Examples with CumulativeOffsetBinary org.apache.sysml.lops.CumulativeOffsetBinary used on opensource projects

Example 1 with CumulativeOffsetBinary

use of org.apache.sysml.lops.CumulativeOffsetBinary in project incubator-systemml by apache.

the class UnaryOp method constructLopsSparkCumulativeUnary.

private Lop constructLopsSparkCumulativeUnary() {
    Hop input = getInput().get(0);
    long rlen = input.getDim1();
    long clen = input.getDim2();
    long brlen = input.getRowsInBlock();
    long bclen = input.getColsInBlock();
    boolean force = !dimsKnown() || _etypeForced == ExecType.SPARK;
    OperationTypes aggtype = getCumulativeAggType();
    Lop X = input.constructLops();
    Lop TEMP = X;
    ArrayList<Lop> DATA = new ArrayList<>();
    int level = 0;
    // recursive preaggregation until aggregates fit into CP memory budget
    while (((2 * OptimizerUtils.estimateSize(TEMP.getOutputParameters().getNumRows(), clen) + OptimizerUtils.estimateSize(1, clen)) > OptimizerUtils.getLocalMemBudget() && TEMP.getOutputParameters().getNumRows() > 1) || force) {
        DATA.add(TEMP);
        // preaggregation per block (for spark, the CumulativePartialAggregate subsumes both
        // the preaggregation and subsequent block aggregation)
        long rlenAgg = (long) Math.ceil((double) TEMP.getOutputParameters().getNumRows() / brlen);
        Lop preagg = new CumulativePartialAggregate(TEMP, DataType.MATRIX, ValueType.DOUBLE, aggtype, ExecType.SPARK);
        preagg.getOutputParameters().setDimensions(rlenAgg, clen, brlen, bclen, -1);
        setLineNumbers(preagg);
        TEMP = preagg;
        level++;
        // in case of unknowns, generate one level
        force = false;
    }
    // in-memory cum sum (of partial aggregates)
    if (TEMP.getOutputParameters().getNumRows() != 1) {
        int k = OptimizerUtils.getConstrainedNumThreads(_maxNumThreads);
        Unary unary1 = new Unary(TEMP, HopsOpOp1LopsU.get(_op), DataType.MATRIX, ValueType.DOUBLE, ExecType.CP, k);
        unary1.getOutputParameters().setDimensions(TEMP.getOutputParameters().getNumRows(), clen, brlen, bclen, -1);
        setLineNumbers(unary1);
        TEMP = unary1;
    }
    // split, group and mr cumsum
    while (level-- > 0) {
        // (for spark, the CumulativeOffsetBinary subsumes both the split aggregate and
        // the subsequent offset binary apply of split aggregates against the original data)
        double initValue = getCumulativeInitValue();
        CumulativeOffsetBinary binary = new CumulativeOffsetBinary(DATA.get(level), TEMP, DataType.MATRIX, ValueType.DOUBLE, initValue, aggtype, ExecType.SPARK);
        binary.getOutputParameters().setDimensions(rlen, clen, brlen, bclen, -1);
        setLineNumbers(binary);
        TEMP = binary;
    }
    return TEMP;
}

Example 2 with CumulativeOffsetBinary

use of org.apache.sysml.lops.CumulativeOffsetBinary in project incubator-systemml by apache.

the class UnaryOp method constructLopsMRCumulativeUnary.

