ModelFactory.java
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package mior.model;
import mior.model.dist.LinearDistribution;
import mior.model.dist.StaticDistribution;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;
public class ModelFactory {
private boolean batchModeEnabled;
private boolean randomizePopulations;
private int groupSize;
private int blockSize;
private final static Logger logger = LoggerFactory.getLogger(ModelFactory.class);
public ModelFactory() {
this.batchModeEnabled = true;
this.randomizePopulations = false;
this.groupSize = 1;
this.blockSize = -1;
}
public IMiorModel createModel(int nbMM, int nbOM, int scale, int modelVersion) {
final MiorWorld world = new MiorWorld(nbMM, nbOM, scale);
final IMiorModel model;
logger.info("Using model version v" + modelVersion);
if (modelVersion <= 5 && groupSize > 1) {
throw new UnsupportedOperationException("Mior implementation "
+ modelVersion + " does not support this degree ("
+ groupSize + ") of parallelism.");
}
switch (modelVersion) {
case 0:
model = new CPUMiorModel(world);
break;
case 1:
model = new OCLMiorModel(world);
break;
case 2:
model = new OCLMiorModel2(world);
break;
case 3:
model = new OCLCSRModel(world, false);
break;
case 4:
model = new OCLCSRModel(world, true);
break;
case 5:
model = new OCLCSRModel2(world);
break;
case 6:
model = new OCLParaCSRModel2(nbMM, nbOM, groupSize);
if (blockSize != -1) model.setBlockSize(blockSize);
break;
case 7:
if (randomizePopulations) {
model = new OCLParaCSRModel3(nbMM, nbOM, groupSize, new LinearDistribution(nbMM, nbOM, 0.3));
} else {
model = new OCLParaCSRModel3(nbMM, nbOM, groupSize, new StaticDistribution(nbMM, nbOM));
}
if (blockSize != -1) model.setBlockSize(blockSize);
break;
case 8:
if (randomizePopulations) {
model = new OCLParaCSRModelAlt(nbMM, nbOM, groupSize, new LinearDistribution(nbMM, nbOM, 0.3));
} else {
model = new OCLParaCSRModelAlt(nbMM, nbOM, groupSize, new StaticDistribution(nbMM, nbOM));
}
if (blockSize != -1) model.setBlockSize(blockSize);
break;
case 9:
if (randomizePopulations) {
model = new OCLParaCSRModel4(nbMM, nbOM, groupSize, new LinearDistribution(nbMM, nbOM, 0.3));
} else {
model = new OCLParaCSRModel4(nbMM, nbOM, groupSize, new StaticDistribution(nbMM, nbOM));
}
if (blockSize != -1) model.setBlockSize(blockSize);
break;
default:
throw new RuntimeException("Invalid model version: " + modelVersion);
}
model.setBatchModeEnabled(batchModeEnabled);
model.setRandomEnabled(randomizePopulations);
return model;
}
public void setBatchModeEnabled(boolean batchModeEnabled) {
this.batchModeEnabled = batchModeEnabled;
}
public boolean isBatchModeEnabled() {
return batchModeEnabled;
}
public void setRandomEnabled(boolean randomEnabled) {
this.randomizePopulations = randomEnabled;
}
public boolean isRandomEnabled() {
return randomizePopulations;
}
public void setBlockSize(int blockSize) {
this.blockSize = blockSize;
}
public int getBlockSize() {
return blockSize;
}
public void setGroupSize(int kernelSize) {
this.groupSize = kernelSize;
}
public int getGroupSize() {
return groupSize;
}
}