eclipse-tools/responseTime-analyzer/plugins/org.eclipse.app4mc.gsoc_rta/src/org/eclipse/app4mc/gsoc_rta/Contention.java

/*******************************************************************************
 * Copyright (c) 2019 Dortmund University of Applied Sciences and Arts.
 *
 * This program and the accompanying materials
 * are made available under the terms of the Eclipse Public License 2.0
 * which accompanies this distribution, and is available at
 * https://www.eclipse.org/legal/epl-2.0/
 *
 * SPDX-License-Identifier: EPL-2.0
 *
 * Contributors:
 *     FH Dortmund - initial API and implementation
 *******************************************************************************/
package org.eclipse.app4mc.gsoc_rta;

import java.math.BigInteger;
import java.util.ArrayList;
import java.util.HashMap;
import java.util.List;
import java.util.Set;
import java.util.stream.Collectors;

import org.eclipse.app4mc.amalthea.model.Amalthea;
import org.eclipse.app4mc.amalthea.model.CallSequenceItem;
import org.eclipse.app4mc.amalthea.model.InterProcessStimulus;
import org.eclipse.app4mc.amalthea.model.InterProcessTrigger;
import org.eclipse.app4mc.amalthea.model.Label;
import org.eclipse.app4mc.amalthea.model.LabelAccess;
import org.eclipse.app4mc.amalthea.model.LabelAccessEnum;
import org.eclipse.app4mc.amalthea.model.Process;
import org.eclipse.app4mc.amalthea.model.ProcessingUnit;
import org.eclipse.app4mc.amalthea.model.PuType;
import org.eclipse.app4mc.amalthea.model.Runnable;
import org.eclipse.app4mc.amalthea.model.SetEvent;
import org.eclipse.app4mc.amalthea.model.Task;
import org.eclipse.app4mc.amalthea.model.TaskRunnableCall;
import org.eclipse.app4mc.amalthea.model.Time;
import org.eclipse.app4mc.amalthea.model.TimeUnit;
import org.eclipse.app4mc.amalthea.model.util.FactoryUtil;
import org.eclipse.app4mc.amalthea.model.util.SoftwareUtil;
import org.eclipse.emf.common.util.EList;

public class Contention {
	/*
	 * this class will give the worst case latency for each task when memory
	 * contention happen.
	 */

	private final int[] ia;
	private final Amalthea model;
	private final int[] flagArray;
	private final HashMap<Task, List<Label>[]> gpuLabels = new HashMap<Task, List<Label>[]>();

	public Contention(final int[] iap, final Amalthea modelp) {
		this.ia = iap;
		this.model = modelp;
		if (iap == null) {
			this.flagArray = null;
		} else {
			this.flagArray = new int[iap.length];
		}
	}

	/**
	 * Checking whether ProcessingUnit is GPU or CPU
	 * @param pu
	 * @return true for CPU and false for GPU
	 */

	public static boolean identifyPUType(final ProcessingUnit pu) {
		/* true=CPU; false=GPU; */
		boolean result = false;

		if (pu.getDefinition().getPuType().equals(PuType.CPU)) {
			result = true;
		}

		else {
			result = false;
		}
		return result;
	}

	/**
	 * 	Initiallize hashmap contain GPU tasks' labels hashmap <task, List<label>> (readList = hashmap.get(task)[0];)
	 * and create flagarray that have the same length with our integerArray, then loop through all the PRE_POST tasks ( via looking for InterProcessStimulus) and check whether the triggered task (GPU task) are located on GPU or not, if it is, then I will give the value of that task in flagArray equal 1. It will look like this

		ia = { 5, 1, 5, 0, 1, 0, 2, 1, 2, 1, 6, 3, 4, 6 } - our mapping array
		fa = { 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0,} - the flag array
	 *
	 * @param tpumapping (or ia)
	 */
	private void setUpFlagArrayAndHashMap(final int[] iap) {
		final EList<Task> modelTask = this.model.getSwModel().getTasks();
		/* get gpu task list via checking task with InterProcessStimulus*/

