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

Example 16 with ValueFunction

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

the class SpoofCellwise method execute.

@Override
public ScalarObject execute(ArrayList<MatrixBlock> inputs, ArrayList<ScalarObject> scalarObjects, int k) {
    // sanity check
    if (inputs == null || inputs.size() < 1)
        throw new RuntimeException("Invalid input arguments.");
    // input preparation
    MatrixBlock a = inputs.get(0);
    SideInput[] b = prepInputMatrices(inputs);
    double[] scalars = prepInputScalars(scalarObjects);
    final int m = a.getNumRows();
    final int n = a.getNumColumns();
    // sparse safe check
    boolean sparseSafe = isSparseSafe() || (b.length == 0 && genexec(0, b, scalars, m, n, 0, 0) == 0);
    long inputSize = sparseSafe ? getTotalInputNnz(inputs) : getTotalInputSize(inputs);
    if (inputSize < PAR_NUMCELL_THRESHOLD) {
        // serial execution
        k = 1;
    }
    double ret = 0;
    if (// SINGLE-THREADED
    k <= 1) {
        if (inputs.get(0) instanceof CompressedMatrixBlock)
            ret = executeCompressedAndAgg((CompressedMatrixBlock) a, b, scalars, m, n, sparseSafe, 0, m);
        else if (!inputs.get(0).isInSparseFormat())
            ret = executeDenseAndAgg(a.getDenseBlock(), b, scalars, m, n, sparseSafe, 0, m);
        else
            ret = executeSparseAndAgg(a.getSparseBlock(), b, scalars, m, n, sparseSafe, 0, m);
    } else // MULTI-THREADED
    {
        try {
            ExecutorService pool = CommonThreadPool.get(k);
            ArrayList<ParAggTask> tasks = new ArrayList<>();
            int nk = (a instanceof CompressedMatrixBlock) ? k : UtilFunctions.roundToNext(Math.min(8 * k, m / 32), k);
            int blklen = (int) (Math.ceil((double) m / nk));
            if (a instanceof CompressedMatrixBlock)
                blklen = BitmapEncoder.getAlignedBlocksize(blklen);
            for (int i = 0; i < nk & i * blklen < m; i++) tasks.add(new ParAggTask(a, b, scalars, m, n, sparseSafe, i * blklen, Math.min((i + 1) * blklen, m)));
            // execute tasks
            List<Future<Double>> taskret = pool.invokeAll(tasks);
            pool.shutdown();
            // aggregate partial results
            ValueFunction vfun = getAggFunction();
            if (vfun instanceof KahanFunction) {
                KahanObject kbuff = new KahanObject(0, 0);
                KahanPlus kplus = KahanPlus.getKahanPlusFnObject();
                for (Future<Double> task : taskret) kplus.execute2(kbuff, task.get());
                ret = kbuff._sum;
            } else {
                for (Future<Double> task : taskret) ret = vfun.execute(ret, task.get());
            }
        } catch (Exception ex) {
            throw new DMLRuntimeException(ex);
        }
    }
    // correction for min/max
    if ((_aggOp == AggOp.MIN || _aggOp == AggOp.MAX) && sparseSafe && a.getNonZeros() < a.getNumRows() * a.getNumColumns())
        // unseen 0 might be max or min value
        ret = getAggFunction().execute(ret, 0);
    return new DoubleObject(ret);
}

Example 17 with ValueFunction

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

the class SpoofCellwise method execute.

