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# Thursday, February 05, 2009


This article describes an older version of the Parallel Framework. Please go to the Parallel Framework - Download and Support page to get the latest update.

This article will remain for historical purpose. Download links have been removed from this page.


In a previous article "Parallel computing with sockets", I wrote about the implications when using parallelism in combination with sockets. But when talking to coders around me, it seems that they also encounter difficulties in grasping the concepts of parallel coding in general.
Reason enough to write an article about it.

Please note that if you want to know more about "Why parallelism?" and some of the basics, please read the article "Parallel computing with sockets".

Ok, to explain how to "simply" make parallel software, I'll first recapitulate a little of the basics from the previous article.



To code in parallelism, is to think in tasks. Each task can run on its own and runs in a separate process (thread) than the other tasks and the application.

Define your code into tasks rather than into threads. For example, a thread can first find all logfiles in a directory, open each file, read some lines, parse the lines and write the result into an output file. This can all be done in the same thread and if you want more concurrency, you can run multiple threads all doing the same thing.

If you would look at this in terms of tasks, you could make the following distinction:
1. Find all logfiles in a directory
2. Open a logfile and read some lines
3. Parse a line and write output

The task 1 can create new tasks of type 2, and task 2 can create new tasks of type 3. In this model your tasks are autonomic and are able to run separately from each other. Also, they don't have to run in any specific order after they are created.

This makes it easy for the underlying OS to spread the tasks over the available threads and cpu-cores.

Though you don't want to see tasks as threads in a programmatic-flow kind-of-way, you do have to remind that all these tasks run on separate threads in some sort of a threadpool, and thus face the same memory mapping and locking issues as the underlying threads.

If you don't know much about programming with threads, please first read the article "Threading in C#" by Joseph Albahari. He explains all the ins and outs about threading.


Parallel Framework

To make it a little easier for people to write parallel code, I've developed a lightweight Parallel Framework. It's a simple framework to work with tasks. There are several more frameworks available on the internet, but most are a lot more complex and have additional features that I (and probably you) don't need most of the time.

The framework is simple. It's basically built up of three different classes:
- ParallelState
- ParallelTask
- ParallelTaskPool

And (because I needed it for the example) I've written one additional class:
- PropertybagState

The following diagram shows it all.

The ParallelTask is the task that runs your code and do the appropriate (optional) callbacks when your code has finished. It's basically nothing more than some sort of wrapper which delivers the ParallelState object to your code.

The ParallelTaskPool can run the tasks in a fashionable way. Though you can run the tasks by themselves, you would most probably want to use the pool. It takes care of problems like overloading the process with too many concurrent tasks. And it can also block your main thread until all tasks have finished.

To be able to pass state info and parameters to a task, ParallelState objects containing all necessary data are passed along the tasks. These are merely containers of data and perform no real function themselves.
It also contains functionality to pass exceptions, which occurred in a task during execution, back to the main thread.

To create a specifically suiting state object for your needs, you can inherit from the ParallelState class and extend it. If you want a simple (but less efficient) state object, you can use the PropertybagState. It contains a hash-table in which you can store all parameters.

You will find the C# source files of the framework below.


Example Implementation: IIS LogParser

As an example on how to create parallel software, I've coded a little application which parses the (W3C) logfiles of an IIS webserver.

Basically what I want it to do is this:
- Find all logfiles in a given directory
- Read all the loglines
- Convert the usable information to XML
- and, Write all XML to one result file

For this example the following diagram applies.

So, basically you have a main thread, a number of "parse file" tasks and a vast number of "parse line" tasks. For a directory containing lots of huge logfiles this setup results in a huge amount of tasks.

# logfiles * # loglines per file = # tasks

To make sure that I don't kill the processor and memory with too many concurrent tasks, I'll use the ParallelTaskPool.


Example Code: parseFolder

public static void parseFolder(string _srcPath, string _dstPath, DateTime _from)
   StreamWriter dst = null;
     //create result file
     dst = new StreamWriter(File.Open(_dstPath, FileMode.Create, FileAccess.Write, FileShare.Read));
     dst.WriteLine("<?xml version=\"1.0\" encoding=\"utf-8\"?>");
     dst.WriteLine("<?xml-stylesheet type=\"text/xsl\" href=\"translate.xslt\"?>");

     //get all logfiles
     string[] files = Directory.GetFiles(_srcPath, "*.log", SearchOption.AllDirectories);

     //parse all files
     for (int idx=0; idx<files.Length && !exceptionOccurred(); idx++)
       //only process new files
       if (File.GetCreationTime(files[idx]) >= _from)
         //create state object
         PropertybagState state = new PropertybagState();
         state.setProperty("file", files[idx]);
         state.setProperty("dst", dst);

         //create task
         ParallelTask task = new ParallelTask( new ParallelTask.TaskRunnerDelegate(parseFile),
                                               new ParallelTask.TaskCallbackDelegate(taskDone),

         //run the task

     //wait until all tasks are done

     //close report tag in result file

     //rethrow the task exception, if pressent
     if (taskException != null)
       throw taskException;
     //try to close result file
     try{  dst.Close();  }catch(Exception e){;}

This is the main function and runs in the main-thread.
This function finds all the log files in _srcPath. For each log file found, a task is created to handle that specific file.
To pass on parameters for the task, a PropertybagState object is used, which contains the logfile path and a reference to the output file.

On creation of the task (at the constructor), the state object is passed and also two delegates are passed as parameters. One delegate refers to the code that will be executed by the task. The other delegate, an optional one, is a callback delegate and is executed when the task is done.

The ParallelTaskPool is used to run the task. Running tasks this way also enables you to block your main thread (or any other for that matter) and wait until all tasks are done.  Now you can run all your tasks, wait until all tasks have finished and then cleanup after the tasks. You can do this with ParallelTaskPool.blockWhileTasksRunning().

In the example, I used this technique to close the output file (which is passed to all tasks as a state parameter). If I wouldn't wait until all tasks were finished, I'd either close the output file too early, resulting in IO errors in the tasks. Or I had to open and close the output file in each task, creating a huge IO overhead.


Example Code: taskDone

 private static void taskDone(ParallelState _state)
   //check for errors
   if (_state.taskExceptionOccurred())
     taskException = new Exception("Exception during task execution", _state.taskException);

   //end task

This is the callback delegate function. As you can see, the state object is passed to this method and with it, you are able to check if there was an exception during the execution of the task.

Also, this is the place where you signal the ParallelTaskPool that the task is finished. When using the pool, this is vital to do. If you fail to call the ParallelTaskPool.endTask() the pool clutters up with tasks and only a few tasks will actually run. Others will remain queued and main thread execution will be halted.

Please also make sure that the call to ParallelTaskPool.endTask() is the last thing your task does. You can call it earlier without a problem, but tasks will run better (smoother) when you do so.


Example Code: exceptionOccurred

private static  Exception taskException = null;

private static bool exceptionOccurred()
  return (taskException != null);

This is a holder for an exception which occurred during execution of the task. It's filled by the taskDone (callback) function.
This function is used by parseFolder (looping through all the files) and by parseFile (looping through all the lines) to stop looping when an exception has occurred.


Example Code: parseFile

 private static void parseFile(ParallelState _state)
   //get state properties
   PropertybagState  stateIn   = (PropertybagState)_state;
   StreamWriter      dst       = (StreamWriter)stateIn.getProperty("dst");
   string            filePath  = stateIn.getPropertyString("file");

   StreamReader log = null;
     //open logfile
     log = new StreamReader(File.Open(filePath, FileMode.Open, FileAccess.Read, FileShare.ReadWrite));

     //go through all lines
     string line = null;
     while ((line = log.ReadLine()) != null && !exceptionOccurred())
       if (!line.StartsWith("#"))
         //create state object
         PropertybagState state = new PropertybagState();
         state.setProperty("logline", line);
         state.setProperty("dst", dst);

         //create task
         ParallelTask task = new ParallelTask( new ParallelTask.TaskRunnerDelegate(parseLine),
                                               new ParallelTask.TaskCallbackDelegate(taskDone),
         //run the task
     //close logfile
     try{ log.Close(); }catch(Exception e){;}

This code runs as a task. The state is passed to the task as a parameter.
Here the log file is opened and for each line that is not a comment, a new task is created which handles the parsing of the line.

As you can see a new state object is created. The current one can't be reused, because it would overwrite the logline property for each line (and thus task). But when you would extend the ParallelState class with your own specific state class, you would be able to make some properties static and create a .Clone() method. That way you would lessen overhead.


Example Code: parseLine

 private static void parseLine(ParallelState _state)
   //get the state object
   PropertybagState state = (PropertybagState)_state;

   //get some state properties
   StreamWriter  dst   = (StreamWriter)state.getProperty("dst");
   string        line  = state.getPropertyString("logline");

   //split line into fields
   string[] fields = line.Split(new char[]{' '});

   if (fields.Length < 12)
     throw new Exception("Bad format of logline: \""+line+"\"");
   //create activity element
   XmlElement xml = (new XmlDocument()).CreateElement("ACTIVITY");
   //add attributes
   xml.SetAttribute("datetime", fields[0]+"T"+fields[1]);
   xml.SetAttribute("site", fields[2]);
   xml.SetAttribute("url", fields[5]);
   xml.SetAttribute("query", fields[6]);
   xml.SetAttribute("clientIP", fields[9]);
   xml.SetAttribute("userAgent", fields[10]);
   xml.SetAttribute("reponseCode", fields[11]);

   //write xml to file
   lock (dst)

This code also runs as a task. The state is passed to the task as a parameter.
Here the log line is parsed and converted to XML. The XML is written to the output file.

Because tasks run on different threads (which can be executed simultaneously) and because all the parseLine tasks write to the same output file, you must use locking to avoid any thread safety issues on the file handle. Lock an object that is known (available) to all tasks which use the output file.
In my example I've used the reference to the output file itself for the lock.

Please always make sure to lock as less as possible! Only lock what is absolutely necessary and nothing more, because otherwise performance can be drastically reduced and deadlocks are more easy to happen.

Now, please enjoy the Parallel Framework and parallel programming. If you have any questions about this subject, please leave a comment below.


Threading in C# (http://www.albahari.com/threading/)
Parallel Computing with Sockets (http://blog.rednael.com/2008/11/07/ParallelComputingWithSockets.aspx)


Please download the latest version at the Parallel Framework - Download and Support page.

Source files and documentation included.


Thursday, February 05, 2009 10:51:30 AM (W. Europe Standard Time, UTC+01:00)  #    Comments [6]
.Net | Parallelism
Thursday, February 05, 2009 3:21:45 PM (W. Europe Standard Time, UTC+01:00)
A very interesting article, it seems that you concentrate very much on multi cores and threading. How would your framework behave if you were to apply it to very large scale computing (say a cluster) with data parallelism in mind? Is it simple to change the framework so that it is more message passing rather than shared memory (can this be done automatically by the language it is written in?)

Readers might also find the Parallel Programming article at http://www.technicaltalk.net/index.php/topic,835.0.html interesting - I think they complement each other quite well.
Thursday, February 05, 2009 7:34:49 PM (W. Europe Standard Time, UTC+01:00)
Good post. I'll try to work with this.
Actually, I am researching about multicore programming with C#.
I bought a new book for beginners by Packt Publishing: "C# 2008 and 2005 Threaded Programming: Beginner’s Guide", by Gaston C. Hillar - http://www.packtpub.com/beginners-guide-for-C-sharp-2008-and-2005-threaded-programming/book

I bought it from B&N: http://search.barnesandnoble.com/C-2008-and-2005-Threaded-Programming/Gaston-C-Hillar/e/9781847197108/?itm=2
It is also available at Amazon (but more expensive): http://www.amazon.com/2008-2005-Threaded-Programming-Beginners/dp/1847197108

The book is for beginners who want to exploit multi-core with C# 2005; 2008 and future 2010.

I read the book in one week and it has great exercises to help developers run in the multi-core jungle. Highly recommended.

I must also recommend Joe Duffy's book "Concurrent Programming on Windows".

They helped me a lot to understand and exploit the multicore revolution.
Tomas Jordan
Thursday, February 05, 2009 7:51:14 PM (W. Europe Standard Time, UTC+01:00)
Great post, Rednael!
Are you going to improve this framework? It is very interesting.
I've also read "C# 2008 and 2005 Threaded Programming: Beginner’s Guide", by Gaston C. Hillar. It develops a very simple framework to process data parallelism without worrying about threads, using good object-oriented encapsulation. After reading this book, I realized the importance of multicore programming and parallelism.
I think that cluster processing is different from multicore programming.

Your framework is good for multicore. OpenMP and MPI add a great overhead. We do not need them to improve single computer applications. We need to exploit multicore.

I'd love to see new posts. And, again, I think that beginners should read Hillar's book.

If you are not a beginner, and you have time to read 1,000 pages, Joe Duffy's work recommended by Tomas Jordan, is also a must.

Keep your good work!
Diego Salinas
Friday, February 06, 2009 8:58:26 AM (W. Europe Standard Time, UTC+01:00)
Hi Diego,

Yes, you're totally right. This framework is meant for multi-core task parallelism and is (in its current state) not suited for multi-system parallelism. I think that multi-core is the way we need to explore more. This is what all home and business computers will (already do) need and where software development lags behind.
Though it's very interesting to take a look at multi-system parallelism, I think it's mostly usable for governments and universities, because they are the ones who might build such a super-computer... Well, and it's also quite usable for hackers... with bot-networks :)

About the OO encapsulation of parallel frameworks... I think it's best to not encapsulate everything for a developer. Two disadvantages can be:
1. A developer doesn't know what's happening under the hood, so he can make mistakes more easily. The same with threading... you have to learn it before you can use it. But after you've learned it, you can do amazing things with it.
2. A framework with too much encapsulation might become less lean-and-mean. This would prevent a skilled coder from developing lean-and-mean software with the framework.

It's probably all about skills. Some languages/compilers try to create an everybody-can-code language, but I think that good coders are the ones who understand what's happening under the hood (and are eager to learn).

Friday, February 05, 2010 1:57:21 AM (W. Europe Standard Time, UTC+01:00)
alguno de ustedes a trabajado en el xbox 360 en paralelo?
estoy desarrollando una aplicacion y quisiera que me explicaran un poco de la arquitectura del xbox, por favor
Ivan Munoz
Friday, February 05, 2010 7:53:58 AM (W. Europe Standard Time, UTC+01:00)
Google translates it like this...
"any of you worked on the Xbox 360 in parallel?
I am developing an application and I want to explain a little of the architecture of the xbox, please"


No, I don't know anything about the architecture of the XBox. But Wikipedia does ;-)

"The Xbox 360 uses the triple-core IBM designed Xenon as its CPU, with each core capable of simultaneously processing two threads, and can therefore operate on up to six threads at once"


So, yes, you can use parallelism very well on the XBox 360!
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