Examples with KahanPlus org.apache.sysml.runtime.functionobjects.KahanPlus used on opensource projects

Example 11 with KahanPlus

use of org.apache.sysml.runtime.functionobjects.KahanPlus in project incubator-systemml by apache.

the class LibMatrixAgg method aggregateUnaryMatrixDense.

private static void aggregateUnaryMatrixDense(MatrixBlock in, MatrixBlock out, AggType optype, ValueFunction vFn, IndexFunction ixFn, int rl, int ru) {
    final int n = in.clen;
    // note: due to corrections, even the output might be a large dense block
    DenseBlock a = in.getDenseBlock();
    DenseBlock c = out.getDenseBlock();
    switch(optype) {
        case KAHAN_SUM:
            {
                // SUM/TRACE via k+,
                KahanObject kbuff = new KahanObject(0, 0);
                if (// SUM
                ixFn instanceof ReduceAll)
                    d_uakp(a, c, n, kbuff, (KahanPlus) vFn, rl, ru);
                else if (// ROWSUM
                ixFn instanceof ReduceCol)
                    d_uarkp(a, c, n, kbuff, (KahanPlus) vFn, rl, ru);
                else if (// COLSUM
                ixFn instanceof ReduceRow)
                    d_uackp(a, c, n, kbuff, (KahanPlus) vFn, rl, ru);
                else if (// TRACE
                ixFn instanceof ReduceDiag)
                    d_uakptrace(a, c, n, kbuff, (KahanPlus) vFn, rl, ru);
                break;
            }
        case KAHAN_SUM_SQ:
            {
                // SUM_SQ via k+,
                KahanObject kbuff = new KahanObject(0, 0);
                if (// SUM_SQ
                ixFn instanceof ReduceAll)
                    d_uasqkp(a, c, n, kbuff, (KahanPlusSq) vFn, rl, ru);
                else if (// ROWSUM_SQ
                ixFn instanceof ReduceCol)
                    d_uarsqkp(a, c, n, kbuff, (KahanPlusSq) vFn, rl, ru);
                else if (// COLSUM_SQ
                ixFn instanceof ReduceRow)
                    d_uacsqkp(a, c, n, kbuff, (KahanPlusSq) vFn, rl, ru);
                break;
            }
        case CUM_KAHAN_SUM:
            {
                // CUMSUM
                KahanObject kbuff = new KahanObject(0, 0);
                KahanPlus kplus = KahanPlus.getKahanPlusFnObject();
                d_ucumkp(in.getDenseBlock(), null, out.getDenseBlock(), n, kbuff, kplus, rl, ru);
                break;
            }
        case CUM_PROD:
            {
                // CUMPROD
                d_ucumm(in.getDenseBlockValues(), null, out.getDenseBlockValues(), n, rl, ru);
                break;
            }
        case CUM_MIN:
        case CUM_MAX:
            {
                double init = (optype == AggType.CUM_MAX) ? Double.NEGATIVE_INFINITY : Double.POSITIVE_INFINITY;
                d_ucummxx(in.getDenseBlockValues(), null, out.getDenseBlockValues(), n, init, (Builtin) vFn, rl, ru);
                break;
            }
        case MIN:
        case MAX:
            {
                // MAX/MIN
                double init = (optype == AggType.MAX) ? Double.NEGATIVE_INFINITY : Double.POSITIVE_INFINITY;
                if (// MIN/MAX
                ixFn instanceof ReduceAll)
                    d_uamxx(a, c, n, init, (Builtin) vFn, rl, ru);
                else if (// ROWMIN/ROWMAX
                ixFn instanceof ReduceCol)
                    d_uarmxx(a, c, n, init, (Builtin) vFn, rl, ru);
                else if (// COLMIN/COLMAX
                ixFn instanceof ReduceRow)
                    d_uacmxx(a, c, n, init, (Builtin) vFn, rl, ru);
                break;
            }
        case MAX_INDEX:
            {
                double init = Double.NEGATIVE_INFINITY;
                if (// ROWINDEXMAX
                ixFn instanceof ReduceCol)
                    d_uarimxx(a, c, n, init, (Builtin) vFn, rl, ru);
                break;
            }
        case MIN_INDEX:
            {
                double init = Double.POSITIVE_INFINITY;
                if (// ROWINDEXMIN
                ixFn instanceof ReduceCol)
                    d_uarimin(a, c, n, init, (Builtin) vFn, rl, ru);
                break;
            }
        case MEAN:
            {
                // MEAN
                KahanObject kbuff = new KahanObject(0, 0);
                if (// MEAN
                ixFn instanceof ReduceAll)
                    d_uamean(a, c, n, kbuff, (Mean) vFn, rl, ru);
                else if (// ROWMEAN
                ixFn instanceof ReduceCol)
                    d_uarmean(a, c, n, kbuff, (Mean) vFn, rl, ru);
                else if (// COLMEAN
                ixFn instanceof ReduceRow)
                    d_uacmean(a, c, n, kbuff, (Mean) vFn, rl, ru);
                break;
            }
        case VAR:
            {
                // VAR
                CM_COV_Object cbuff = new CM_COV_Object();
                if (// VAR
                ixFn instanceof ReduceAll)
                    d_uavar(a, c, n, cbuff, (CM) vFn, rl, ru);
                else if (// ROWVAR
                ixFn instanceof ReduceCol)
                    d_uarvar(a, c, n, cbuff, (CM) vFn, rl, ru);
                else if (// COLVAR
                ixFn instanceof ReduceRow)
                    d_uacvar(a, c, n, cbuff, (CM) vFn, rl, ru);
                break;
            }
        case PROD:
            {
                // PROD
                if (// PROD
                ixFn instanceof ReduceAll)
                    d_uam(a, c, n, rl, ru);
                break;
            }
        default:
            throw new DMLRuntimeException("Unsupported aggregation type: " + optype);
    }
}