		final List<Task> gpuTask = modelTask.stream().filter(a -> a.getStimuli().get(0) instanceof InterProcessStimulus).collect(Collectors.toList());
		/* get shotting task start*/
		for (final Task t : gpuTask) {
			/* shotting task => t.getName */
			final List<SetEvent> shottingTaskEvent = SoftwareUtil.collectSetEvents(t, null);
			final Process shottingTask = shottingTaskEvent.get(0).getProcess();
			final int shotIndex = this.model.getSwModel().getTasks().indexOf(shottingTask);
			final int gpuIndex = this.model.getSwModel().getTasks().indexOf(t);
			if (iap[gpuIndex] != 6) {
				/* If GPU is at GPU task then no flag */
				this.flagArray[shotIndex] = 1;
			}
			/* create HashMap for GPU task that mapped to CPU start here*/
			final List<Label> readLabelList = new ArrayList<Label>();
			final List<Label> writeLabelList = new ArrayList<Label>();
			final List<CallSequenceItem> callList = SoftwareUtil.collectCalls(shottingTask);
			final CallSequenceItem ipt = callList.stream().filter(a -> a instanceof InterProcessTrigger).iterator().next();
			/*
			 * get the position of InterProcessTrigger within task taking
			 * readLabel and write labels from pre and post processing (
			 * preprocessing happen before trigger and post_processing happen
			 * after the trigger )
			 */
			final int indexforTrigger = callList.indexOf(ipt);
			for (int i = 0; i < callList.size(); i++) {
				Runnable thisRunnable = null;
				/* Pre-processing Runnable */
				if ((i < indexforTrigger) && (callList.get(i) instanceof TaskRunnableCall)) {
					thisRunnable = ((TaskRunnableCall) callList.get(i)).getRunnable();
					final List<LabelAccess> thisLAList = SoftwareUtil.getLabelAccessList(thisRunnable, null);
					for (final LabelAccess la : thisLAList) {
						if (la.getAccess().equals(LabelAccessEnum.READ)) {
							readLabelList.add(la.getData());
						}
					}
				}
				/* Post-processing Runnable */
				else if ((i > indexforTrigger) && (callList.get(i) instanceof TaskRunnableCall)) {
					thisRunnable = ((TaskRunnableCall) callList.get(i)).getRunnable();
					final List<LabelAccess> thisLAList = SoftwareUtil.getLabelAccessList(thisRunnable, null);
					for (final LabelAccess la : thisLAList) {
						if (la.getAccess().equals(LabelAccessEnum.WRITE)) {
							writeLabelList.add(la.getData());
						}
					}
				}
			}

			@SuppressWarnings("unchecked")
			final List<Label>[] aryofLabelList = new ArrayList[2];
			aryofLabelList[0] = readLabelList;
			aryofLabelList[1] = writeLabelList;
			this.gpuLabels.put(t, aryofLabelList);
			// HashMap created with <Task, ArrayofLabelList>
		}

	}

	/** Calculating contention for CPU task only
	 *
	 * @param task
	 * @return time
	 */

	public Time contentionForTask(final Task task) {
		/* Initialize hashmap */
		setUpFlagArrayAndHashMap(this.ia);

		final Time time = FactoryUtil.createTime(BigInteger.ZERO, TimeUnit.PS);
		/**
		 * Constant and variable init, change those constant for different model number
		 *
		 * The arrayList below are used to find number of core interfering and also for easier debugging
		 * b1 = current task's name
		 * b2 = intefering task's name
		 * c1 = current task's core
		 * c2 = intefering task's core
		 * c3 = core definition's name (to check is task PU to apply the appropiate calculation)
		 * d2 = c2 but reduced duplication
		 */
		final ArrayList<String> b1 = new ArrayList<>();
		final ArrayList<String> b2 = new ArrayList<>();
		final ArrayList<String> c1 = new ArrayList<>();
		final ArrayList<String> c2 = new ArrayList<>();
		final ArrayList<String> c3 = new ArrayList<>();
		ArrayList<String> d2 = new ArrayList<>();

		/* name of GPU core, use for identification task's core while calculating */
		final String GPU = "GPU";
		/* name of CPU core, use for identification task's core while calculating
		 * can't use the identifyPU type above to distinguished because we have 2 different CPU in this model*/
		final String A57CPU = "A57";
		final String DenverCPU = "Denver";

		/* time taken to read or write a cacheLine (64B)*/
		final double A57_Baseline = 20.0;
		final double Denver_Baseline = 8.0;
		final double GPU_Baseline = 3.0;
		/*increase in latency operated by a single interfering core.
		 *  Do note: it does  not matter if the interfering core is Denver or A57
		 *  : this number only depends on the observed CPU core (CPUtype)*/


		final double A57_KConstant = 20.0;
		final double Denver_KConstant = 2.0;

