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

Example 6 with DenseBlock

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

the class SpoofOuterProduct method execute.

@Override
public ScalarObject execute(ArrayList<MatrixBlock> inputs, ArrayList<ScalarObject> scalarObjects, int numThreads) {
    // sanity check
    if (inputs == null || inputs.size() < 3)
        throw new RuntimeException("Invalid input arguments.");
    if (inputs.get(0).isEmptyBlock(false))
        return new DoubleObject(0);
    if (2 * inputs.get(0).getNonZeros() * inputs.get(1).getNumColumns() < PAR_MINFLOP_THRESHOLD)
        // sequential
        return execute(inputs, scalarObjects);
    // input preparation
    DenseBlock[] ab = getDenseMatrices(prepInputMatrices(inputs, 1, 2, true, false));
    SideInput[] b = prepInputMatrices(inputs, 3, false);
    double[] scalars = prepInputScalars(scalarObjects);
    // core sequential execute
    final int m = inputs.get(0).getNumRows();
    final int n = inputs.get(0).getNumColumns();
    // rank
    final int k = inputs.get(1).getNumColumns();
    final long nnz = inputs.get(0).getNonZeros();
    double sum = 0;
    try {
        ExecutorService pool = CommonThreadPool.get(k);
        ArrayList<ParOuterProdAggTask> tasks = new ArrayList<>();
        int numThreads2 = getPreferredNumberOfTasks(m, n, nnz, k, numThreads);
        int blklen = (int) (Math.ceil((double) m / numThreads2));
        for (int i = 0; i < numThreads2 & i * blklen < m; i++) tasks.add(new ParOuterProdAggTask(inputs.get(0), ab[0], ab[1], b, scalars, m, n, k, _outerProductType, i * blklen, Math.min((i + 1) * blklen, m), 0, n));
        // execute tasks
        List<Future<Double>> taskret = pool.invokeAll(tasks);
        pool.shutdown();
        for (Future<Double> task : taskret) sum += task.get();
    } catch (Exception e) {
        throw new DMLRuntimeException(e);
    }
    return new DoubleObject(sum);
}

Example 7 with DenseBlock

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

the class SpoofOuterProduct method execute.

@Override
public ScalarObject execute(ArrayList<MatrixBlock> inputs, ArrayList<ScalarObject> scalarObjects) {
    // sanity check
    if (inputs == null || inputs.size() < 3)
        throw new RuntimeException("Invalid input arguments.");
    if (inputs.get(0).isEmptyBlock(false))
        return new DoubleObject(0);
    // input preparation
    DenseBlock[] ab = getDenseMatrices(prepInputMatrices(inputs, 1, 2, true, false));
    SideInput[] b = prepInputMatrices(inputs, 3, false);
    double[] scalars = prepInputScalars(scalarObjects);
    // core sequential execute
    final int m = inputs.get(0).getNumRows();
    final int n = inputs.get(0).getNumColumns();
    // rank
    final int k = inputs.get(1).getNumColumns();
    MatrixBlock a = inputs.get(0);
    MatrixBlock out = new MatrixBlock(1, 1, false);
    out.allocateDenseBlock();
    if (a instanceof CompressedMatrixBlock)
        executeCellwiseCompressed((CompressedMatrixBlock) a, ab[0], ab[1], b, scalars, out, m, n, k, _outerProductType, 0, m, 0, n);
    else if (!a.isInSparseFormat())
        executeCellwiseDense(a.getDenseBlock(), ab[0], ab[1], b, scalars, out.getDenseBlock(), m, n, k, _outerProductType, 0, m, 0, n);
    else
        executeCellwiseSparse(a.getSparseBlock(), ab[0], ab[1], b, scalars, out, m, n, k, a.getNonZeros(), _outerProductType, 0, m, 0, n);
    return new DoubleObject(out.getDenseBlock().get(0, 0));
}

Example 8 with DenseBlock

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

the class SpoofRowwise method execute.

public MatrixBlock execute(ArrayList<MatrixBlock> inputs, ArrayList<ScalarObject> scalarObjects, MatrixBlock out, boolean allocTmp, boolean aggIncr) {
    // sanity check
    if (inputs == null || inputs.size() < 1 || out == null)
        throw new RuntimeException("Invalid input arguments.");
    // result allocation and preparations
    final int m = inputs.get(0).getNumRows();
    final int n = inputs.get(0).getNumColumns();
    final int n2 = _type.isConstDim2(_constDim2) ? (int) _constDim2 : _type.isRowTypeB1() || hasMatrixSideInput(inputs) ? getMinColsMatrixSideInputs(inputs) : -1;
    if (!aggIncr || !out.isAllocated())
        allocateOutputMatrix(m, n, n2, out);
    DenseBlock c = out.getDenseBlock();
    final boolean flipOut = _type.isRowTypeB1ColumnAgg() && LibSpoofPrimitives.isFlipOuter(out.getNumRows(), out.getNumColumns());
    // input preparation
    SideInput[] b = prepInputMatrices(inputs, 1, inputs.size() - 1, false, _tB1);
    double[] scalars = prepInputScalars(scalarObjects);
    // setup thread-local memory if necessary
    if (allocTmp && _reqVectMem > 0)
        LibSpoofPrimitives.setupThreadLocalMemory(_reqVectMem, n, n2);
    // core sequential execute
    MatrixBlock a = inputs.get(0);
    if (a instanceof CompressedMatrixBlock)
        executeCompressed((CompressedMatrixBlock) a, b, scalars, c, n, 0, m);
    else if (!a.isInSparseFormat())
        executeDense(a.getDenseBlock(), b, scalars, c, n, 0, m);
    else
        executeSparse(a.getSparseBlock(), b, scalars, c, n, 0, m);
    // post-processing
    if (allocTmp && _reqVectMem > 0)
        LibSpoofPrimitives.cleanupThreadLocalMemory();
    if (flipOut) {
        fixTransposeDimensions(out);
        out = LibMatrixReorg.transpose(out, new MatrixBlock(out.getNumColumns(), out.getNumRows(), false));
    }
    if (!aggIncr) {
        out.recomputeNonZeros();
        out.examSparsity();
    }
    return out;
}

Example 9 with DenseBlock

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

the class ColGroupDDC1 method computeRowSums.

@Override
protected 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();
    // pre-aggregate nnz per value tuple
    double[] vals = sumAllValues(kplus, kbuff, false);
    // for correctness in case of sqk+)
    for (int i = rl; i < ru; i++) {
        double[] cvals = c.values(i);
        int cix = c.pos(i);
        kbuff.set(cvals[cix], cvals[cix + 1]);
        kplus2.execute2(kbuff, vals[_data[i] & 0xFF]);
        cvals[cix] = kbuff._sum;
        cvals[cix + 1] = kbuff._correction;
    }
}

Example 10 with DenseBlock

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

the class ColGroupDDC2 method computeRowSums.

@Override
protected 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();
    // pre-aggregate nnz per value tuple
    double[] vals = sumAllValues(kplus, kbuff, false);
    // for correctness in case of sqk+)
    for (int i = rl; i < ru; i++) {
        double[] cvals = c.values(i);
        int cix = c.pos(i);
        kbuff.set(cvals[cix], cvals[cix + 1]);
        kplus2.execute2(kbuff, vals[_data[i]]);
        cvals[cix] = kbuff._sum;
        cvals[cix + 1] = kbuff._correction;
    }
}