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Declarative Machine Learning
/*
* 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.instructions.spark.data;
import org.apache.spark.Partitioner;
import org.apache.sysml.runtime.matrix.MatrixCharacteristics;
import org.apache.sysml.runtime.matrix.data.MatrixIndexes;
/**
* Default partitioner used for all binary block rdd operations in order
* to enable sufficient local aggregation independent of the aggregation
* direction (row/col-wise). Especially, on large squared matrices
* (as common for factorization or graph algorithms), this is crucial
* for performance.
*
*/
public class BlockPartitioner extends Partitioner
{
private static final long serialVersionUID = 3207938407732880324L;
private int _numParts = -1;
private int _ncparts = -1;
private long _rbPerPart = -1;
private long _cbPerPart = -1;
public BlockPartitioner(MatrixCharacteristics mc, int numParts)
{
//sanity check known dimensions
if( !mc.dimsKnown() || mc.getRowsPerBlock()<1 || mc.getColsPerBlock()<1 ) {
throw new RuntimeException("Invalid unknown matrix characteristics.");
}
//prepare meta data
long nrblks = mc.getNumRowBlocks();
long ncblks = mc.getNumColBlocks();
long nblks = nrblks * ncblks;
//compute perfect squared tile-size (via flooring to
//avoid empty partitions; overflow handled via mod numParts)
double nblksPerPart = Math.max((double)nblks/numParts,1);
long dimBlks = (long)Math.max(Math.floor(Math.sqrt(nblksPerPart)),1);
//adjust tile shape according to matrix shape
if( nrblks < dimBlks ) { //short and fat
_rbPerPart = nrblks;
_cbPerPart = (long)Math.max(Math.floor(nblksPerPart/_rbPerPart),1);
}
else if( ncblks < dimBlks ) { //tall and skinny
_cbPerPart = ncblks;
_rbPerPart = (long)Math.max(Math.floor(nblksPerPart/_cbPerPart),1);
}
else { //general case
_rbPerPart = dimBlks;
_cbPerPart = dimBlks;
}
//compute meta data for runtime
_ncparts = (int)Math.ceil((double)ncblks/_cbPerPart);
_numParts = numParts;
}
@Override
public int getPartition(Object arg0)
{
//sanity check for valid class
if( !(arg0 instanceof MatrixIndexes) ) {
throw new RuntimeException("Unsupported key class "
+ "(expected MatrixIndexes): "+arg0.getClass().getName());
}
//get partition id
MatrixIndexes ix = (MatrixIndexes) arg0;
int ixr = (int)((ix.getRowIndex()-1)/_rbPerPart);
int ixc = (int)((ix.getColumnIndex()-1)/_cbPerPart);
int id = ixr * _ncparts + ixc;
//ensure valid range
return id % _numParts;
}
@Override
public int numPartitions() {
return _numParts;
}
@Override
public boolean equals(Object obj)
{
if( !(obj instanceof BlockPartitioner) )
return false;
BlockPartitioner that = (BlockPartitioner) obj;
return _numParts == that._numParts
&& _ncparts == that._ncparts
&& _rbPerPart == that._rbPerPart
&& _cbPerPart == that._cbPerPart;
}
}