		/* sensibility to GPU CE activity.*/
		final double A57_sGPU = 100.0;
		final double Denver_sGPU = 20.0;

		/*bGPU = boolean, 1: if GPU is operating the copy engine, 0 other wise
		 * We are assuming WCET so this will always be 1 in this implementation*/
		final double bGPU = 1.0;

		/*Computation to get NUMBER OF CORE intefering start*/

		final List<ProcessingUnit> pul = CommonUtils.getPUs(this.model);
		for (final Task t : this.model.getSwModel().getTasks()) {
			if (t.equals(task)) {

				/* Checking if task is flagged or not, because
				 * if GPU task is on CPU => PREPOST will not be considered */

				final int currentTaskIndex = this.model.getSwModel().getTasks().indexOf(task);
				if (this.flagArray[currentTaskIndex] == 1) {
					return time;
				}

				for (final Label l : CommonUtils.getAccessedLabelSet(t)) {

					final Set<Runnable> wL = SoftwareUtil.getWriterSetOfLabel(l, null);
					for (final Runnable r : wL) {
						final List<Process> lT = SoftwareUtil.getProcesses(r, null);
						for (final Process lTT : lT) {
							/* get related task through checking which runnable are accessing the same label
							 * put the task that contain those runnable into b2 and the core of that task in c2
							 */
							for (int taskIndex = 0; taskIndex < this.ia.length; taskIndex++) {
								final ProcessingUnit pup = pul.get(this.ia[taskIndex]);
								final String taskName = this.model.getSwModel().getTasks().get(taskIndex).getName();
								final String coreName = pup.getName();
								if (t.getName().equals(taskName)) {
									b1.add(taskName);
									c1.add(coreName);
									c3.add(pup.getDefinition().getName());
								}
								final int b2Index = this.model.getSwModel().getTasks().indexOf(lTT);
								/*only put that task in our array if task isn't flagged (check for
								 * unnecessary PRE_POST task, we assume those task are gone when
								 * corresponded GPU task is mapped to CPU)*/
								if (lTT.getName().equals(taskName) && (this.flagArray[b2Index] == 0)) {
									b2.add(taskName);
									c2.add(coreName);
								}
							}
						}
					}


					final Set<Runnable> rL = SoftwareUtil.getReadersSetOfLabel(l, null);
					for (final Runnable r : rL) {
						final List<Process> lT1 = SoftwareUtil.getProcesses(r, null);
						for (final Process lTT1 : lT1) {
							/* get related task through checking which runnable are accessing the same label
							 * put the task that contain those runnable into b2 and the core of that task in c2
							 */
							for (int taskIndex = 0; taskIndex < this.ia.length; taskIndex++) {
								final ProcessingUnit pup = pul.get(this.ia[taskIndex]);

								final String taskName = this.model.getSwModel().getTasks().get(taskIndex).getName();
								final String coreName = pup.getName();
								if (t.getName().equals(taskName)) {
									b1.add(taskName);
									c1.add(coreName);
									c3.add(pup.getDefinition().getName());

								}
								/*only put that task in our array if task isn't flagged (check for
								 * unnecessary PRE_POST task, we assume those task are gone when
								 * corresponded GPU task is mapped to CPU)*/

								final int b2Index1 = this.model.getSwModel().getTasks().indexOf(lTT1);

								if (lTT1.getName().equals(taskName) && (this.flagArray[b2Index1] == 0)) {
									b2.add(taskName);
									c2.add(coreName);
								}
							}
						}
					}

				}

				/* Remove all the duplicates inside c2.*/
				d2 = (ArrayList<String>) c2.stream().distinct().collect(Collectors.toList());

