Examples with DistributedCacheInput org.apache.sysml.runtime.matrix.mapred.DistributedCacheInput used on opensource projects

Example 6 with DistributedCacheInput

use of org.apache.sysml.runtime.matrix.mapred.DistributedCacheInput in project incubator-systemml by apache.

the class MapMultChainInstruction method processXtXvOperations.

/**
 * Chain implementation for r = (t(X)%*%(X%*%v))
 * (implemented as r = (t(t(X%*%v)%*%X))
 *
 * @param inIx input matrix indexes
 * @param inVal input matrix value
 * @param outIx output matrix indexes
 * @param outVal output matrix value
 */
private void processXtXvOperations(MatrixIndexes inIx, MatrixValue inVal, MatrixIndexes outIx, MatrixValue outVal) {
    // v
    DistributedCacheInput dcInput2 = MRBaseForCommonInstructions.dcValues.get(_input2);
    MatrixBlock Xi = (MatrixBlock) inVal;
    MatrixBlock v = (MatrixBlock) dcInput2.getDataBlock(1, 1).getValue();
    // process core block operation
    Xi.chainMatrixMultOperations(v, null, (MatrixBlock) outVal, ChainType.XtXv);
    outIx.setIndexes(1, 1);
}

Example 7 with DistributedCacheInput

use of org.apache.sysml.runtime.matrix.mapred.DistributedCacheInput in project incubator-systemml by apache.

the class PMMJMRInstruction method processInstruction.

@Override
public void processInstruction(Class<? extends MatrixValue> valueClass, CachedValueMap cachedValues, IndexedMatrixValue tempValue, IndexedMatrixValue zeroInput, int blockRowFactor, int blockColFactor) {
    // get both matrix inputs (left side always permutation)
    DistributedCacheInput dcInput = MRBaseForCommonInstructions.dcValues.get(input1);
    IndexedMatrixValue in2 = cachedValues.getFirst(input2);
    IndexedMatrixValue in1 = dcInput.getDataBlock((int) in2.getIndexes().getRowIndex(), 1);
    MatrixBlock mb1 = (MatrixBlock) in1.getValue();
    MatrixBlock mb2 = (MatrixBlock) in2.getValue();
    // compute target block indexes
    long minPos = UtilFunctions.toLong(mb1.minNonZero());
    long maxPos = UtilFunctions.toLong(mb1.max());
    long rowIX1 = (minPos - 1) / blockRowFactor + 1;
    long rowIX2 = (maxPos - 1) / blockRowFactor + 1;
    boolean multipleOuts = (rowIX1 != rowIX2);
    if (// at least one row selected
    minPos >= 1) {
        // output sparsity estimate
        double spmb1 = OptimizerUtils.getSparsity(mb1.getNumRows(), 1, mb1.getNonZeros());
        long estnnz = (long) (spmb1 * mb2.getNonZeros());
        boolean sparse = MatrixBlock.evalSparseFormatInMemory(blockRowFactor, mb2.getNumColumns(), estnnz);
        // compute and allocate output blocks
        IndexedMatrixValue out1 = cachedValues.holdPlace(output, valueClass);
        IndexedMatrixValue out2 = multipleOuts ? cachedValues.holdPlace(output, valueClass) : null;
        out1.getValue().reset(blockRowFactor, mb2.getNumColumns(), sparse);
        if (out2 != null)
            out2.getValue().reset(UtilFunctions.computeBlockSize(_rlen, rowIX2, blockRowFactor), mb2.getNumColumns(), sparse);
        // compute core matrix permutation (assumes that out1 has default blocksize,
        // hence we do a meta data correction afterwards)
        mb1.permutationMatrixMultOperations(mb2, out1.getValue(), (out2 != null) ? out2.getValue() : null);
        ((MatrixBlock) out1.getValue()).setNumRows(UtilFunctions.computeBlockSize(_rlen, rowIX1, blockRowFactor));
        out1.getIndexes().setIndexes(rowIX1, in2.getIndexes().getColumnIndex());
        if (out2 != null)
            out2.getIndexes().setIndexes(rowIX2, in2.getIndexes().getColumnIndex());
        // empty block output filter (enabled by compiler consumer operation is in CP)
        if (!_outputEmptyBlocks && out1.getValue().isEmpty() && (out2 == null || out2.getValue().isEmpty())) {
            cachedValues.remove(output);
        }
    }
}

Example 8 with DistributedCacheInput

use of org.apache.sysml.runtime.matrix.mapred.DistributedCacheInput in project incubator-systemml by apache.

the class UaggOuterChainInstruction method processInstruction.

