/**
* 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) {
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;
}
/**
* @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) {
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;
}
private Lop constructLopsIQM() throws HopsException, LopsException {
ExecType et = optFindExecType();
Hop input = getInput().get(0);
if (et == ExecType.MR) {
CombineUnary combine =
CombineUnary.constructCombineLop(input.constructLops(), DataType.MATRIX, getValueType());
combine
.getOutputParameters()
.setDimensions(
input.getDim1(),
input.getDim2(),
input.getRowsInBlock(),
input.getColsInBlock(),
input.getNnz());
SortKeys sort =
SortKeys.constructSortByValueLop(
combine,
SortKeys.OperationTypes.WithoutWeights,
DataType.MATRIX,
ValueType.DOUBLE,
ExecType.MR);
// Sort dimensions are same as the first input
sort.getOutputParameters()
.setDimensions(
input.getDim1(),
input.getDim2(),
input.getRowsInBlock(),
input.getColsInBlock(),
input.getNnz());
Data lit = Data.createLiteralLop(ValueType.DOUBLE, Double.toString(0.25));
lit.setAllPositions(
this.getBeginLine(), this.getBeginColumn(), this.getEndLine(), this.getEndColumn());
PickByCount pick =
new PickByCount(
sort, lit, DataType.MATRIX, getValueType(), PickByCount.OperationTypes.RANGEPICK);
pick.getOutputParameters().setDimensions(-1, -1, getRowsInBlock(), getColsInBlock(), -1);
setLineNumbers(pick);
PartialAggregate pagg =
new PartialAggregate(
pick,
HopsAgg2Lops.get(Hop.AggOp.SUM),
HopsDirection2Lops.get(Hop.Direction.RowCol),
DataType.MATRIX,
getValueType());
setLineNumbers(pagg);
// Set the dimensions of PartialAggregate LOP based on the
// direction in which aggregation is performed
pagg.setDimensionsBasedOnDirection(getDim1(), getDim2(), getRowsInBlock(), getColsInBlock());
Group group1 = new Group(pagg, Group.OperationTypes.Sort, DataType.MATRIX, getValueType());
group1
.getOutputParameters()
.setDimensions(getDim1(), getDim2(), getRowsInBlock(), getColsInBlock(), getNnz());
setLineNumbers(group1);
Aggregate agg1 =
new Aggregate(
group1,
HopsAgg2Lops.get(Hop.AggOp.SUM),
DataType.MATRIX,
getValueType(),
ExecType.MR);
agg1.getOutputParameters()
.setDimensions(getDim1(), getDim2(), getRowsInBlock(), getColsInBlock(), getNnz());
agg1.setupCorrectionLocation(pagg.getCorrectionLocation());
setLineNumbers(agg1);
UnaryCP unary1 =
new UnaryCP(
agg1, HopsOpOp1LopsUS.get(OpOp1.CAST_AS_SCALAR), getDataType(), getValueType());
unary1.getOutputParameters().setDimensions(0, 0, 0, 0, -1);
setLineNumbers(unary1);
Unary iqm =
new Unary(
sort,
unary1,
Unary.OperationTypes.MR_IQM,
DataType.SCALAR,
ValueType.DOUBLE,
ExecType.CP);
iqm.getOutputParameters().setDimensions(0, 0, 0, 0, -1);
setLineNumbers(iqm);
return iqm;
} else {
SortKeys sort =
SortKeys.constructSortByValueLop(
input.constructLops(),
SortKeys.OperationTypes.WithoutWeights,
DataType.MATRIX,
ValueType.DOUBLE,
et);
sort.getOutputParameters()
.setDimensions(
input.getDim1(),
input.getDim2(),
input.getRowsInBlock(),
input.getColsInBlock(),
input.getNnz());
PickByCount pick =
new PickByCount(
sort, null, getDataType(), getValueType(), PickByCount.OperationTypes.IQM, et, true);
pick.getOutputParameters()
.setDimensions(getDim1(), getDim2(), getRowsInBlock(), getColsInBlock(), getNnz());
setLineNumbers(pick);
return pick;
}
}