/**
 * MR Cumsum is currently based on a multipass algorithm of (1) preaggregation and (2) subsequent offsetting.
 * Note that we currently support one robust physical operator but many alternative
 * realizations are possible for specific scenarios (e.g., when the preaggregated intermediate
 * fit into the map task memory budget) or by creating custom job types.
 *
 * @return low-level operator
 */
private Lop constructLopsMRCumulativeUnary() {
    Hop input = getInput().get(0);
    long rlen = input.getDim1();
    long clen = input.getDim2();
    long brlen = input.getRowsInBlock();
    long bclen = input.getColsInBlock();
    boolean force = !dimsKnown() || _etypeForced == ExecType.MR;
    OperationTypes aggtype = getCumulativeAggType();
    Lop X = input.constructLops();
    Lop TEMP = X;
    ArrayList<Lop> DATA = new ArrayList<>();
    int level = 0;
    // recursive preaggregation until aggregates fit into CP memory budget
    while (((2 * OptimizerUtils.estimateSize(TEMP.getOutputParameters().getNumRows(), clen) + OptimizerUtils.estimateSize(1, clen)) > OptimizerUtils.getLocalMemBudget() && TEMP.getOutputParameters().getNumRows() > 1) || force) {
        DATA.add(TEMP);
        // preaggregation per block
        long rlenAgg = (long) Math.ceil((double) TEMP.getOutputParameters().getNumRows() / brlen);
        Lop preagg = new CumulativePartialAggregate(TEMP, DataType.MATRIX, ValueType.DOUBLE, aggtype, ExecType.MR);
        preagg.getOutputParameters().setDimensions(rlenAgg, clen, brlen, bclen, -1);
        setLineNumbers(preagg);
        Group group = new Group(preagg, Group.OperationTypes.Sort, DataType.MATRIX, ValueType.DOUBLE);
        group.getOutputParameters().setDimensions(rlenAgg, clen, brlen, bclen, -1);
        setLineNumbers(group);
        Aggregate agg = new Aggregate(group, HopsAgg2Lops.get(AggOp.SUM), getDataType(), getValueType(), ExecType.MR);
        agg.getOutputParameters().setDimensions(rlenAgg, clen, brlen, bclen, -1);
        // aggregation uses kahanSum but the inputs do not have correction values
        agg.setupCorrectionLocation(CorrectionLocationType.NONE);
        setLineNumbers(agg);
        TEMP = agg;
        level++;
        // in case of unknowns, generate one level
        force = false;
    }
    // in-memory cum sum (of partial aggregates)
    if (TEMP.getOutputParameters().getNumRows() != 1) {
        int k = OptimizerUtils.getConstrainedNumThreads(_maxNumThreads);
        Unary unary1 = new Unary(TEMP, HopsOpOp1LopsU.get(_op), DataType.MATRIX, ValueType.DOUBLE, ExecType.CP, k);
        unary1.getOutputParameters().setDimensions(TEMP.getOutputParameters().getNumRows(), clen, brlen, bclen, -1);
        setLineNumbers(unary1);
        TEMP = unary1;
    }
    // split, group and mr cumsum
    while (level-- > 0) {
        double init = getCumulativeInitValue();
        CumulativeSplitAggregate split = new CumulativeSplitAggregate(TEMP, DataType.MATRIX, ValueType.DOUBLE, init);
        split.getOutputParameters().setDimensions(rlen, clen, brlen, bclen, -1);
        setLineNumbers(split);
        Group group1 = new Group(DATA.get(level), Group.OperationTypes.Sort, DataType.MATRIX, ValueType.DOUBLE);
        group1.getOutputParameters().setDimensions(rlen, clen, brlen, bclen, -1);
        setLineNumbers(group1);
        Group group2 = new Group(split, Group.OperationTypes.Sort, DataType.MATRIX, ValueType.DOUBLE);
        group2.getOutputParameters().setDimensions(rlen, clen, brlen, bclen, -1);
        setLineNumbers(group2);
        CumulativeOffsetBinary binary = new CumulativeOffsetBinary(group1, group2, DataType.MATRIX, ValueType.DOUBLE, aggtype, ExecType.MR);
        binary.getOutputParameters().setDimensions(rlen, clen, brlen, bclen, -1);
        setLineNumbers(binary);
        TEMP = binary;
    }
    return TEMP;
}

Example 3 with CumulativeOffsetBinary

use of org.apache.sysml.lops.CumulativeOffsetBinary in project systemml by apache.

the class UnaryOp method constructLopsMRCumulativeUnary.

/**
 * MR Cumsum is currently based on a multipass algorithm of (1) preaggregation and (2) subsequent offsetting.
 * Note that we currently support one robust physical operator but many alternative
 * realizations are possible for specific scenarios (e.g., when the preaggregated intermediate
 * fit into the map task memory budget) or by creating custom job types.
 *
 * @return low-level operator
 */
private Lop constructLopsMRCumulativeUnary() {
    Hop input = getInput().get(0);
    long rlen = input.getDim1();
    long clen = input.getDim2();
    long brlen = input.getRowsInBlock();
    long bclen = input.getColsInBlock();
    boolean force = !dimsKnown() || _etypeForced == ExecType.MR;
    OperationTypes aggtype = getCumulativeAggType();
    Lop X = input.constructLops();
    Lop TEMP = X;
    ArrayList<Lop> DATA = new ArrayList<>();
    int level = 0;
    // recursive preaggregation until aggregates fit into CP memory budget
    while (((2 * OptimizerUtils.estimateSize(TEMP.getOutputParameters().getNumRows(), clen) + OptimizerUtils.estimateSize(1, clen)) > OptimizerUtils.getLocalMemBudget() && TEMP.getOutputParameters().getNumRows() > 1) || force) {
        DATA.add(TEMP);
        // preaggregation per block
        long rlenAgg = (long) Math.ceil((double) TEMP.getOutputParameters().getNumRows() / brlen);
        Lop preagg = new CumulativePartialAggregate(TEMP, DataType.MATRIX, ValueType.DOUBLE, aggtype, ExecType.MR);
        preagg.getOutputParameters().setDimensions(rlenAgg, clen, brlen, bclen, -1);
        setLineNumbers(preagg);
        Group group = new Group(preagg, Group.OperationTypes.Sort, DataType.MATRIX, ValueType.DOUBLE);
        group.getOutputParameters().setDimensions(rlenAgg, clen, brlen, bclen, -1);
        setLineNumbers(group);
        Aggregate agg = new Aggregate(group, HopsAgg2Lops.get(AggOp.SUM), getDataType(), getValueType(), ExecType.MR);
        agg.getOutputParameters().setDimensions(rlenAgg, clen, brlen, bclen, -1);
        // aggregation uses kahanSum but the inputs do not have correction values
        agg.setupCorrectionLocation(CorrectionLocationType.NONE);
        setLineNumbers(agg);
        TEMP = agg;
        level++;
        // in case of unknowns, generate one level
        force = false;
    }
    // in-memory cum sum (of partial aggregates)
    if (TEMP.getOutputParameters().getNumRows() != 1) {
        int k = OptimizerUtils.getConstrainedNumThreads(_maxNumThreads);
        Unary unary1 = new Unary(TEMP, HopsOpOp1LopsU.get(_op), DataType.MATRIX, ValueType.DOUBLE, ExecType.CP, k);
        unary1.getOutputParameters().setDimensions(TEMP.getOutputParameters().getNumRows(), clen, brlen, bclen, -1);
        setLineNumbers(unary1);
        TEMP = unary1;
    }
    // split, group and mr cumsum
    while (level-- > 0) {
        double init = getCumulativeInitValue();
        CumulativeSplitAggregate split = new CumulativeSplitAggregate(TEMP, DataType.MATRIX, ValueType.DOUBLE, init);
        split.getOutputParameters().setDimensions(rlen, clen, brlen, bclen, -1);
        setLineNumbers(split);
        Group group1 = new Group(DATA.get(level), Group.OperationTypes.Sort, DataType.MATRIX, ValueType.DOUBLE);
        group1.getOutputParameters().setDimensions(rlen, clen, brlen, bclen, -1);
        setLineNumbers(group1);
        Group group2 = new Group(split, Group.OperationTypes.Sort, DataType.MATRIX, ValueType.DOUBLE);
        group2.getOutputParameters().setDimensions(rlen, clen, brlen, bclen, -1);
        setLineNumbers(group2);
        CumulativeOffsetBinary binary = new CumulativeOffsetBinary(group1, group2, DataType.MATRIX, ValueType.DOUBLE, aggtype, ExecType.MR);
        binary.getOutputParameters().setDimensions(rlen, clen, brlen, bclen, -1);
        setLineNumbers(binary);
        TEMP = binary;
    }
    return TEMP;
}