				/* Hashmap of <Core, Integer>
				 * Assign value for other task as 1, GPU task and observed task as 0
				 * total of value in integer will be equal to number of core intefering
				 * Output of hashMap look like this {Core3=1, Core4=1, Core0=0, GP10B=0}*/

				final HashMap<Object, Integer> coreHashMap = new HashMap<Object, Integer>();
				for (final ProcessingUnit pu : pul) {
					final String puName = pu.getName();
					for (final String cName : d2) {
						if (cName.equals(puName)) {
							if (identifyPUType(pu)) {
								coreHashMap.put(puName, 1);
								if (puName.equals(c1.get(0))) {
									coreHashMap.put(puName, 0);
								}
							}

							else {
								coreHashMap.put(puName, 0);
							}
						}
					}
				}

				/*calculate total number of core via summing up all value in hashmap*/
				int NoOfCore = 0;
				for (final int val : coreHashMap.values()) {
					NoOfCore += val;
				}
				/*Memory contention calculation for CPU
				 * 	Lat = baseline + K*C + sGPU*bGPU
				 * Lat = time necessary to read/write 64B ( nanosecond )
				 * 64B I repeat. Scale the Lat accordingly to the memory footprint size
				 * NoOfCore = C in the formula above.
				 */
				double Lat = 0.0;

				if (!c3.isEmpty()) {
					if (c3.get(0).contains(A57CPU)) {
						Lat = A57_Baseline + (A57_KConstant * NoOfCore) + (A57_sGPU * bGPU);
					}
					if (c3.get(0).contains(DenverCPU)) {
						Lat = Denver_Baseline + (Denver_KConstant * NoOfCore) + (Denver_sGPU * bGPU);
					}

					/*Memory contention calculation for GPU
					 * 	Lat = GPU_Baseline + 0.5*C
					 * Lat = time necessary to transfer 64B with copy engine ( nanosecond )
					 * 64B I repeat. Scale the Lat accordingly to the memory footprint size
					 * NoOfCore = C in the formula above. */

					if (c3.get(0).contains(GPU)) {
						Lat = GPU_Baseline + 0.5 * NoOfCore;
					}
				}

				/* Latency for 64B calculation end here*/

				long sumCycle = 0;


				/*get data size of task start here */
				final EList<Task> modelTask = this.model.getSwModel().getTasks();
				final List<Task> gpuTask = modelTask.stream().filter(a -> a.getStimuli().get(0) instanceof InterProcessStimulus).collect(Collectors.toList());

				/*if task is mapped to GPU, use the label within hashmap */
				if (gpuTask.contains(task)) {
					final List<SetEvent> shottingTaskEvent = SoftwareUtil.collectSetEvents(task, null);
					final Process shottingTask = shottingTaskEvent.get(0).getProcess();
					/*checking whether shotting task (triggering task) is flagged or not
					 * if it is flagged, then the current GPU task is on CPU, hence using label in hashmap
					 * if it is not, then the current GPU task is on GPU, therefore no need to look further more*/
					final int shotIndex = this.model.getSwModel().getTasks().indexOf(shottingTask);

					if (this.flagArray[shotIndex] == 1) {
						final List<Label> readList = this.gpuLabels.get(task)[0];
						final List<Label> writeList = this.gpuLabels.get(task)[1];
						for (final Label l : readList) {

							sumCycle += (long) Math.ceil(l.getSize().getNumberBytes() / 64.0);

						}

						for (final Label l : writeList) {
							sumCycle += (long) Math.ceil(l.getSize().getNumberBytes() / 64.0);
						}
					}

				}

				/*normal calculation of total label within task for contention calculation
				 * this is what we use most of the time*/
				else {
					for (final Runnable r : SoftwareUtil.getRunnableList(t, null)) {
						for (final Label rl : SoftwareUtil.getReadLabelSet(r, null)) {
							sumCycle += (long) Math.ceil(rl.getSize().getNumberBytes() / 64.0);
						}

						for (final Label wl : SoftwareUtil.getWriteLabelSet(r, null)) {
							sumCycle += (long) Math.ceil(wl.getSize().getNumberBytes() / 64.0);
						}
					}

				}


				/* No core = no interference => No contention*/
				if (NoOfCore == 0) {
					Lat = 0;
				}

				/* task's contention in picosecond */
				//
				double taskLat = 0.0;
				taskLat = Lat * sumCycle;

				time.setValue(BigInteger.valueOf((long) taskLat * 1000l));
				/*Clear out the Array for next task, print console before this if want to check */
				b1.clear();
				b2.clear();
				c1.clear();
				c2.clear();
				d2.clear();
				c3.clear();
			}

