Examples with IJV org.apache.sysml.runtime.matrix.data.IJV used on opensource projects

Example 46 with IJV

use of org.apache.sysml.runtime.matrix.data.IJV in project incubator-systemml by apache.

the class SpoofCellwise method executeCompressedRowAggSum.

private long executeCompressedRowAggSum(CompressedMatrixBlock a, SideInput[] b, double[] scalars, double[] c, int m, int n, boolean sparseSafe, int rl, int ru) {
    KahanFunction kplus = (KahanFunction) getAggFunction();
    KahanObject kbuff = new KahanObject(0, 0);
    long lnnz = 0;
    Iterator<IJV> iter = a.getIterator(rl, ru, !sparseSafe);
    while (iter.hasNext()) {
        IJV cell = iter.next();
        double val = genexec(cell.getV(), b, scalars, m, n, cell.getI(), cell.getJ());
        kbuff.set(c[cell.getI()], 0);
        kplus.execute2(kbuff, val);
        c[cell.getI()] = kbuff._sum;
    }
    for (int i = rl; i < ru; i++) lnnz += (c[i] != 0) ? 1 : 0;
    return lnnz;
}

Example 47 with IJV

use of org.apache.sysml.runtime.matrix.data.IJV in project incubator-systemml by apache.

the class SpoofCellwise method executeCompressedAggMxx.

private double executeCompressedAggMxx(CompressedMatrixBlock a, SideInput[] b, double[] scalars, int m, int n, boolean sparseSafe, int rl, int ru) {
    // safe aggregation for min/max w/ handling of zero entries
    // note: sparse safe with zero value as min/max handled outside
    double ret = (_aggOp == AggOp.MIN) ? Double.POSITIVE_INFINITY : Double.NEGATIVE_INFINITY;
    ValueFunction vfun = getAggFunction();
    Iterator<IJV> iter = a.getIterator(rl, ru, !sparseSafe);
    while (iter.hasNext()) {
        IJV cell = iter.next();
        double val = genexec(cell.getV(), b, scalars, m, n, cell.getI(), cell.getJ());
        ret = vfun.execute(ret, val);
    }
    return ret;
}

Example 48 with IJV

use of org.apache.sysml.runtime.matrix.data.IJV in project incubator-systemml by apache.

the class SpoofMultiAggregate method executeCompressed.

private void executeCompressed(CompressedMatrixBlock a, SideInput[] b, double[] scalars, double[] c, int m, int n, int rl, int ru) {
    // core compressed aggregation operation
    Iterator<IJV> iter = a.getIterator(rl, ru, true);
    while (iter.hasNext()) {
        IJV cell = iter.next();
        genexec(cell.getV(), b, scalars, c, m, n, cell.getI(), cell.getJ());
    }
}

Example 49 with IJV

use of org.apache.sysml.runtime.matrix.data.IJV in project incubator-systemml by apache.

the class SpoofOuterProduct method executeCellwiseCompressed.

private void executeCellwiseCompressed(CompressedMatrixBlock a, DenseBlock u, DenseBlock v, SideInput[] b, double[] scalars, MatrixBlock out, int m, int n, int k, OutProdType type, int rl, int ru, int cl, int cu) {
    // NOTE: we don't create sparse side inputs w/ row-major cursors because
    // compressed data is access in a column-major order
    double[] c = (out.getDenseBlock() != null) ? out.getDenseBlockValues() : null;
    SparseBlock csblock = out.getSparseBlock();
    Iterator<IJV> iter = a.getIterator(rl, ru, false);
    while (iter.hasNext()) {
        IJV cell = iter.next();
        double[] uvals = u.values(cell.getI());
        double[] vvals = v.values(cell.getJ());
        int uix = u.pos(cell.getI());
        int vix = v.pos(cell.getJ());
        if (type == OutProdType.CELLWISE_OUTER_PRODUCT) {
            if (out.isInSparseFormat()) {
                csblock.allocate(cell.getI());
                csblock.append(cell.getI(), cell.getJ(), genexecCellwise(cell.getV(), uvals, uix, vvals, vix, b, scalars, m, n, k, cell.getI(), cell.getJ()));
            } else {
                c[cell.getI() * n + cell.getJ()] = genexecCellwise(cell.getV(), uvals, uix, vvals, vix, b, scalars, m, n, k, cell.getI(), cell.getJ());
            }
        } else {
            c[0] += genexecCellwise(cell.getV(), uvals, uix, vvals, vix, b, scalars, m, n, k, cell.getI(), cell.getJ());
        }
    }
}

Example 50 with IJV

use of org.apache.sysml.runtime.matrix.data.IJV in project incubator-systemml by apache.

the class SpoofOuterProduct method executeCompressed.

private void executeCompressed(CompressedMatrixBlock a, DenseBlock u, DenseBlock v, SideInput[] b, double[] scalars, DenseBlock c, int m, int n, int k, OutProdType type, int rl, int ru, int cl, int cu) {
    // NOTE: we don't create sparse side inputs w/ row-major cursors because
    // compressed data is access in a column-major order
    boolean left = (_outerProductType == OutProdType.LEFT_OUTER_PRODUCT);
    Iterator<IJV> iter = !left ? a.getIterator(rl, ru, false) : // cl/cu -> colgroups
    a.getIterator(rl, ru, cl, cu, false);
    while (iter.hasNext()) {
        IJV cell = iter.next();
        double[] uvals = u.values(cell.getI());
        double[] vvals = v.values(cell.getJ());
        double[] cvals = c.values(left ? cell.getJ() : cell.getI());
        int uix = u.pos(cell.getI());
        int vix = v.pos(cell.getJ());
        genexecDense(cell.getV(), uvals, uix, vvals, vix, b, scalars, cvals, left ? vix : uix, m, n, k, cell.getI(), cell.getJ());
    }
}