/* -*- tab-width: 4 -*-
*
* Electric(tm) VLSI Design System
*
* File: PlacementGenetic.java
* Written by Team 4: Benedikt Mueller, Richard Fallert
*
* This code has been developed at the Karlsruhe Institute of Technology (KIT), Germany,
* as part of the course "Multicore Programming in Practice: Tools, Models, and Languages".
* Contact instructor: Dr. Victor Pankratius (pankratius@ipd.uka.de)
*
* Copyright (c) 2010 Sun Microsystems and Static Free Software
*
* Electric(tm) is free software; you can redistribute it and/or modify
* it under the terms of the GNU General Public License as published by
* the Free Software Foundation; either version 3 of the License, or
* (at your option) any later version.
*
* Electric(tm) is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
* GNU General Public License for more details.
*
* You should have received a copy of the GNU General Public License
* along with Electric(tm); see the file COPYING. If not, write to
* the Free Software Foundation, Inc., 59 Temple Place, Suite 330,
* Boston, Mass 02111-1307, USA.
*/
package com.sun.electric.tool.placement.genetic2;
import com.sun.electric.tool.placement.PlacementFrame;
import java.util.List;
/**
* Combination of Genetic Algorithm and Simulated Annealing.
*
* We have implemented a parallel genetic algorithm using a UnifiedPopulation
* and multiple Evolver threads working on it.
*
* We save only changes in our DeltaIndividuals, which saves CPU time and memory.
*
* Please note: We had to make some methods inside PlacementFrame public:
* PlacementPort.getPlacementNode()
* PlacementPort.getRotatedOffX()
* PlacementPort.getRotatedOffY()
* PlacementNode.getPlacementX()
* PlacementNode.getPlacementY()
* @see GeneticPlacer
* @see UnifiedPopulation
* @see DeltaIndividual
* @see Evolver
* @see SimulatedAnnealing
*/
public class PlacementGenetic extends PlacementFrame
{
GeneticPlacer placer = null;
// maximum runtime of the placement algorithm in seconds
public int maxRuntime = 240;
// number of threads
public int numThreads = 4;
// if false: NO system.out.println statements
public boolean printDebugInformation = true;
// information for the benchmark framework
String teamName = "team 4";
String studentName1 = "Benedikt Mueller";
String studentName2 = "Richard Fallert";
String algorithmType = "genetic";
public void setBenchmarkValues(int runtime, int threads, boolean debug) {
maxRuntime = runtime;
numThreads = threads;
printDebugInformation = debug;
}
public String getAlgorithmName() { return "Genetic-2"; }
public UnifiedPopulation getPopulation()
{
if(placer != null) return placer.getPopulation();
else return null;
}
public PlacementGenetic()
{
}
/**
* Method to run the genetic algorithm to find a good placement.
* @param nodesToPlace a list of all nodes that are to be placed.
* @param allNetworks a list of all networks that connect the nodes.
* @param cellName the name of the cell being placed.
*/
public void runPlacement(List<PlacementNode> nodesToPlace, List<PlacementNetwork> allNetworks, String cellName)
{
placer = new GeneticPlacer(nodesToPlace, allNetworks, maxRuntime, numThreads, printDebugInformation);
placer.runPlacement(nodesToPlace, allNetworks);
}
}