Wednesday, December 25, 2013

Getting a simple CDC demo (serial port via USB) working with a PIC32 (PIC32MX575)

This is just a quick write-up to save others some debugging time. The adventure is called: Get the "cdc_serial_emulator" example supplied of Microchip Harmony (/harmony/v0_70b/apps/usb/device/cdc_serial_emulator) up and running on my custom PIC32 board.

Once one understands the inner workings of the firmeware, the first steps are quite simple. First of all I modified the code which assumed we would run on an Explorer Board. Therefore I commented everything uncompilable and unneeded in bsp_sys_init.c out.

Now that I was able to compile the project, I was ready to enter the USB descriptor hell. And believe it or not, my Windows 7 machine was the most helpful tool for my first steps. Every time I plugged my board in, those annoying "USB device connected" sounds let me know if the communication worked or not. Of course, it didn't  by default. The reason is the clock frequency of the PIC. The USB module has an internal PLL which generates the required 48 MHz. This PLL is fed by the main oscillator and therefore everything needs to be setup correctly. I used (for no special reason) a 20 MHz crystal instead of the 8 MHz crystal assumed by the example.

Therefore I needed to tweak the config settings found in system_init.c: 
OSCO Pin(OSCIOFNC)                        = Enable
Primary Oscillator Configuration(POSCMOD) = External (Highspeed)
Secondary Oscillator Enable(FSOSCEN)      = Disabled
Oscillator Selection Bits(FNOSC)          = Primary osc with PLL


PLL Input Divider (FPLLIDIV)               = Divide by 5 
(20 Mhz / 5 = 4 MHz) 
PLL Multiplier (FPLLMUL)                   = Multiply by 20

(4 MHz * 20 = 80MHz)
System PLL Output Clock Divider (FPLLODIV) = Divide by 1
(80 MHz / 1 = 80 MHz system clock)
Peripheral Clock Divisor (FPBDIV)          = Divide by 1
(80 MHz / 1 = 80 MHz peripheral clock)

Watchdog Timer Enable (FWDTEN)                 = Disabled
Clock Switching and Monitor Selection (FCKSM)  

 = Clock Switch Enable, Fail Safe Clock Monitoring Enable

#pragma config FPLLIDIV = DIV_5, FPLLMUL = MUL_20, FPLLODIV = DIV_1
#pragma config FWDTEN = OFF, FCKSM = CSECME, FPBDIV = DIV_1 

Enable PLL for USB clock generation 

#pragma config UPLLEN   = ON

Divide external input clock by 5 before it is fed into the USB PLL.

20 MHz / 5 = 4 MHz (This is multiplied by 24 and then divided by 2 - see Reference Manual Page 27-3). This will result in the desired 48 MHz USB reference clock.

#pragma config UPLLIDIV = DIV_5

As this only took 3 evenings to figure out, this can be considered the easy part...The problem was, that neither Linux nor OSX would recognize the device as serial port (the device itself was found). Therefore it was clear that the timing was ok, but the USB descriptor wasn't.

After days of debugging (mostly on OSX using USBProbe) I switched to Linux and tried "lsusb -v". And there you go: The CDC-Descriptor provided by microchip was/is wrong! lsusb said something like "INVALID CDC(UNION) 0x04 0x24 0x06 0x00. Looking at the descriptor, this was really the input in system_config.c:

    // Size of the descriptor
    // Type of functional descriptor
    //com interface number

Compared to any other CDC device I tried, this was missing one byte as the record (descriptor) has to look like LENGTH, TYPE, SUBTYPE, MASTER_INTERFACE, SLAVE_INTERFACE - so this was clearly missing one byte. Therefore I changed to code to:

0x05, // Size (5 bytes)
0x24, // DescriptorType: CS_INTERFACE
0x06, // DescriptorSubtype: Union Functional Descriptor
0x00, // MasterInterface
0x01, // SlaveInterface0

Of course, that didn't work out of the box - as I changed the descriptor by adding one byte, I had to fix the size at the begin of the descriptor:

/* Configuration 1 Descriptor */
const uint8_t configDescriptor1[]={
    /* Configuration Descriptor */
    //sizeof(USB_CFG_DSC),    // Size of this descriptor in bytes
    // CONFIGURATION descriptor type
    // Total length of data for this cfg
    67,0, // This was 66 originally

Using this, both, my Mac and Linux machines now recognize the device and supply a tty for it :-)

Saturday, December 21, 2013

How to write one of the fastest expression evaluators in Java

Granted, the title is a bit of an attention grabber, but nevertheless true (You course you never trust a benchmark you didn't fake yourself - but that's another story).

So last week I was looking for a small and usable library to evaluate mathematical expressions. I almost directly stumbled upon this stackoverflow post. The recommended library (Expr) is really quite fast and had almost everything I needed. However, what it didn't provide was the ability to limit the scope of variables (everything is in one global namespace within the VM).

Therefore I did, what one normally shouldn't do: I reinvented the wheel and wrote my own parser / evaluator. It was a rainy saturday anyway so I thought a small recursive descending parser, an AST which simplifies and eventually computes expressions along with a little helper for managing variables doesn't seem to be a big deal anyway. And it wasn't. I had an initial implementation up and running quite fast. Once I had some tests giving me confidence that it computed everything the right way, I wanted to know how fast the evaluator was, compared to other libraries mentioned in the original post. Having not hand-optimized every inner loop and everything, I had't much expectations, some of the libraries are commercial ones afterall. So I was quite suprised when I looked at the results. The list below shows a micro benchmark which evaluates the same expression using the respective library. The measurements for parsii, which is my library, were done using the final version, which performs some simplifications, like pre-evaluating constant expressions. However, no "black magic" like bytecode generation or anything in that league is done.

For a performance measurement the expression "2 + (7 - 5) * 3.14159 * x^(12-10) + sin(-3.141)" was evaluated with x running from 0 to 1000000. This was done 10 times to warm the JIT and then 15 times again of which the average execution time was taken:
  • PARSII:      28.3 ms
  • EXPR:        37.2 ms
  • MathEval:  7748.5 ms
  • JEP:        647.0 ms
  • MESP:       220.8 ms
  • JFEP:       274.3 ms
Now I'm sure, each of these libraries has their own strengths, so they can't be directly compared. Still it's amazing to see that a simple implementation can compete quite well.

For those of you who are not too deep into compiler contruction, here's a small outline of how it works:

As any parser or compiler parsii uses the classic approach of having a tokenizer, which converts a stream of characters into a stream of tokens. Therefore "4 + 3 *8" which is '4', ' ',  '+', ' ', '3' , ' ', '*', '8' as character array will be converted into:
  •  4 (INTEGER)
  • + (SYMBOL)
  • 3 (INTEGER)
  • * (SYMBOL)
  • 8 (INTEGER)
The tokenizer takes a look at the current charater, then decides what kind of token it is looking at and then reads all characters, which belong to that token. Each token has a type, textual contents and knows the position (line and character) where it started. A lot of in-depth tutorials are available on the net, so I won't go into any details here. You can take a look at the source code, but as I said, it is just a basic naiive implementation.

The parser which translates the given stream of tokens into an AST (Abstract Syntax Tree) which can then be evaluated, is a classic recursive descending parser. This is one of the simplest ways to build a parser, as it is completely written by hand and not generated by a tool. A parser like this basically contains a method for every syntax rule.

Again a lot of tutrials for this kind of parsers are available. However, what most example leave out is proper error handling. Next to parsing an expression correcly and fast, good error handling is one of the central aspects of a good parser. And it's not that hard: As you can see in the source code, the parser never throws an exception while parsing the expression. All errors are collected and the parser continues to go on as long as possible. Even though after the first error, the resulting AST cannot be evaluated correctly, it is important to go on as we can and should report as many errors as possible in one run. The same approach is used for the tokenizer, as reports malformed tokens, like decimal numbers with two decimal separators, to the same list of errors.

