/* -*- 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, Oracle and/or its affiliates. All rights reserved.
*
* 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 com.sun.electric.tool.placement.PlacementFrame.PlacementParameter;
import java.util.ArrayList;
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 PlacementParameter maxRuntimeParam = new PlacementParameter("runtime", "Runtime (seconds):", 240);
// number of threads
public PlacementParameter maxThreadsParam = new PlacementParameter("threads", "Number of threads:", 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) {
this.setParamterValues(this.maxThreadsParam.getIntValue(), this.maxRuntimeParam.getIntValue());
placer = new GeneticPlacer(nodesToPlace, allNetworks, runtime, numOfThreads, printDebugInformation);
placer.runPlacement(nodesToPlace, allNetworks);
}
}