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

Example 16 with KahanObject

use of org.apache.sysml.runtime.instructions.cp.KahanObject 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) throws DMLRuntimeException {
    final int m = in.rlen;
    final int n = in.clen;
    double[] a = in.getDenseBlock();
    double[] c = out.getDenseBlock();
    switch(optype) {
        case //SUM/TRACE via k+, 
        KAHAN_SUM:
            {
                KahanObject kbuff = new KahanObject(0, 0);
                if (// SUM
                ixFn instanceof ReduceAll)
                    d_uakp(a, c, m, n, kbuff, (KahanPlus) vFn, rl, ru);
                else if (//ROWSUM
                ixFn instanceof ReduceCol)
                    d_uarkp(a, c, m, n, kbuff, (KahanPlus) vFn, rl, ru);
                else if (//COLSUM
                ixFn instanceof ReduceRow)
                    d_uackp(a, c, m, n, kbuff, (KahanPlus) vFn, rl, ru);
                else if (//TRACE
                ixFn instanceof ReduceDiag)
                    d_uakptrace(a, c, m, n, kbuff, (KahanPlus) vFn, rl, ru);
                break;
            }
        case //SUM_SQ via k+,
        KAHAN_SUM_SQ:
            {
                KahanObject kbuff = new KahanObject(0, 0);
                if (//SUM_SQ
                ixFn instanceof ReduceAll)
                    d_uasqkp(a, c, m, n, kbuff, (KahanPlusSq) vFn, rl, ru);
                else if (//ROWSUM_SQ
                ixFn instanceof ReduceCol)
                    d_uarsqkp(a, c, m, n, kbuff, (KahanPlusSq) vFn, rl, ru);
                else if (//COLSUM_SQ
                ixFn instanceof ReduceRow)
                    d_uacsqkp(a, c, m, n, kbuff, (KahanPlusSq) vFn, rl, ru);
                break;
            }
        case //CUMSUM
        CUM_KAHAN_SUM:
            {
                KahanObject kbuff = new KahanObject(0, 0);
                KahanPlus kplus = KahanPlus.getKahanPlusFnObject();
                d_ucumkp(a, null, c, m, n, kbuff, kplus, rl, ru);
                break;
            }
        case //CUMPROD
        CUM_PROD:
            {
                d_ucumm(a, null, c, m, n, rl, ru);
                break;
            }
        case CUM_MIN:
        case CUM_MAX:
            {
                double init = Double.MAX_VALUE * ((optype == AggType.CUM_MAX) ? -1 : 1);
                d_ucummxx(a, null, c, m, n, init, (Builtin) vFn, rl, ru);
                break;
            }
        case MIN:
        case //MAX/MIN
        MAX:
            {
                double init = Double.MAX_VALUE * ((optype == AggType.MAX) ? -1 : 1);
                if (// MIN/MAX
                ixFn instanceof ReduceAll)
                    d_uamxx(a, c, m, n, init, (Builtin) vFn, rl, ru);
                else if (//ROWMIN/ROWMAX
                ixFn instanceof ReduceCol)
                    d_uarmxx(a, c, m, n, init, (Builtin) vFn, rl, ru);
                else if (//COLMIN/COLMAX
                ixFn instanceof ReduceRow)
                    d_uacmxx(a, c, m, n, init, (Builtin) vFn, rl, ru);
                break;
            }
        case MAX_INDEX:
            {
                double init = -Double.MAX_VALUE;
                if (//ROWINDEXMAX
                ixFn instanceof ReduceCol)
                    d_uarimxx(a, c, m, n, init, (Builtin) vFn, rl, ru);
                break;
            }
        case MIN_INDEX:
            {
                double init = Double.MAX_VALUE;
                if (//ROWINDEXMIN
                ixFn instanceof ReduceCol)
                    d_uarimin(a, c, m, 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, m, n, kbuff, (Mean) vFn, rl, ru);
                else if (//ROWMEAN
                ixFn instanceof ReduceCol)
                    d_uarmean(a, c, m, n, kbuff, (Mean) vFn, rl, ru);
                else if (//COLMEAN
                ixFn instanceof ReduceRow)
                    d_uacmean(a, c, m, 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, m, n, cbuff, (CM) vFn, rl, ru);
                else if (//ROWVAR
                ixFn instanceof ReduceCol)
                    d_uarvar(a, c, m, n, cbuff, (CM) vFn, rl, ru);
                else if (//COLVAR
                ixFn instanceof ReduceRow)
                    d_uacvar(a, c, m, n, cbuff, (CM) vFn, rl, ru);
                break;
            }
        case //PROD
        PROD:
            {
                if (// PROD
                ixFn instanceof ReduceAll)
                    d_uam(a, c, m, n, rl, ru);
                break;
            }
        default:
            throw new DMLRuntimeException("Unsupported aggregation type: " + optype);
    }
}