Evaluating an AST which is the result of a parsed expressions is quite easy. Each node of the syntax tree has an evaluate method which will be called by its parent node, starting from the root node. The result of eval here, is the result of evaluating the expression. A basic example of this approach can be found in BinaryOperation, which represents operations like +, -, * and so on.

In order to improve evaluation time a bit, three optimizations are performed:

First, after parsing the AST is reduced calling a method called simplify on the root node, which propagates to each child node. Each node then decides if a simpler representation of the own sub-expression can be found. As an example: For binary operations, we  check if both operands are constant (numbers). In that case, we evaluate the expression and returns a new constant containing the result of the operation. The same is done for functions where all parameters are constant.

The second optimization is done when using variables in expressions. The naiive approach here is to use a map and read or write the values of the variable when needed. While this certainly works, a lot of lookups while be performed. Therefore we have a special class called Variable which contains the name and the numeric value of the variable. When an expression is parsed, the variable is looked up once in the scope (which is basically just a map) and then used from now on. As each lookup returns the same instance, variable access when evaluating expressions is as cheap as a field read or write, as we just access the value field of Variable.

The third and last optimization won't probably often come into play. But as it is simple to realize, it was implemented anyway. It basically goes by the name "lazy evaluation" and is used when calling functions. A function does not automatically evaluate all its arguments and then perform the function call iteself. It rather looks at the arguments and can deciede by iteself, which argument to evaluate and which not. An example where this is used, can be found in the if function.

parsii is licensed under the MIT license. All sources can be found on GitHub along with a pre-compiled jar

(Speedy Gonzales Image-(c) by

Thursday, September 19, 2013

How to kill Java with a Regular Expression

We recently stumbled upon a phenomen we absolutely weren't aware of: You can kill any Java IDE and also any Java process with a simple regular expression...

Back in university, I was taught that regular expressions, which are called regular grammers or type 3 grammers  always end up in an finite state automaton and can therefore be processed in linear time (input length doubles, processing time doubles). However, that's only true for "sane" expressions. A regular expression can also result in an non-deterministic finite state automaton and things can get messed up quite bad.

Consider the expression: (0*)*A  This will any number of zeros, followed by an upper case A. Now if you use Matcher.find() for this expression, everything is fine as long as there is a match in the input. However, if you call this, with "00000000000000000000" as input, your program will hang (and so will the regex console in Eclipse or IntelliJ and every (Java-based) online regex service).

What at first glance looks like an infinite loop, truns out to be catastrophic backtracking. What this basically means is, that the matcher detects, that no A was found at the end of the input. Now the outer quantifier goes on step back - the inner one forward and again - no result. Therefore the matcher goes back step by step retrying all combinations to find a match. It will eventually return (without a match) but the complexity (and therefore the runtime) of this is expotential (adding one character to the input doubles the runtime). A detailed description can be found here: catastrophic backtracking

Here are some runtimes I measured (which almost exactly double for each character added):

0000000000: 0.1ms
00000000000: 0.2ms
000000000000: 0.7ms
0000000000000: 1.3ms
00000000000000: 1.7ms
000000000000000: 3.5ms
0000000000000000: 7.2ms
00000000000000000: 13.9ms
000000000000000000: 27.5ms
0000000000000000000: 55.5ms
00000000000000000000: 113.0ms
000000000000000000000: 226.4ms
0000000000000000000000: 439.1ms
00000000000000000000000: 886.0ms

As a little side-note: For micro benchmarks like this, you always need to "warm" up the JVM as the HotSpot JIT will jump in at some point and optimize the code. Therefore the first run looks like this:

0000000000: 6.8ms
00000000000: 11.8ms
000000000000: 25.5ms
0000000000000: 39.5ms
00000000000000: 6.3ms   <- JIT jumped in and started to translate
000000000000000: 5.4ms     to native code.
0000000000000000: 7.1ms

00000000000000000: 14.2ms
000000000000000000: 26.8ms
0000000000000000000: 54.4ms
00000000000000000000: 109.6ms
000000000000000000000: 222.1ms
0000000000000000000000: 439.2ms
00000000000000000000000: 885.6ms

So what's the take-away here? If you're running a server application or anything critical used by many users, don't let them enter regular expressions unless you really trust them. There are regex implementations out there, which detect this problem and abort, but Java (up to JDK 8) doesn't.

