Examples with KahanObject org.apache.sysml.runtime.instructions.cp.KahanObject used on opensource projects

Example 76 with KahanObject

use of org.apache.sysml.runtime.instructions.cp.KahanObject in project incubator-systemml by apache.

the class ColGroupOLE method computeRowSums.

@Override
protected final void computeRowSums(MatrixBlock result, KahanFunction kplus, int rl, int ru) {
    // note: due to corrections the output might be a large dense block
    DenseBlock c = result.getDenseBlock();
    KahanObject kbuff = new KahanObject(0, 0);
    KahanPlus kplus2 = KahanPlus.getKahanPlusFnObject();
    final int blksz = BitmapEncoder.BITMAP_BLOCK_SZ;
    final int numVals = getNumValues();
    if (ALLOW_CACHE_CONSCIOUS_ROWSUMS && LOW_LEVEL_OPT && numVals > 1 && _numRows > blksz) {
        final int blksz2 = ColGroupOffset.WRITE_CACHE_BLKSZ / 2;
        // step 1: prepare position and value arrays
        int[] apos = skipScan(numVals, rl);
        double[] aval = sumAllValues(kplus, kbuff, false);
        // step 2: cache conscious row sums via horizontal scans
        for (int bi = rl; bi < ru; bi += blksz2) {
            int bimax = Math.min(bi + blksz2, ru);
            // horizontal segment scan, incl pos maintenance
            for (int k = 0; k < numVals; k++) {
                int boff = _ptr[k];
                int blen = len(k);
                double val = aval[k];
                int bix = apos[k];
                for (int ii = bi; ii < bimax && bix < blen; ii += blksz) {
                    // prepare length, start, and end pos
                    int len = _data[boff + bix];
                    int pos = boff + bix + 1;
                    // compute partial results
                    for (int i = 0; i < len; i++) {
                        int rix = ii + _data[pos + i];
                        double[] cvals = c.values(rix);
                        int cix = c.pos(rix);
                        kbuff.set(cvals[cix], cvals[cix + 1]);
                        kplus2.execute2(kbuff, val);
                        cvals[cix] = kbuff._sum;
                        cvals[cix + 1] = kbuff._correction;
                    }
                    bix += len + 1;
                }
                apos[k] = bix;
            }
        }
    } else {
        // iterate over all values and their bitmaps
        for (int k = 0; k < numVals; k++) {
            // prepare value-to-add for entire value bitmap
            int boff = _ptr[k];
            int blen = len(k);
            double val = sumValues(k, kplus, kbuff);
            // iterate over bitmap blocks and add values
            if (val != 0) {
                int slen;
                int bix = skipScanVal(k, rl);
                for (int off = ((rl + 1) / blksz) * blksz; bix < blen && off < ru; bix += slen + 1, off += blksz) {
                    slen = _data[boff + bix];
                    for (int i = 1; i <= slen; i++) {
                        int rix = off + _data[boff + bix + i];
                        double[] cvals = c.values(rix);
                        int cix = c.pos(rix);
                        kbuff.set(cvals[cix], cvals[cix + 1]);
                        kplus2.execute2(kbuff, val);
                        cvals[cix] = kbuff._sum;
                        cvals[cix + 1] = kbuff._correction;
                    }
                }
            }
        }
    }
}

Example 77 with KahanObject

use of org.apache.sysml.runtime.instructions.cp.KahanObject in project incubator-systemml by apache.

the class PerformGroupByAggInCombiner method call.

@Override
public WeightedCell call(WeightedCell value1, WeightedCell value2) throws Exception {
    WeightedCell outCell = new WeightedCell();
    CM_COV_Object cmObj = new CM_COV_Object();
    if (// everything except sum
    _op instanceof CMOperator) {
        if (((CMOperator) _op).isPartialAggregateOperator()) {
            cmObj.reset();
            // cmFn.get(key.getTag());
            CM lcmFn = CM.getCMFnObject(((CMOperator) _op).aggOpType);
            // partial aggregate cm operator
            lcmFn.execute(cmObj, value1.getValue(), value1.getWeight());
            lcmFn.execute(cmObj, value2.getValue(), value2.getWeight());
            outCell.setValue(cmObj.getRequiredPartialResult(_op));
            outCell.setWeight(cmObj.getWeight());
        } else // forward tuples to reducer
        {
            throw new DMLRuntimeException("Incorrect usage, should have used PerformGroupByAggInReducer");
        }
    } else if (// sum
    _op instanceof AggregateOperator) {
        AggregateOperator aggop = (AggregateOperator) _op;
        if (aggop.correctionExists) {
            KahanObject buffer = new KahanObject(aggop.initialValue, 0);
            KahanPlus.getKahanPlusFnObject();
            // partial aggregate with correction
            aggop.increOp.fn.execute(buffer, value1.getValue() * value1.getWeight());
            aggop.increOp.fn.execute(buffer, value2.getValue() * value2.getWeight());
            outCell.setValue(buffer._sum);
            outCell.setWeight(1);
        } else // no correction
        {
            double v = aggop.initialValue;
            // partial aggregate without correction
            v = aggop.increOp.fn.execute(v, value1.getValue() * value1.getWeight());
            v = aggop.increOp.fn.execute(v, value2.getValue() * value2.getWeight());
            outCell.setValue(v);
            outCell.setWeight(1);
        }
    } else
        throw new DMLRuntimeException("Unsupported operator in grouped aggregate instruction:" + _op);
    return outCell;
}

Example 78 with KahanObject

use of org.apache.sysml.runtime.instructions.cp.KahanObject in project incubator-systemml by apache.

the class PerformGroupByAggInReducer method call.

