Developing on a Pocket CHIP with “Motor”, a Text-based IDE

Nothing like a Text-based UI

A recent discussion on the Pocket C.H.I.P forums made me dig up memories of my time with DJGPP (a wonderful DOS port of GCC), and its quite functional text-based IDE; RHIDE.

I thought that it would be great to have RHIDE running on my Pocket CHIP for doing simple C/C++ development on the go, as TUIs (Text-based User Interfaces) were pretty simple and could work in really low resolutions, but unfortunately CHIP’s ARM architecture was completely foreign to RHIDE’s build scripts, and it refused to compile.

I’m definitely no Linux guru, but I reckoned that adding the architecture to the build script wasn’t going to be easy, especially since it apparently had several architecture-dependent libraries, modules, etc, so I embarked on a quest to find another text-based IDE for Linux, and that’s how I found Motor.

Sure, the project is 12 years old (last “build” is 3.4.0 from February 2005, and still more recent than RHIDE), but I thought that perhaps it could still work on a relatively modern Linux distribution, and to my surprise, it did (with a few gotchas, though).

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Using your Pocket C.H.I.P as a portable Logic Analyzer

Pocket C.H.I.P + USB Analyzer

So a time ago I purchased a cheap USB Logic Analyzer from eBay that works great with a PC, and it’s been really helpful to debug several projects to date. It uses the Logic software from Saleae as hinted by the label on it, although I am not sure if the device is supposed to be a cheap knockoff of one of the (pricier) genuine Saleae analyzers, or it was just designed to be “Saleae-compatible” and use their software. Read More

Mini HTTP Daemon Service for RaspberryPi/Linux apps

So recently I had to design a relatively convoluted system with a database that communicates with a hardware controller board and a RFID reader. Among other things, the system has to respond to several commands issued from a web frontend over HTTP, and report the status of each sub-system, sensor, etc hopefully in JSON or similar web-friendly format.

For this task I picked a Raspberry Pi as the platform, and made a program in C that talks directly to the hardware and handles everything including the HTTP requests.  Now, this is definitely not the first time that I need to write a program that has to listen for requests and reply with simple data over HTTP,  so I thought that perhaps it would be useful to encapsulate this functionality in a small module that I could later re-use in other projects.

So I ended up doing exactly that, and uploaded the code to my GIT-Hub Repository, so you’ll find the result from that here: https://github.com/battlecoder/httpdpi.

Before you dive into the code, please bear in mind that it’s an extremely simple service that will only respond to GET requests, but has all it needs to reply with different status codes, text, and binary data. It only uses sockets and POSIX threads, so it’s very fast, doesn’t depend on a huge framework, and can run in parallel with your code. It’s also really easy to expand if you want to support other types of requests.

Since it doesn’t have obscure dependencies, it should also compile on most linux boxes including other small computers like C.H.I.P, Beaglebone, etc.

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Random Numbers, Distributions and Games (Part 2)

Continuing from my previous post on randomness, I’d like to talk about non-uniform distributions, which certainly don’t get all the love they deserve. When people talk or think about randomness in games, they commonly think about fair distributions. And I know we spent a lot of time in Part 1 actually trying to achieve perfect uniformity because it brings “fairness” to games,  but in reality, there are cases where you don’t want your random events to be ruled by a “fair” distribution at all.

Reality Check #4: Sometimes uniformity is bad

Fun fact: For a lot of “random” events in nature, every possible outcome rarely has the same chance of occurring, so perhaps trying to achieve that uniformity in games could be a mistake to begin with.

Let’s take rabbits for instance.

Yes, rabbits.

If you observe the population of rabbits of a given area, you’ll notice that they’ll have a”typical average size”. Let’s call that size X. You’ll find that most rabbits in the area will be around that size. There will be a rare few that are either considerably smaller than X or considerable bigger (outliers), but for the most of it, rabbits will be “just around” X in size. Same goes for any other quantifiable trait that depends on enough factors to be considered random.

They will most-likely follow what is called a Normal (or Gaussian) distribution, which is said to appear in nature all the time. The function that defines this distribution is also called the Gaussian Bell, due to the shape of the curve.

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Random Numbers, Distributions, and Games (Part 1)

DISCLAIMER: This is a rather long post on the topic of random numbers, so …uh, sorry for that. 

I want to talk about a couple of interesting things related to randomness and its many nuisances especially when applied to games. But before we get to that I guess I’ll introduce the basic notions for those of you who are not familiar with this whole thing. You can skip the first two sections if you know what a PRNG is, and how it works.

What is Random

Random is commonly defined as “unpredictable“. In general, when we are unable to find a pattern that would allow us to anticipate the outcome or occurrence of an event, we call it “unpredictable” and there’s a chance we will consider the event “random”.