@Override
public void processInstruction(Class<? extends MatrixValue> valueClass, CachedValueMap cachedValues, IndexedMatrixValue tempValue, IndexedMatrixValue zeroInput, int blockRowFactor, int blockColFactor) {
    ArrayList<IndexedMatrixValue> blkList = null;
    boolean rightCached = (_uaggOp.indexFn instanceof ReduceCol || _uaggOp.indexFn instanceof ReduceAll || !LibMatrixOuterAgg.isSupportedUaggOp(_uaggOp, _bOp));
    // get the main data input
    if (rightCached)
        blkList = cachedValues.get(input1);
    else
        // ReduceRow
        blkList = cachedValues.get(input2);
    if (blkList == null)
        return;
    for (IndexedMatrixValue imv : blkList) {
        if (imv == null)
            continue;
        MatrixIndexes in1Ix = imv.getIndexes();
        MatrixValue in1Val = imv.getValue();
        // allocate space for the intermediate and output value
        IndexedMatrixValue iout = cachedValues.holdPlace(output, valueClass);
        MatrixIndexes outIx = iout.getIndexes();
        MatrixValue outVal = iout.getValue();
        MatrixBlock corr = null;
        // get the distributed cache input
        byte dcInputIx = rightCached ? input2 : input1;
        DistributedCacheInput dcInput = MRBaseForCommonInstructions.dcValues.get(dcInputIx);
        // process instruction
        if (LibMatrixOuterAgg.isSupportedUaggOp(_uaggOp, _bOp)) {
            if ((LibMatrixOuterAgg.isRowIndexMax(_uaggOp)) || (LibMatrixOuterAgg.isRowIndexMin(_uaggOp))) {
                if (_bv == null) {
                    if (rightCached)
                        _bv = dcInput.getRowVectorArray();
                    else
                        _bv = dcInput.getColumnVectorArray();
                    _bvi = LibMatrixOuterAgg.prepareRowIndices(_bv.length, _bv, _bOp, _uaggOp);
                }
            } else {
                // step 1: prepare sorted rhs input (once per task)
                if (_bv == null) {
                    if (rightCached)
                        _bv = dcInput.getRowVectorArray();
                    else
                        _bv = dcInput.getColumnVectorArray();
                    Arrays.sort(_bv);
                }
            }
            LibMatrixOuterAgg.resetOutputMatrix(in1Ix, (MatrixBlock) in1Val, outIx, (MatrixBlock) outVal, _uaggOp);
            LibMatrixOuterAgg.aggregateMatrix((MatrixBlock) in1Val, (MatrixBlock) outVal, _bv, _bvi, _bOp, _uaggOp);
        } else // default case
        {
            long in2_cols = dcInput.getNumCols();
            long in2_colBlocks = (long) Math.ceil(((double) in2_cols) / dcInput.getNumColsPerBlock());
            for (int bidx = 1; bidx <= in2_colBlocks; bidx++) {
                IndexedMatrixValue imv2 = dcInput.getDataBlock(1, bidx);
                MatrixValue in2Val = imv2.getValue();
                // outer block operation
                OperationsOnMatrixValues.performBinaryIgnoreIndexes(in1Val, in2Val, _tmpVal1, _bOp);
                // unary aggregate operation
                OperationsOnMatrixValues.performAggregateUnary(in1Ix, _tmpVal1, outIx, _tmpVal2, _uaggOp, blockRowFactor, blockColFactor);
                // aggregate over all rhs blocks
                if (corr == null) {
                    outVal.reset(_tmpVal2.getNumRows(), _tmpVal2.getNumColumns(), false);
                    corr = new MatrixBlock(_tmpVal2.getNumRows(), _tmpVal2.getNumColumns(), false);
                }
                if (_aggOp.correctionExists)
                    OperationsOnMatrixValues.incrementalAggregation(outVal, corr, _tmpVal2, _aggOp, true);
                else
                    OperationsOnMatrixValues.incrementalAggregation(outVal, null, _tmpVal2, _aggOp, true);
            }
        }
    }
}