@Override
protected double computeIntermediateMemEstimate(long dim1, long dim2, long nnz) {
// default: no additional memory required
double val = 0;
double sparsity = OptimizerUtils.getSparsity(dim1, dim2, nnz);
switch (_op) // see MatrixAggLib for runtime operations
{
case MAX:
case MIN:
// worst-case: column-wise, sparse (temp int count arrays)
if (_direction == Direction.Col) val = dim2 * OptimizerUtils.INT_SIZE;
break;
case SUM:
case SUM_SQ:
// worst-case correction LASTROW / LASTCOLUMN
if (_direction == Direction.Col) // (potentially sparse)
val = OptimizerUtils.estimateSizeExactSparsity(1, dim2, sparsity);
else if (_direction == Direction.Row) // (always dense)
val = OptimizerUtils.estimateSizeExactSparsity(dim1, 1, 1.0);
break;
case MEAN:
// worst-case correction LASTTWOROWS / LASTTWOCOLUMNS
if (_direction == Direction.Col) // (potentially sparse)
val = OptimizerUtils.estimateSizeExactSparsity(2, dim2, sparsity);
else if (_direction == Direction.Row) // (always dense)
val = OptimizerUtils.estimateSizeExactSparsity(dim1, 2, 1.0);
break;
case VAR:
// worst-case correction LASTFOURROWS / LASTFOURCOLUMNS
if (_direction == Direction.Col) // (potentially sparse)
val = OptimizerUtils.estimateSizeExactSparsity(4, dim2, sparsity);
else if (_direction == Direction.Row) // (always dense)
val = OptimizerUtils.estimateSizeExactSparsity(dim1, 4, 1.0);
break;
case MAXINDEX:
case MININDEX:
Hop hop = getInput().get(0);
if (isUnaryAggregateOuterCPRewriteApplicable())
val = 3 * OptimizerUtils.estimateSizeExactSparsity(1, hop._dim2, 1.0);
else
// worst-case correction LASTCOLUMN
val = OptimizerUtils.estimateSizeExactSparsity(dim1, 1, 1.0);
break;
default:
// no intermediate memory consumption
val = 0;
}
return val;
}
@Override
protected double computeOutputMemEstimate(long dim1, long dim2, long nnz) {
double sparsity = OptimizerUtils.getSparsity(dim1, dim2, nnz);
return OptimizerUtils.estimateSizeExactSparsity(dim1, dim2, sparsity);
}