You’ll also hear of “true randomness”, and things like natural atmospheric noise, lightning bolts, or particles falling from the space being used as sources and examples of it. But while we can’t currently predict when and where lightning will hit, events in the universe like electric discharges in clouds are most likely just a massively complicated function of a number of different factors, and not really something that happens with no rhyme or reason. It’s quite possible that if we were able to simulate each particle and sub-particle inside a group of storm clouds and their relevant vicinity, lightning would be trivial to anticipate with accuracy. This makes its “unpredictability” debatable,  I guess.

Having said that, let’s not forget that “predictable” and “unpredictable” are relative to an “observer”.  What we consider true random events could totally have a logic behind, but what matters is that from our -and our system’s- point of view, they are impossible to predict, and if the machine (or person) is unable to anticipate the occurrence of an event, the definition applies, regardless of the event’s “actual” predictability.

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USB Adventures Part 2: Custom HID and USB Composite Devices

So in a previous post I’ve discussed how to communicate with a custom HID device using libhid and a Raspberry Pi running linux.

This post is a sort of sequel. I’ll talk about some of the issues and nuances I found when working on a more complex (but related) project; In this case a  Composite USB Device that I had to implement on a PIC 18F4550 microcontroller.

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Gameboy / GBC development tools

If you follow me on Twitter, you might have seen this 1 year ago:

Followed by:

Although I think I never ever posted anything after that, I was definitely working on such a tool, focusing particularly on Gameboy Color development (and as you can see I got it working).

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Raspberry Pi, C and custom HID Devices

So I’m writing a program in C that needs to interact with a custom HID device I built. This program will be running on a Raspberry Pi. This isn’t a massively complicated task but it can be daunting when there’s not a single “barebone” example or tutorial out there on how to do this. So I decided to write this sort of guide in case it may come in handy for anyone (including myself, in a future).

Compiling libhid

Libhid is an open source library designed on top of libusb to deal with HID devices, so the first step is compiling libhid. I’d say this is relatively straight-forward except for the fact that “as-is”, the library fails to build in the Pi. Luckily the problem is a single line of code in one of the examples (yes, and that prevents the whole library from being compiled and installed).

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Brainfuck-on-Arduino: End Of File

Ok, so this is probably the last post I’ll make about my Brainfuck-on-Arduino project, basically because it has reached a point where I’ve already tried all the things I wanted to try and I’ve decided that there’s no point in taking it out of the breadboard and build a board for it. At least not for Brainfuck. And I’ll explain why.

 

The Performance Issue

I previously said that I was expecting the performance to “drop” a bit when reading directly from a SD card instead of the internal RAM, but I was hoping to mitigate that with a sector/block cache similar to the one I wrote for the SPI RAM.

And that’s completely reasonable and actually true. Where I made a mistake however, was in also assuming that doubling the SPI clock would result in a noticeable performance boost. That’s definitely false. Reading a whole 512bytes sector currently takes between 1 and 2 milliseconds at 4Mhz, and RAM access is done at the same speed, so being the RAM pages half the size of the SD sectors it probably takes half that much to get a whole RAM page.

Since we are caching so many bytes in advance, the number of page reads (both from RAM and SD) is not really that high, so even if we were to double the SPI bus speed we will only cut around 1ms from each access. Most programs I’ve tested don’t normally cross the RAM page boundaries nor require more than one SD sector to be stored, so the speedup won’t even be noticeable for most cases. It will be barely 1 or 2 ms, so if we run into performance issues, they are somewhere else. They are NOT in the SPI Bus speed.

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Brainfuck-on-Arduino: Improving the performance

DISCLAIMER: Over the course of this post I’ll be dealing with parsing, programming practices, code refactoring, SPI bus oddities, caching techniques,etc. It’s a mixed bag of things and it’s far from being serious analysis on the topic of optimization. Please don’t expect anything particularly smart in this post like branch prediction, overlapping cache windows, partial block reads, etc. This is just a chronicle of sorts, of the things I’ve done over the past few hours to improve the performance of my Brainfuck-on-Arduino interpreter, which was being painfully slow.

So in my previous post you hopefully got a glimpse of my current project: a Brainfuck interpreter running completely on Arduino. Something that I forgot to mention is that all the input/output (for testing purposes) currently happens through a serial connection. I’m using the “Serial Monitor” console that’s part of the Arduino IDE to “talk” with the board and run the code.

I’d also like to point out that I’m using an external 23K256 chip for the Brainfuck data space, This is basically a 32KB RAM IC that is accessed via SPI (Serial Peripheral Interface). This is relevant for some of the optimizations I’ll do next.

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