Example 12 with KahanPlus

use of org.apache.sysml.runtime.functionobjects.KahanPlus in project incubator-systemml by apache.

the class LibMatrixAgg method aggregateBinaryMatrixLastColSparseGeneric.

private static void aggregateBinaryMatrixLastColSparseGeneric(MatrixBlock in, MatrixBlock aggVal) {
    // sparse-safe operation
    if (in.isEmptyBlock(false))
        return;
    SparseBlock a = in.getSparseBlock();
    KahanObject buffer1 = new KahanObject(0, 0);
    KahanPlus akplus = KahanPlus.getKahanPlusFnObject();
    final int m = in.rlen;
    final int n = in.clen;
    final int rlen = Math.min(a.numRows(), m);
    for (int i = 0; i < rlen; i++) {
        if (!a.isEmpty(i)) {
            int apos = a.pos(i);
            int alen = a.size(i);
            int[] aix = a.indexes(i);
            double[] avals = a.values(i);
            for (int j = apos; j < apos + alen && aix[j] < n - 1; j++) {
                int jix = aix[j];
                double corr = in.quickGetValue(i, n - 1);
                buffer1._sum = aggVal.quickGetValue(i, jix);
                buffer1._correction = aggVal.quickGetValue(i, n - 1);
                akplus.execute(buffer1, avals[j], corr);
                aggVal.quickSetValue(i, jix, buffer1._sum);
                aggVal.quickSetValue(i, n - 1, buffer1._correction);
            }
        }
    }
    // note: nnz of aggVal/aggCorr maintained internally
    aggVal.examSparsity();
}

Example 13 with KahanPlus

use of org.apache.sysml.runtime.functionobjects.KahanPlus in project incubator-systemml by apache.

the class LibMatrixAgg method cumaggregateUnaryMatrixSparse.

