted, prooted Linux Guest System, into a software development platform.

Installing Visual Studio Code under Linux.

Linux users have often been avid followers, but left thirsting for some ability to run the proprietary applications, that Windows and Mac users have had access to since the beginning of Computing for the Masses. In fact, the narrow supply of Open-Source Applications for various Linux distributions has been aggravated by the fact that many Linux distributions exist, and when one follows the subject to its smallest detail, one finds that every Linux computer evolves into a slightly different version of Linux, because they can all be configured slightly differently, which means that some users will configure their Linux boxes in their own, personalized way. Actually, this is not a very good thing to do, unless you really know what you’re doing. But the mere fact that many, professionally configured Linux distributions exist, has also meant that packages destined for one distribution would either not install on another, or that packages which were not meant to be installed on a given distribution, could actually break it, if the user supplied his ‘root’ privileges, to so-install the package anyhow.

At the same time, the total amount of programming time available to open-source development has always been scarce, which means for the sake of this blog posting, that programming hours ended up divided between different Linux distributions. (:2)

In recent Linux distributions, there have been two main mechanisms developed over the years, to reduce the severity of this problem. In fact, since Debian 9 / Stretch, both these solutions have been available:

  • Flatpaks,
  • Snaps.

For the moment, I’m going to ignore that Flatpaks exist, as a very viable way to install software, just because Flatpaks had as their main purpose, to install purely Linux software, but on a wider variety of Linux distributions. So, why do both ‘Flatpak’ and ‘Snap’ exist? I suppose that one reason they both exist is the fact that each was invented, and that in principle, both work. But another reason why these two vehicles exist is, the fact that ‘Snaps’ are really disk images, which get mounted as loopback devices, and that therefore, ‘Snaps’ can install software which is binary in nature and therefore, not open-source, yet, install such software on a Linux computer, where the emphasis has traditionally been on open-source software. (:3)

Both mechanisms for installing software have a limited interface, of which features on the host computer the guest application is meant to have access to, since, if both methods of installing software were completely unrestricted, Linux users would lose the security which they initially gained, through their choice of Linux. I think that the way it is, ‘Snaps’ tend to have more-severe security restrictions than ‘Flatpaks’ do, and this is also how it should be.

What all of this inspired in Linux users, was the hope that eventually, they would also start to be able to install certain proprietary applications. And, the main goal of this posting is to assess, to what extent that hope seems to have been materializing. And I’m just going to ignore the fact for the moment, that some ‘Snaps’ are really just Linux applications, which their programmers compiled to the additional target, that being a ‘Snap’, and that for this reason, some Snaps just don’t work, usually because their programmers did not take into consideration that on an eventual host computer, each Snap only has access to the Interfaces which the security model allows, even though, when residing on Linux computers natively, the same application ‘just works fine’. For the sake of argument, software developers might exist, who are professional enough in what they do, to compile Snaps as Snaps, which in turn do work as intended.

An idea which could make some Linux users uneasy would be, that the supply of proprietary software available as Snaps, may not have grown as much as hoped, and that Linux users could be looking at a bleak future. Well, in order to get a full idea of how many Snaps are presently available, user can just visit ‘the Snap store’, and browse to see what it has to offer. And this would be the URL:

https://snapcraft.io/

What most Computer Users would seem to notice is the fact, that there is not a huge abundance of software, at least, according to my tastes, and at the time I’m writing this. Also, users do not need to pay for anything at this so-called Snap store. However, I have at least one Snap installed, of which I know, that if I activated that, I’d need to make a one-time payment to its developers, before it would actually function as one user-license.

What I’d just like to explore for the moment is the possibility that a User might want to program and compile code he wrote himself, in his favourite language, such as, in C / C++, or in C#, and that additionally, said user might prefer “Visual Studio Code” as his Editor, as well as his IDE. In reality, Linux users do not depend very strongly on the ability to use ‘VSCode’, as it’s also called, because under Linux, we actually have numerous IDEs to choose between. But let’s say I wanted to write code in these 2(3) languages, and, to use ‘VSCode’ to do so…

(Updated 5/04/2020, 17h50… )

Continue reading Installing Visual Studio Code under Linux.

Whether it would be fair to expect, that the Debian libc6-dev package work, on an ARM-64 CPU-based device.

One of the facts which I had posted about before was, that I had installed Debian 10 / Buster on a Google Pixel C Tablet, not because that tablet has any special properties, but just to document that with that one specific configuration, the solution ‘works’. And I had gotten to the subject of wanting to install ‘libc6-dev’, which would normally install Development Libraries, on top of Run-Time Libraries, with the ultimate intention of being able to compile or custom-compile C or C++, from in front of this ARM-64 -CPU Device, for use on the same device. And even one major Debian Update later, from 10.0 to 10.1, this facility still doesn’t work.

What I’d like to comment is the idea, that this is not a fair expectation, and that the naming of these packages cannot always be expected to remain canonical. What this expectation would assume is that the general-purpose GNU Compiler will work, even though that compiler is highly optimized for targeting code that runs, either on ‘amd64′ or ‘i386′ architecture, in that order.

If the goal really was, to compile code from in front of an ARM-64 -based machine, to run on it, then a compiler would need to be selected which is meant to target the ARM-64 CPU, and this might involve installing the correct cross-compiler, even though it’s to be executed on an ARM-64. The fact that an ARM-64 version of ‘libc6-dev’ is available, really just stems from the rather nonsensical idea, that the compiler using it should run on an ARM-64, but that the linked code should not.