@Override
public MatrixBlock execute(ArrayList<MatrixBlock> inputs, ArrayList<ScalarObject> scalarObjects, MatrixBlock out, int k) {
    // sanity check
    if (inputs == null || inputs.size() < 1 || out == null)
        throw new RuntimeException("Invalid input arguments.");
    // input preparation
    MatrixBlock a = inputs.get(0);
    SideInput[] b = prepInputMatrices(inputs);
    double[] scalars = prepInputScalars(scalarObjects);
    final int m = a.getNumRows();
    final int n = a.getNumColumns();
    // sparse safe check
    boolean sparseSafe = isSparseSafe() || (b.length == 0 && genexec(0, b, scalars, m, n, 0, 0) == 0);
    long inputSize = sparseSafe ? getTotalInputNnz(inputs) : getTotalInputSize(inputs);
    if (inputSize < PAR_NUMCELL_THRESHOLD) {
        // serial execution
        k = 1;
    }
    // result allocation and preparations
    boolean sparseOut = _type == CellType.NO_AGG && sparseSafe && a.isInSparseFormat();
    switch(_type) {
        case NO_AGG:
            out.reset(m, n, sparseOut);
            break;
        case ROW_AGG:
            out.reset(m, 1, false);
            break;
        case COL_AGG:
            out.reset(1, n, false);
            break;
        default:
            throw new DMLRuntimeException("Invalid cell type: " + _type);
    }
    out.allocateBlock();
    long lnnz = 0;
    if (// SINGLE-THREADED
    k <= 1) {
        if (inputs.get(0) instanceof CompressedMatrixBlock)
            lnnz = executeCompressed((CompressedMatrixBlock) a, b, scalars, out, m, n, sparseSafe, 0, m);
        else if (!inputs.get(0).isInSparseFormat())
            lnnz = executeDense(a.getDenseBlock(), b, scalars, out, m, n, sparseSafe, 0, m);
        else
            lnnz = executeSparse(a.getSparseBlock(), b, scalars, out, m, n, sparseSafe, 0, m);
    } else // MULTI-THREADED
    {
        try {
            ExecutorService pool = CommonThreadPool.get(k);
            ArrayList<ParExecTask> tasks = new ArrayList<>();
            int nk = UtilFunctions.roundToNext(Math.min(8 * k, m / 32), k);
            int blklen = (int) (Math.ceil((double) m / nk));
            if (a instanceof CompressedMatrixBlock)
                blklen = BitmapEncoder.getAlignedBlocksize(blklen);
            for (int i = 0; i < nk & i * blklen < m; i++) tasks.add(new ParExecTask(a, b, scalars, out, m, n, sparseSafe, i * blklen, Math.min((i + 1) * blklen, m)));
            // execute tasks
            List<Future<Long>> taskret = pool.invokeAll(tasks);
            pool.shutdown();
            // aggregate nnz and error handling
            for (Future<Long> task : taskret) lnnz += task.get();
            if (_type == CellType.COL_AGG) {
                // aggregate partial results
                double[] c = out.getDenseBlockValues();
                ValueFunction vfun = getAggFunction();
                if (vfun instanceof KahanFunction) {
                    for (ParExecTask task : tasks) LibMatrixMult.vectAdd(task.getResult().getDenseBlockValues(), c, 0, 0, n);
                } else {
                    for (ParExecTask task : tasks) {
                        double[] tmp = task.getResult().getDenseBlockValues();
                        for (int j = 0; j < n; j++) c[j] = vfun.execute(c[j], tmp[j]);
                    }
                }
                lnnz = out.recomputeNonZeros();
            }
        } catch (Exception ex) {
            throw new DMLRuntimeException(ex);
        }
    }
    // post-processing
    out.setNonZeros(lnnz);
    out.examSparsity();
    return out;
}

Example 18 with ValueFunction

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

the class SpoofCellwise method executeCompressedRowAggMxx.

private long executeCompressedRowAggMxx(CompressedMatrixBlock a, SideInput[] b, double[] scalars, double[] c, int m, int n, boolean sparseSafe, int rl, int ru) {
    Arrays.fill(c, rl, ru, (_aggOp == AggOp.MIN) ? Double.POSITIVE_INFINITY : Double.NEGATIVE_INFINITY);
    ValueFunction vfun = getAggFunction();
    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());
        c[cell.getI()] = vfun.execute(c[cell.getI()], val);
    }
    for (int i = rl; i < ru; i++) lnnz += (c[i] != 0) ? 1 : 0;
    return lnnz;
}

Example 19 with ValueFunction

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

the class SpoofCellwise method executeCompressedColAggMxx.

private long executeCompressedColAggMxx(CompressedMatrixBlock a, SideInput[] b, double[] scalars, double[] c, int m, int n, boolean sparseSafe, int rl, int ru) {
    Arrays.fill(c, rl, ru, (_aggOp == AggOp.MIN) ? Double.POSITIVE_INFINITY : Double.NEGATIVE_INFINITY);
    ValueFunction vfun = getAggFunction();
    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());
        c[cell.getI()] = vfun.execute(c[cell.getI()], val);
    }
    for (int i = rl; i < ru; i++) lnnz += (c[i] != 0) ? 1 : 0;
    return lnnz;
}

Example 20 with ValueFunction

use of org.apache.sysml.runtime.functionobjects.ValueFunction 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;
}