		}
		return time;
	}
	// contentionForTask function end here


	/**
	 * Contention for GPU CopyEngine only, when copyengine perform copyIn
	 * and copyOut. Mostly same with contention for CPU task,
	 * except we only considering written label when copyOut
	 * (since no need to copy back read label)
	 * @param task
	 * @return time
	 */

	public Time contentionForGPUCE(final Task task) {
		setUpFlagArrayAndHashMap(this.ia);
		final Time contentionTime = FactoryUtil.createTime(BigInteger.ZERO, TimeUnit.PS);
		/**
		 * Constant and variable init, change those constant for different model number
		 *
		 * The arrayList below are used to find number of core interfering and also for easier debugging
		 * b1 = current task's name
		 * b2 = intefering task's name
		 * c1 = current task's core
		 * c2 = intefering task's core
		 * c3 = core definition's name (to check is task PU to apply the appropiate calculation)
		 * d2 = c2 but reduced duplication
		 */

		final ArrayList<String> b1 = new ArrayList<>();
		final ArrayList<String> b2 = new ArrayList<>();
		final ArrayList<String> c1 = new ArrayList<>();
		final ArrayList<String> c2 = new ArrayList<>();
		final ArrayList<String> c3 = new ArrayList<>();
		ArrayList<String> d2 = new ArrayList<>();

		/* name of GPU core, use for identification task's core while calculating */
		final String GPU = "GPU";
		/* name of CPU core, use for identification task's core while calculating
		 * can't use the identifyPU type above to distinguished because we have 2 different CPU in this model*/
		final String A57CPU = "A57";
		final String DenverCPU = "Denver";

		/* time taken to read or write a cacheLine (64B)*/
		final double A57_Baseline = 20.0;
		final double Denver_Baseline = 8.0;
		final double GPU_Baseline = 3.0;
		/*increase in latency operated by a single interfering core.
		 *  Do note: it does  not matter if the interfering core is Denver or A57
		 *  : this number only depends on the observed CPU core (CPUtype)*/

		final double A57_KConstant = 20.0;
		final double Denver_KConstant = 2.0;

		/* sensibility to GPU CE activity.*/
		final double A57_sGPU = 100.0;
		final double Denver_sGPU = 20.0;

		/*bGPU = boolean, 1: if GPU is operating the copy engine, 0 other wise
		 * We are assuming WCET so this will always be 1 in this implementation*/
		final double bGPU = 1.0;

		/*Computation to get NUMBER OF CORE intefering start*/
		final List<ProcessingUnit> pul = CommonUtils.getPUs(this.model);
		final EList<Task> modelTask = this.model.getSwModel().getTasks();
		// get gpu task start
		final List<Task> gpuTask = modelTask.stream().filter(a -> a.getStimuli().get(0) instanceof InterProcessStimulus).collect(Collectors.toList());
		// get gpu task finished
		for (final Task t : gpuTask) {
			if (t.equals(task)) {
				for (final Label l : CommonUtils.getAccessedLabelSet(t)) {

					final Set<Runnable> wL = SoftwareUtil.getWriterSetOfLabel(l, null);
					for (final Runnable r : wL) {
						final List<Process> lT = SoftwareUtil.getProcesses(r, null);
						for (final Process lTT : lT) {

							for (int taskIndex = 0; taskIndex < this.ia.length; taskIndex++) {
								final ProcessingUnit pup = pul.get(this.ia[taskIndex]);
								final String taskName = this.model.getSwModel().getTasks().get(taskIndex).getName();
								final String coreName = pup.getName();
								/* get related task through checking which runnable are accessing the same label
								 * put the task that contain those runnable into b2 and the core of that task in c2
								 */

								if (t.getName().equals(taskName)) {
									b1.add(taskName);
									c1.add(coreName);
									c3.add(pup.getDefinition().getName());
								}
								/*only put that task in our array if task isn't flagged (check for
								 * unnecessary PRE_POST task, we assume those task are gone when
								 * corresponded GPU task is mapped to CPU)*/

								final int b2Index = this.model.getSwModel().getTasks().indexOf(lTT);
								if (lTT.getName().equals(taskName) && (this.flagArray[b2Index] == 0)) {
									b2.add(taskName);
									c2.add(coreName);
								}
							}
						}
					}
					final Set<Runnable> rL = SoftwareUtil.getReadersSetOfLabel(l, null);
					for (final Runnable r : rL) {
						final List<Process> lT1 = SoftwareUtil.getProcesses(r, null);
						for (final Process lTT1 : lT1) {
							for (int taskIndex = 0; taskIndex < this.ia.length; taskIndex++) {
								final ProcessingUnit pup = pul.get(this.ia[taskIndex]);
								final String taskName = this.model.getSwModel().getTasks().get(taskIndex).getName();
								final String coreName = pup.getName();
								/* get related task through checking which runnable are accessing the same label
								 * put the task that contain those runnable into b2 and the core of that task in c2
								 */
								if (t.getName().equals(taskName)) {
									b1.add(taskName);
									c1.add(coreName);
									c3.add(pup.getDefinition().getName());