private static void cumaggregateUnaryMatrixSparse(MatrixBlock in, MatrixBlock out, AggType optype, ValueFunction vFn, double[] agg, int rl, int ru) {
    final int m = in.rlen;
    final int n = in.clen;
    SparseBlock a = in.getSparseBlock();
    DenseBlock dc = out.getDenseBlock();
    double[] c = out.getDenseBlockValues();
    switch(optype) {
        case CUM_KAHAN_SUM:
            {
                // CUMSUM
                KahanObject kbuff = new KahanObject(0, 0);
                KahanPlus kplus = KahanPlus.getKahanPlusFnObject();
                s_ucumkp(a, agg, dc, m, n, kbuff, kplus, rl, ru);
                break;
            }
        case CUM_PROD:
            {
                // CUMPROD
                s_ucumm(a, agg, c, n, rl, ru);
                break;
            }
        case CUM_MIN:
        case CUM_MAX:
            {
                double init = (optype == AggType.CUM_MAX) ? Double.NEGATIVE_INFINITY : Double.POSITIVE_INFINITY;
                s_ucummxx(a, agg, c, n, init, (Builtin) vFn, rl, ru);
                break;
            }
        default:
            throw new DMLRuntimeException("Unsupported cumulative aggregation type: " + optype);
    }
}

Example 14 with KahanPlus

use of org.apache.sysml.runtime.functionobjects.KahanPlus in project incubator-systemml by apache.

the class LibMatrixAgg method aggregateBinaryMatrixAllDense.

private static void aggregateBinaryMatrixAllDense(MatrixBlock in, MatrixBlock aggVal, MatrixBlock aggCorr) {
    if (in.denseBlock == null || in.isEmptyBlock(false))
        return;
    // allocate output arrays (if required)
    // should always stay in dense
    aggVal.allocateDenseBlock();
    // should always stay in dense
    aggCorr.allocateDenseBlock();
    double[] a = in.getDenseBlockValues();
    double[] c = aggVal.getDenseBlockValues();
    double[] cc = aggCorr.getDenseBlockValues();
    KahanObject buffer1 = new KahanObject(0, 0);
    KahanPlus akplus = KahanPlus.getKahanPlusFnObject();
    final int len = Math.min(a.length, in.rlen * in.clen);
    int nnzC = 0;
    int nnzCC = 0;
    for (int i = 0; i < len; i++) {
        buffer1._sum = c[i];
        buffer1._correction = cc[i];
        akplus.execute2(buffer1, a[i]);
        c[i] = buffer1._sum;
        cc[i] = buffer1._correction;
        nnzC += (buffer1._sum != 0) ? 1 : 0;
        nnzCC += (buffer1._correction != 0) ? 1 : 0;
    }
    aggVal.nonZeros = nnzC;
    aggCorr.nonZeros = nnzCC;
}

Example 15 with KahanPlus

use of org.apache.sysml.runtime.functionobjects.KahanPlus in project incubator-systemml by apache.

the class LibMatrixAgg method aggregateBinaryMatrixSparseGeneric.

private static void aggregateBinaryMatrixSparseGeneric(MatrixBlock in, MatrixBlock aggVal, MatrixBlock aggCorr) {
    if (in.isEmptyBlock(false))
        return;
    SparseBlock a = in.getSparseBlock();
    KahanObject buffer1 = new KahanObject(0, 0);
    KahanPlus akplus = KahanPlus.getKahanPlusFnObject();
    final int m = in.rlen;
    final int rlen = Math.min(a.numRows(), m);
    for (int i = 0; i < rlen; i++) {
        if (!a.isEmpty(i)) {
            int apos = a.pos(i);
            int alen = a.size(i);
            int[] aix = a.indexes(i);
            double[] avals = a.values(i);
            for (int j = apos; j < apos + alen; j++) {
                int jix = aix[j];
                buffer1._sum = aggVal.quickGetValue(i, jix);
                buffer1._correction = aggCorr.quickGetValue(i, jix);
                akplus.execute2(buffer1, avals[j]);
                aggVal.quickSetValue(i, jix, buffer1._sum);
                aggCorr.quickSetValue(i, jix, buffer1._correction);
            }
        }
    }
    // note: nnz of aggVal/aggCorr maintained internally
    if (aggVal.sparse)
        aggVal.examSparsity(false);
    if (aggCorr.sparse)
        aggCorr.examSparsity(false);
}