Esempio n. 1
0
  /**
   * @return
   * @throws HopsException
   * @throws LopsException
   */
  private Lop constructLopsSparkCumulativeUnary() throws HopsException, LopsException {
    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<Lop>();
    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++;
      force = false; // in case of unknowns, generate one level
    }

    // in-memory cum sum (of partial aggregates)
    if (TEMP.getOutputParameters().getNumRows() != 1) {
      Unary unary1 =
          new Unary(TEMP, HopsOpOp1LopsU.get(_op), DataType.MATRIX, ValueType.DOUBLE, ExecType.CP);
      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;
  }
Esempio n. 2
0
 public boolean hasNonBlockedInputs() {
   for (Lop in : getInputs())
     if (in.getDataType() == DataType.MATRIX && !in.getOutputParameters().isBlocked()) return true;
   return false;
 }
Esempio n. 3
0
  /**
   * 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
   * @throws HopsException
   * @throws LopsException
   */
  private Lop constructLopsMRCumulativeUnary() throws HopsException, LopsException {
    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<Lop>();
    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);
      agg.setupCorrectionLocation(
          CorrectionLocationType
              .NONE); // aggregation uses kahanSum but the inputs do not have correction values
      setLineNumbers(agg);
      TEMP = agg;
      level++;
      force = false; // in case of unknowns, generate one level
    }

    // in-memory cum sum (of partial aggregates)
    if (TEMP.getOutputParameters().getNumRows() != 1) {
      Unary unary1 =
          new Unary(TEMP, HopsOpOp1LopsU.get(_op), DataType.MATRIX, ValueType.DOUBLE, ExecType.CP);
      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;
  }