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

Example 36 with DenseBlock

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

the class ResultMergeRemoteSparkWCompare method call.

@Override
public Tuple2<MatrixIndexes, MatrixBlock> call(Tuple2<MatrixIndexes, Tuple2<Iterable<MatrixBlock>, MatrixBlock>> arg) throws Exception {
    MatrixIndexes ixin = arg._1();
    Iterator<MatrixBlock> din = arg._2()._1().iterator();
    MatrixBlock cin = arg._2()._2();
    // create compare array
    DenseBlock compare = DataConverter.convertToDenseBlock(cin, false);
    // merge all blocks into compare block
    MatrixBlock out = new MatrixBlock(cin);
    while (din.hasNext()) mergeWithComp(out, din.next(), compare);
    // create output tuple
    return new Tuple2<>(new MatrixIndexes(ixin), out);
}

Example 37 with DenseBlock

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

the class DataConverter method convertToDenseBlock.

public static DenseBlock convertToDenseBlock(MatrixBlock mb, boolean deep) {
    int rows = mb.getNumRows();
    int cols = mb.getNumColumns();
    DenseBlock ret = (!mb.isInSparseFormat() && mb.isAllocated() && !deep) ? mb.getDenseBlock() : // 0-initialized
    DenseBlockFactory.createDenseBlock(rows, cols);
    if (!mb.isEmptyBlock(false)) {
        if (mb.isInSparseFormat()) {
            Iterator<IJV> iter = mb.getSparseBlockIterator();
            while (iter.hasNext()) {
                IJV cell = iter.next();
                ret.set(cell.getI(), cell.getJ(), cell.getV());
            }
        } else if (deep) {
            ret.set(mb.getDenseBlock());
        }
    }
    return ret;
}

Example 38 with DenseBlock

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

the class SpoofCellwise method executeSparseNoAggDense.

private long executeSparseNoAggDense(SparseBlock sblock, SideInput[] b, double[] scalars, MatrixBlock out, int m, int n, boolean sparseSafe, int rl, int ru) {
    // note: sequential scan algorithm for both sparse-safe and -unsafe
    // in order to avoid binary search for sparse-unsafe
    DenseBlock c = out.getDenseBlock();
    long lnnz = 0;
    for (int i = rl; i < ru; i++) {
        int lastj = -1;
        // handle non-empty rows
        if (sblock != null && !sblock.isEmpty(i)) {
            int apos = sblock.pos(i);
            int alen = sblock.size(i);
            int[] aix = sblock.indexes(i);
            double[] avals = sblock.values(i);
            double[] cvals = c.values(i);
            int cix = c.pos(i);
            for (int k = apos; k < apos + alen; k++) {
                // process zeros before current non-zero
                if (!sparseSafe)
                    for (int j = lastj + 1; j < aix[k]; j++) lnnz += ((cvals[cix + j] = genexec(0, b, scalars, m, n, i, j)) != 0) ? 1 : 0;
                // process current non-zero
                lastj = aix[k];
                lnnz += ((cvals[cix + lastj] = genexec(avals[k], b, scalars, m, n, i, lastj)) != 0) ? 1 : 0;
            }
        }
        // process empty rows or remaining zeros
        if (!sparseSafe)
            for (int j = lastj + 1; j < n; j++) {
                double[] cvals = c.values(i);
                int cix = c.pos(i);
                lnnz += ((cvals[cix + j] = genexec(0, b, scalars, m, n, i, j)) != 0) ? 1 : 0;
            }
    }
    return lnnz;
}

Example 39 with DenseBlock

use of org.apache.sysml.runtime.matrix.data.DenseBlock in project 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 40 with DenseBlock

use of org.apache.sysml.runtime.matrix.data.DenseBlock in project 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));
}