Note: You can test this with your local IDE or a small Java program to your hearts content - but please don't start to knock out all the regex tester websites out there. Those guys provide a nice tool free of charge, so it would be quite unfair..

Here is the tiny benchmark I used:

public class Test {
    public static void main(String[] args) {
        for (int runs = 0; runs < 2; runs++) {
            Pattern pattern = Pattern.compile("(0*)*A");
            // Run from 5 to 25 characters
            for (int length = 5; length < 25; length++) {
                // Build input of specified length
                String input = "";
                for (int i = 0; i < length; i++) { input += "0"; }
                // Measure the average duration of two calls... 
                long start = System.nanoTime();
                for (int i = 0; i < 2; i++) {
                System.out.println(input + ": " 

                       + ((System.nanoTime() - start) / 2000000d) 
                       + "ms");

Wednesday, September 4, 2013

A Monoflop class for easier looping...

Whether you want to join the contents of a collection or build a URL query string. There are lot's of cases where you have to handle to first iteraton of a loop a bit different from all others. Often I used this construct:

List<String> listToJoin = ...
boolean first = true;
StringBuilder result = new StringBuilder();
for(String item : listToJoin) {
   if (!first) {
      result.append(", ");
   first = false;

Yes, this works like a charm, but just looks plain ugly. And don't ask how long one has to debug if you get the position of the first = false wrong.

A simple class called Monoflop can help here. A properly commented version can be found on GitHub, but a very short version will do here:

public class Monoflop {

    private boolean toggled = false;
     * Reads and returns the internal state.

     * Toggles it to true once.
    public boolean successiveCall() {
        if (toggled) {
            return true;
        toggled = true;
        return false;

     * Inverse of successiveCall

boolean firstCall() {
        return !successiveCall();

Using this neat helper results in the following:
List<String> listToJoin = ...
Monoflop mf = new Monoflop();
StringBuilder result = new StringBuilder();
for(String item : listToJoin) {
   if (mf.successiveCall()) {
      result.append(", ");

Granted, you didn't save a whole lot of line in this example. However the logic is much more visible and you have less possibilities for errors. The only thing which is a bit misleading is that a call named successiveCall() has side-effects. On the otherhad, as it toggles the internal state, I didn't want to make it a getter (isSuccessiveCall()) since that would be even more evil.

Feel free to use this class in your own code base (but use the one from GitHub - as it is better documented). However, if you like it and you have uses for the fastest dependency injection framework out there, with lots of other features, check out: (GitHub). SIRIUS (which contains Monoflop) is OpenSource (MIT license) and developed and maintained by scireum GmbH.

Monday, September 2, 2013

S3 Emulator / Running S3 Locally

We've all been there: You're in a remote spot, distant from all your daily stress and hassle. A romantic candle light setup puts you in the right mood - Nothing can stop you from coding on your newest cloud project like there's no morning. Nothing? Well, without a proper internet connection, accessing S3 and the like is quite unpleasant. But fear no more! With S3 ninja you can setup a S3 / Ceph compatible API in seconds.

Download the latest zip from, follow the installation instrcutions and you're ready to go. Being a java application, you need nothing but an installed Java JRE (1.6.xx). At last a word of caution: S3 ninja is intended to be a simple easy to use emulator for the S3 API. It has neither caching nor any kind of replcation layer.

Feel like hacking? Both, S3 ninja and its underlying server platform SIRIUS are open source and welcome contributions: Project on Github

This is what the main GUI looks like. On the left it even shows you the credentials to use, to test your hash generation:

In the (API) Acess Logs all calls against the S3 API are tracked and visualized: