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LeoTask is a fast, flexible and reliable framework for computational research. It is a free and open-source project.


  • Automatic & parallel parameter space exploration
  • Flexible & configuration-based result aggregation
  • Programming model focusing only on the key logic
  • Reliable & automatic interruption recovery
  • ...


  • Dynamic & cloneable networks structures: a node, a link, a network, and a network set (within which networks can overlap with each other).
  • Integration with Gnuplot
  • Network generation according to common network models
  • DelimitedReader: a sophisticated reader that explores CSV (Comma-separated values) files like a database
  • Fast random number generator based on the Mersenne Twister algorithm
  • An integrated CurveFitter from the ImageJ project
  • ...

Programming Model

Every application should extend the Task class and implement the application by overriding Task's methods. The following figure shows the default method flow and time points for a task. There are 6 default built-in time points for result collection: 1) beforeTask: before the start of a task, 2) beforeRept: before starting a repeated run of a task, 3) beforeStep: before starting a step in a run, 4) afterStep: after finishing a step in a run, 5) afterRept: after finishing a repeated run of a task, 6) afterTask: after finishing a task.

Default method flow and time points for a task.

Example Application

Blow is an example application of the framework: RollDice. The application simulates to roll a dice at each step. Each dice has nSide sides, and there are nDice dices. In total it conducts nDice steps in each repeat of a task.

Code (RollDice.java): <source lang="Java"> public class RollDice extends Task {

   private static final long serialVersionUID = -4612453806484156399L;
   public Integer nSide; //Number of dice sides
   public Integer nDice; //Number of dices to roll
   public Integer sum;
   public boolean prepTask() {
       boolean rtn = nSide > 0 && nDice > 0;
       return rtn;
   public void beforeRept() {
       sum = 0;
   public boolean step() {
       boolean rtn = iStep <= nDice;
       if (rtn) {
           sum += (int) (rand.nextDouble() * nSide);
       return rtn;

} </source>

Configuration (rolldice.xml): <source lang="XML"> <Tasks>

 <name val="task-rolldice"/><usage val="0.5"/><nRepeats val="5"/><checkInterval val="4"/>
 <variables class="org.leores.task.app.RollDice">    
   <nSide val="2;4;6"/>
   <nDice val="2:1:5"/>
     <info val="Fig1%[email protected]@"/><valVar val="sum;#$sum$/$nDice$#"/><parVars val="nSide;nDice"/>
     <info val="Fig2%[email protected]@"/><valVar val="sum"/><parVars val="nSide"/>
     <info val="Fig3%[email protected]@"/><valVar val="sum"/><parVars val="nDice"/> 

</Tasks> </source>

The configuration entitles (name) the tasks "task-rolldice", allocates (usage) 50% CPU cores to run the tasks, sets each task to repeat (nRepeats) 5 times, and set checkpoint files (checkInterval) to be saved very 4 tasks.

The task is set to be org.leores.task.app.RollDice. The values of RollDice's two parameters are provided. nSide has 3 values: 2, 4, and 6. nDice has 4 values:2, 3, 4, and 5. There are 12 (=3x4) combinations of parameter values in this configuration. 12 tasks each with a different combination of parameter values will be conducted in parallel. Checkpoint files will be saved when the 4th, 8th, and 12th task starts.

The configuration collects 3 statistic results: 1) sum and sum/nDice (average outcome of a roll of a dice) conditioned on (nSide, nDice) pair after each repeat of a task; 2) sum conditioned on nSide after each repeat of a task; and 3) sum conditioned on nDice after each repeat of a task.

Running this example application will generate three figures and a CSV (Comma-separated values) file containing the statistic results:

The result figures of the example application

For more detailed explanation of this example application and its results: an introduction to LeoTask.

External links