Example 17 with KahanObject

use of org.apache.sysml.runtime.instructions.cp.KahanObject 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) throws DMLRuntimeException {
    final int m = in.rlen;
    final int n = in.clen;
    SparseBlock a = in.getSparseBlock();
    double[] c = out.getDenseBlock();
    switch(optype) {
        case //CUMSUM
        CUM_KAHAN_SUM:
            {
                KahanObject kbuff = new KahanObject(0, 0);
                KahanPlus kplus = KahanPlus.getKahanPlusFnObject();
                s_ucumkp(a, agg, c, m, n, kbuff, kplus, rl, ru);
                break;
            }
        case //CUMPROD
        CUM_PROD:
            {
                s_ucumm(a, agg, c, m, n, rl, ru);
                break;
            }
        case CUM_MIN:
        case CUM_MAX:
            {
                double init = Double.MAX_VALUE * ((optype == AggType.CUM_MAX) ? -1 : 1);
                s_ucummxx(a, agg, c, m, n, init, (Builtin) vFn, rl, ru);
                break;
            }
        default:
            throw new DMLRuntimeException("Unsupported cumulative aggregation type: " + optype);
    }
}

Example 18 with KahanObject

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

the class LibMatrixAgg method aggregateBinaryMatrixLastColSparseGeneric.

private static void aggregateBinaryMatrixLastColSparseGeneric(MatrixBlock in, MatrixBlock aggVal) throws DMLRuntimeException {
    //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 19 with KahanObject

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

the class LibMatrixAgg method aggregateUnaryMatrixSparse.