And then, if one has installed the correct cross-compilers, because those packages are available in ‘arm64′ versions, they will run in spite of being named cross-compilers, and then installing them will also pull in the correct development libraries. Only then, in order actually to compile anything, one would need to specify yay-long commands from the command-line. And the main reason I’ll have none of this, is the simple fact that entering many non-standard ASCII characters using an Android-oriented keyboard, does not appeal to me for the moment.

This is similar to why I don’t install ‘Web-development software’, that is compiled and available from the repositories, but that would require a long sequence of special characters to be typed in, in order to allow any sort of Web-development. And it remains consistent with having LibreOffice installed, where what gets typed, is consistent with the English language, just as what the Google Pixel C’s OEM Keyboard offers, is…

There’s an added level of weirdness that would result, if somebody was just to write and compile C or C++ to run on an ARM-64 CPU in that way: The resulting binary wouldn’t be Android-compatible. It would assume that the O/S is Linux, but with an ARM-64 CPU, just like the Guest System. Writing Android-compatible code would require, that the ‘Android Development Kit’ be installed. Due to cross-compiling by the Debian package maintainers, there just might be ‘arm64′ packages of that available, but again, with no further guarantee that it all works…


 

(Update 9/08/2019, 10h20 : )

Unfortunately, this recognition does not negate the fact, that the way certain packages have been compiled to run on an ARM-64 CPU, still contain a bug…

Continue reading Whether it would be fair to expect, that the Debian libc6-dev package work, on an ARM-64 CPU-based device.

Generating a Germain Prime efficiently, using Python and an 8-core CPU.

I have spent extensive time, as well as previous blog postings, exploring the subject of how to generate a Germain prime (number), in the Python (3.5) programming language, but taking advantage of parallel computing to some slight degree.

Before I go any further with this subject, I’d like to point out that generally, for production-ready applications, Python is not the best language to use. The main reason is the fact that Python is an interpreted language, even though many modern interpreted languages are compiled into bytecode before being interpreted. This makes a Python script slower by nature, than very well-written C or even C++. But what I aim to do is to use Lego-Blocks to explore this exercise, yet, to use tools which would be used professionally.

The main way I am generating prime numbers is, to start with a pseudo-random, 512-bit number (just as a default, the user can specify different bit-lengths), and then to attempt to divide this number by a list of known, definite primes, that by now only extend to 4096 (exclusively, of course), in an attempt to disprove that number prime. In about 1/8 of all cases, the number survives this test, after which I undertake a more robust, Miller-Rabin approach to try disproving it prime 192 times, probabilistically. If the number has survived these tests, my program assumes it to be prime.

Even though I’ve never been told this, the probability of a non-prime Candidate surviving even a single Miller-Rabin Test, or Witness, is very small, smaller than 1/8. This could be due to the fact that the Witness in question is raised to a high, odd exponent, in its 512-bit modulus etc., after which it would be squared some number of times. Because the first Candidate in the modulus of 4 is 3, that one actually gets squared a subsequent total of zero times. And after each exponentiation, the result could be any number in the modulus, with the possible exception of zero. It needs to become either (1) or (n-1) in the modulus of (n), for the Candidate to survive the test. (:1)

Further, there is no need for the numbers that get used as witnesses, which are pseudo-random, to be of the same, high, cryptographic quality of pseudo-random, as the candidate is, which is being tested.

But there is a sub-category of prime numbers which have recently been of greater interest to me, which is known as the Germain prime numbers, such that the Totient of that main candidate, divided by two, should also be prime. And so, if the density of prime numbers roughly equal to (n) is (1 / log(n)), and if we can assume a uniform distribution of random numbers, then the probability of finding a Germain prime is roughly (1 / (log (n))2), assuming that our code was inefficient enough actually to test all numbers. The efficiency can be boosted by making sure that the random number modulo 4 equals 3.

But one early difficulty I had with this project was, that if I needed to start with a new pseudo-random number for each candidate, on my Linux computers, I’d actually break ‘/dev/urandom’ ! Therefore, the slightly better approach which I needed to take next was, to make my initial random number the pseudo-random one, and then just to keep incrementing it by 4, until the code plodded into a Germain prime.

Even when all this is incorporated into the solution I find that with Python, I need the added advantage of parallel computing. Thus, I next learned about the GIL – The Global Interpreter Lock – and the resulting pitfalls of multi-threaded Python, which is not concurrent. Multi-threading under Python tells the O/S to allocate CPU cores as usual, but then only allows 1 core to be running at any one time! But, even under those constraints, I found that I was benefiting from the fact that my early code was able to test at least 2 candidates for primality simultaneously, those being the actual candidate, as well as its Totient divided by 2. And as soon as either candidate was disproved prime, testing on the other could be stopped quickly. This reduced the necessary number of computations dramatically, to make up for the slowness of multi-threaded Python, and I felt that I was on the right track.

The only hurdle which remained was, how to convert my code into multi-processing code, no longer merely multi-threaded, while keeping the ability for two processes, then, to send each other a shutdown-command, as soon as the present process disproved its number to be prime.

(Updated 9/19/2020, 17h55 … )

Continue reading Generating a Germain Prime efficiently, using Python and an 8-core CPU.