								}

								/*only put that task in our array if task isn't flagged (check for
								 * unnecessary PRE_POST task, we assume those task are gone when
								 * corresponded GPU task is mapped to CPU)*/
								final int b2Index1 = this.model.getSwModel().getTasks().indexOf(lTT1);
								if (lTT1.getName().equals(taskName) && (this.flagArray[b2Index1] == 0)) {
									b2.add(taskName);
									c2.add(coreName);
								}
							}
						}
					}

				}
				// Remove all the duplicates inside c2.
				d2 = (ArrayList<String>) c2.stream().distinct().collect(Collectors.toList());

				/* Hashmap of <Core, Integer>
				 * Assign value for other task as 1, GPU task and observed task as 0
				 * total of value in integer will be equal to number of core intefering
				 * Output of hashMap look like this {Core3=1, Core4=1, Core0=0, GP10B=0}*/
				final HashMap<Object, Integer> coreHashMap = new HashMap<Object, Integer>();
				for (final ProcessingUnit pu : pul) {
					final String puName = pu.getName();
					for (final String cName : d2) {
						if (cName.equals(puName)) {
							if (identifyPUType(pu)) {
								coreHashMap.put(puName, 1);
								if (puName.equals(c1.get(0))) {
									coreHashMap.put(puName, 0);
								}
							}
							else {
								coreHashMap.put(puName, 0);
							}
						}
					}
				}
				int NoOfCore = 0;
				for (final int val : coreHashMap.values()) {
					NoOfCore += val;
				}
				/*Memory contention calculation for CPU
				 * 	Lat = baseline + K*C + sGPU*bGPU
				 * Lat = time necessary to read/write 64B ( nanosecond )
				 * 64B I repeat. Scale the Lat accordingly to the memory footprint size
				 * NoOfCore = C in the formula above.
				 */
				double Lat = 0.0;
				if (!c3.isEmpty()) {
					if (c3.get(0).contains(A57CPU)) {
						Lat = A57_Baseline + (A57_KConstant * NoOfCore) + (A57_sGPU * bGPU);
					}
					if (c3.get(0).contains(DenverCPU)) {
						Lat = Denver_Baseline + (Denver_KConstant * NoOfCore) + (Denver_sGPU * bGPU);
					}
					/*Memory contention calculation for GPU
					 * 	Lat = GPU_Baseline + 0.5*C
					 * Lat = time necessary to transfer 64B with copy engine ( nanosecond )
					 * 64B I repeat. Scale the Lat accordingly to the memory footprint size
					 * NoOfCore = C in the formula above. */

					if (c3.get(0).contains(GPU)) {
						Lat = GPU_Baseline + 0.5 * NoOfCore;
					}
				}
				/* Latency for 64B calculation end here*/
				long sumCycle = 0;
				/* Full read and write labels for CE when copyIN */
				final Set<Label> fullList = SoftwareUtil.getAccessedLabelSet(t, null);
				for (final Label l : fullList) {
					final long fullSize = l.getSize().getNumberBytes();
					final long cycle = (long) Math.ceil(fullSize / 64.0);
					sumCycle += cycle;
				}


				/* Written labels only for CE when CopyOut*/
				final Set<Label> writeList = SoftwareUtil.getWriteLabelSet(t, null);
				for (final Label l : writeList) {
					final long fullSize = l.getSize().getNumberBytes();
					final long cycle = (long) Math.ceil(fullSize / 64.0);
					sumCycle += cycle;
				}


				// No core = no interference => No contention
				if (NoOfCore == 0) {
					Lat = 0;
				}
				double taskLat = 0.0;
				taskLat = Lat * sumCycle;

				contentionTime.setValue(BigInteger.valueOf((long) taskLat * 1000l));

				/*Clear out the Array for next task, print console before this if want to check */
				b1.clear();
				b2.clear();
				c1.clear();
				c2.clear();
				d2.clear();
				c3.clear();
			}

		}
		// continue

		return contentionTime;
	}
}