private static void aggregateUnaryMatrixSparse(MatrixBlock in, MatrixBlock out, AggType optype, ValueFunction vFn, IndexFunction ixFn, int rl, int ru) throws DMLRuntimeException {
    final int m = in.rlen;
    final int n = in.clen;
    SparseBlock a = in.getSparseBlock();
    double[] c = out.getDenseBlock();
    switch(optype) {
        case //SUM via k+
        KAHAN_SUM:
            {
                KahanObject kbuff = new KahanObject(0, 0);
                if (// SUM
                ixFn instanceof ReduceAll)
                    s_uakp(a, c, m, n, kbuff, (KahanPlus) vFn, rl, ru);
                else if (//ROWSUM
                ixFn instanceof ReduceCol)
                    s_uarkp(a, c, m, n, kbuff, (KahanPlus) vFn, rl, ru);
                else if (//COLSUM
                ixFn instanceof ReduceRow)
                    s_uackp(a, c, m, n, kbuff, (KahanPlus) vFn, rl, ru);
                else if (//TRACE
                ixFn instanceof ReduceDiag)
                    s_uakptrace(a, c, m, n, kbuff, (KahanPlus) vFn, rl, ru);
                break;
            }
        case //SUM_SQ via k+
        KAHAN_SUM_SQ:
            {
                KahanObject kbuff = new KahanObject(0, 0);
                if (//SUM_SQ
                ixFn instanceof ReduceAll)
                    s_uasqkp(a, c, m, n, kbuff, (KahanPlusSq) vFn, rl, ru);
                else if (//ROWSUM_SQ
                ixFn instanceof ReduceCol)
                    s_uarsqkp(a, c, m, n, kbuff, (KahanPlusSq) vFn, rl, ru);
                else if (//COLSUM_SQ
                ixFn instanceof ReduceRow)
                    s_uacsqkp(a, c, m, n, kbuff, (KahanPlusSq) vFn, rl, ru);
                break;
            }
        case //CUMSUM
        CUM_KAHAN_SUM:
            {
                KahanObject kbuff = new KahanObject(0, 0);
                KahanPlus kplus = KahanPlus.getKahanPlusFnObject();
                s_ucumkp(a, null, c, m, n, kbuff, kplus, rl, ru);
                break;
            }
        case //CUMPROD
        CUM_PROD:
            {
                s_ucumm(a, null, c, m, n, rl, ru);
                break;
            }
        case CUM_MIN:
        case CUM_MAX:
            {
                double init = Double.MAX_VALUE * ((optype == AggType.CUM_MAX) ? -1 : 1);
                s_ucummxx(a, null, c, m, n, init, (Builtin) vFn, rl, ru);
                break;
            }
        case MIN:
        case //MAX/MIN
        MAX:
            {
                double init = Double.MAX_VALUE * ((optype == AggType.MAX) ? -1 : 1);
                if (// MIN/MAX
                ixFn instanceof ReduceAll)
                    s_uamxx(a, c, m, n, init, (Builtin) vFn, rl, ru);
                else if (//ROWMIN/ROWMAX
                ixFn instanceof ReduceCol)
                    s_uarmxx(a, c, m, n, init, (Builtin) vFn, rl, ru);
                else if (//COLMIN/COLMAX
                ixFn instanceof ReduceRow)
                    s_uacmxx(a, c, m, n, init, (Builtin) vFn, rl, ru);
                break;
            }
        case MAX_INDEX:
            {
                double init = -Double.MAX_VALUE;
                if (//ROWINDEXMAX
                ixFn instanceof ReduceCol)
                    s_uarimxx(a, c, m, n, init, (Builtin) vFn, rl, ru);
                break;
            }
        case MIN_INDEX:
            {
                double init = Double.MAX_VALUE;
                if (//ROWINDEXMAX
                ixFn instanceof ReduceCol)
                    s_uarimin(a, c, m, n, init, (Builtin) vFn, rl, ru);
                break;
            }
        case MEAN:
            {
                KahanObject kbuff = new KahanObject(0, 0);
                if (// MEAN
                ixFn instanceof ReduceAll)
                    s_uamean(a, c, m, n, kbuff, (Mean) vFn, rl, ru);
                else if (//ROWMEAN
                ixFn instanceof ReduceCol)
                    s_uarmean(a, c, m, n, kbuff, (Mean) vFn, rl, ru);
                else if (//COLMEAN
                ixFn instanceof ReduceRow)
                    s_uacmean(a, c, m, n, kbuff, (Mean) vFn, rl, ru);
                break;
            }
        case //VAR
        VAR:
            {
                CM_COV_Object cbuff = new CM_COV_Object();
                if (//VAR
                ixFn instanceof ReduceAll)
                    s_uavar(a, c, m, n, cbuff, (CM) vFn, rl, ru);
                else if (//ROWVAR
                ixFn instanceof ReduceCol)
                    s_uarvar(a, c, m, n, cbuff, (CM) vFn, rl, ru);
                else if (//COLVAR
                ixFn instanceof ReduceRow)
                    s_uacvar(a, c, m, n, cbuff, (CM) vFn, rl, ru);
                break;
            }
        case //PROD
        PROD:
            {
                if (// PROD
                ixFn instanceof ReduceAll)
                    s_uam(a, c, m, n, rl, ru);
                break;
            }
        default:
            throw new DMLRuntimeException("Unsupported aggregation type: " + optype);
    }
}

Example 20 with KahanObject

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

the class LibMatrixAgg method aggregateBinaryMatrixAllDense.

private static void aggregateBinaryMatrixAllDense(MatrixBlock in, MatrixBlock aggVal, MatrixBlock aggCorr) throws DMLRuntimeException {
    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.getDenseBlock();
    double[] c = aggVal.getDenseBlock();
    double[] cc = aggCorr.getDenseBlock();
    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;
}