Example 4 with CumulativeOffsetBinary

use of org.apache.sysml.lops.CumulativeOffsetBinary in project systemml by apache.

the class UnaryOp method constructLopsSparkCumulativeUnary.

private Lop constructLopsSparkCumulativeUnary() {
    Hop input = getInput().get(0);
    long rlen = input.getDim1();
    long clen = input.getDim2();
    long brlen = input.getRowsInBlock();
    long bclen = input.getColsInBlock();
    boolean force = !dimsKnown() || _etypeForced == ExecType.SPARK;
    OperationTypes aggtype = getCumulativeAggType();
    Lop X = input.constructLops();
    Lop TEMP = X;
    ArrayList<Lop> DATA = new ArrayList<>();
    int level = 0;
    // recursive preaggregation until aggregates fit into CP memory budget
    while (((2 * OptimizerUtils.estimateSize(TEMP.getOutputParameters().getNumRows(), clen) + OptimizerUtils.estimateSize(1, clen)) > OptimizerUtils.getLocalMemBudget() && TEMP.getOutputParameters().getNumRows() > 1) || force) {
        DATA.add(TEMP);
        // preaggregation per block (for spark, the CumulativePartialAggregate subsumes both
        // the preaggregation and subsequent block aggregation)
        long rlenAgg = (long) Math.ceil((double) TEMP.getOutputParameters().getNumRows() / brlen);
        Lop preagg = new CumulativePartialAggregate(TEMP, DataType.MATRIX, ValueType.DOUBLE, aggtype, ExecType.SPARK);
        preagg.getOutputParameters().setDimensions(rlenAgg, clen, brlen, bclen, -1);
        setLineNumbers(preagg);
        TEMP = preagg;
        level++;
        // in case of unknowns, generate one level
        force = false;
    }
    // in-memory cum sum (of partial aggregates)
    if (TEMP.getOutputParameters().getNumRows() != 1) {
        int k = OptimizerUtils.getConstrainedNumThreads(_maxNumThreads);
        Unary unary1 = new Unary(TEMP, HopsOpOp1LopsU.get(_op), DataType.MATRIX, ValueType.DOUBLE, ExecType.CP, k);
        unary1.getOutputParameters().setDimensions(TEMP.getOutputParameters().getNumRows(), clen, brlen, bclen, -1);
        setLineNumbers(unary1);
        TEMP = unary1;
    }
    // split, group and mr cumsum
    while (level-- > 0) {
        // (for spark, the CumulativeOffsetBinary subsumes both the split aggregate and
        // the subsequent offset binary apply of split aggregates against the original data)
        double initValue = getCumulativeInitValue();
        CumulativeOffsetBinary binary = new CumulativeOffsetBinary(DATA.get(level), TEMP, DataType.MATRIX, ValueType.DOUBLE, initValue, aggtype, ExecType.SPARK);
        binary.getOutputParameters().setDimensions(rlen, clen, brlen, bclen, -1);
        setLineNumbers(binary);
        TEMP = binary;
    }
    return TEMP;
}