org.apache.sysml.runtime.controlprogram.parfor.mqo.RuntimePiggybackingUtils Maven / Gradle / Ivy

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Declarative Machine Learning

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/*
 * Licensed to the Apache Software Foundation (ASF) under one
 * or more contributor license agreements.  See the NOTICE file
 * distributed with this work for additional information
 * regarding copyright ownership.  The ASF licenses this file
 * to you under the Apache License, Version 2.0 (the
 * "License"); you may not use this file except in compliance
 * with the License.  You may obtain a copy of the License at
 * 
 *   http://www.apache.org/licenses/LICENSE-2.0
 * 
 * Unless required by applicable law or agreed to in writing,
 * software distributed under the License is distributed on an
 * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
 * KIND, either express or implied.  See the License for the
 * specific language governing permissions and limitations
 * under the License.
 */

package org.apache.sysml.runtime.controlprogram.parfor.mqo;

import java.io.IOException;
import org.apache.sysml.runtime.controlprogram.parfor.stat.InfrastructureAnalyzer;
import org.apache.sysml.yarn.ropt.YarnClusterAnalyzer;

public class RuntimePiggybackingUtils 
{

	/**
	 * In general the cluster utilization reflects the percentage of
	 * currently used resources relative to maximum resources.
	 * 
	 * On MR1, we compute this by the number of occupied and maxmimum
	 * map/reduce slots. 
	 * On YARN, we use the memory consumption and virtual cores as an indicator of 
	 * the cluster utilization since the binary compatible API returns
	 * always a constant of 1 for occupied slots.
	 * 
	 * @return
	 * @throws IOException 
	 */
	public static double getCurrentClusterUtilization() 
		throws IOException
	{
		double util = 0;
		
		if( InfrastructureAnalyzer.isYarnEnabled() )
			util = YarnClusterAnalyzer.getClusterUtilization();
		else
			util = InfrastructureAnalyzer.getClusterUtilization(true);
		
		return util;
	}
}