@Override
public WeightedCell call(Iterable<WeightedCell> kv) throws Exception {
    WeightedCell outCell = new WeightedCell();
    CM_COV_Object cmObj = new CM_COV_Object();
    if (// everything except sum
    op instanceof CMOperator) {
        cmObj.reset();
        // cmFn.get(key.getTag());
        CM lcmFn = CM.getCMFnObject(((CMOperator) op).aggOpType);
        if (((CMOperator) op).isPartialAggregateOperator()) {
            throw new DMLRuntimeException("Incorrect usage, should have used PerformGroupByAggInCombiner");
        } else // forward tuples to reducer
        {
            for (WeightedCell value : kv) lcmFn.execute(cmObj, value.getValue(), value.getWeight());
            outCell.setValue(cmObj.getRequiredResult(op));
            outCell.setWeight(1);
        }
    } else if (// sum
    op instanceof AggregateOperator) {
        AggregateOperator aggop = (AggregateOperator) op;
        if (aggop.correctionExists) {
            KahanObject buffer = new KahanObject(aggop.initialValue, 0);
            KahanPlus.getKahanPlusFnObject();
            // partial aggregate with correction
            for (WeightedCell value : kv) aggop.increOp.fn.execute(buffer, value.getValue() * value.getWeight());
            outCell.setValue(buffer._sum);
            outCell.setWeight(1);
        } else // no correction
        {
            double v = aggop.initialValue;
            // partial aggregate without correction
            for (WeightedCell value : kv) v = aggop.increOp.fn.execute(v, value.getValue() * value.getWeight());
            outCell.setValue(v);
            outCell.setWeight(1);
        }
    } else
        throw new DMLRuntimeException("Unsupported operator in grouped aggregate instruction:" + op);
    return outCell;
}

Example 79 with KahanObject

use of org.apache.sysml.runtime.instructions.cp.KahanObject in project incubator-systemml by apache.

the class LibMatrixAgg method aggregateBinaryMatrixDenseGeneric.

private static void aggregateBinaryMatrixDenseGeneric(MatrixBlock in, MatrixBlock aggVal, MatrixBlock aggCorr) {
    if (in.denseBlock == null || in.isEmptyBlock(false))
        return;
    final int m = in.rlen;
    final int n = in.clen;
    double[] a = in.getDenseBlockValues();
    KahanObject buffer = new KahanObject(0, 0);
    KahanPlus akplus = KahanPlus.getKahanPlusFnObject();
    // incl implicit nnz maintenance
    for (int i = 0, ix = 0; i < m; i++) for (int j = 0; j < n; j++, ix++) {
        buffer._sum = aggVal.quickGetValue(i, j);
        buffer._correction = aggCorr.quickGetValue(i, j);
        akplus.execute(buffer, a[ix]);
        aggVal.quickSetValue(i, j, buffer._sum);
        aggCorr.quickSetValue(i, j, buffer._correction);
    }
    // note: nnz of aggVal/aggCorr maintained internally
    if (aggVal.sparse)
        aggVal.examSparsity(false);
    if (aggCorr.sparse)
        aggCorr.examSparsity(false);
}

Example 80 with KahanObject

use of org.apache.sysml.runtime.instructions.cp.KahanObject in project incubator-systemml by apache.

the class LibMatrixAgg method aggregateBinaryMatrixLastRowDenseGeneric.

private static void aggregateBinaryMatrixLastRowDenseGeneric(MatrixBlock in, MatrixBlock aggVal) {
    if (in.denseBlock == null || in.isEmptyBlock(false))
        return;
    final int m = in.rlen;
    final int n = in.clen;
    final int cix = (m - 1) * n;
    double[] a = in.getDenseBlockValues();
    KahanObject buffer = new KahanObject(0, 0);
    KahanPlus akplus = KahanPlus.getKahanPlusFnObject();
    // incl implicit nnz maintenance
    for (int i = 0, ix = 0; i < m - 1; i++) for (int j = 0; j < n; j++, ix++) {
        buffer._sum = aggVal.quickGetValue(i, j);
        buffer._correction = aggVal.quickGetValue(m - 1, j);
        akplus.execute(buffer, a[ix], a[cix + j]);
        aggVal.quickSetValue(i, j, buffer._sum);
        aggVal.quickSetValue(m - 1, j, buffer._correction);
    }
    // note: nnz of aggVal maintained internally
    aggVal.examSparsity();
}