Sparse Arrays

By swapping out in-memory NumPy arrays with in-memory sparse arrays, we can reuse the blocked algorithms of Dask's Array to achieve parallel and distributed sparse arrays.

The blocked algorithms in Dask Array normally parallelize around in-memory NumPy arrays. However, if another in-memory array library supports the NumPy interface, then it too can take advantage of Dask Array's parallel algorithms. In particular the sparse <https://github.com/pydata/sparse/>_ array library satisfies a subset of the NumPy API and works well with (and is tested against) Dask Array.

Example

Say we have a Dask array with mostly zeros:

.. code-block:: python

rng = da.random.default_rng() x = rng.random((100000, 100000), chunks=(1000, 1000)) x[x < 0.95] = 0

We can convert each of these chunks of NumPy arrays into a sparse.COO array:

.. code-block:: python

import sparse s = x.map_blocks(sparse.COO)

Now, our array is not composed of many NumPy arrays, but rather of many sparse arrays. Semantically, this does not change anything. Operations that work will continue to work identically (assuming that the behavior of numpy and sparse are identical), but performance characteristics and storage costs may change significantly:

.. code-block:: python

s.sum(axis=0)[:100].compute() <COO: shape=(100,), dtype=float64, nnz=100>

_.todense() array([ 4803.06859272, 4913.94964525, 4877.13266438, 4860.7470773 , 4938.94446802, 4849.51326473, 4858.83977856, 4847.81468485, ... ])

Requirements

Any in-memory library that copies the NumPy ndarray interface should work here. The sparse <https://github.com/pydata/sparse/>_ library is a minimal example. In particular, an in-memory library should implement at least the following operations:

1. Simple slicing with slices, lists, and elements (for slicing, rechunking, reshaping, etc)
2. A concatenate function matching the interface of np.concatenate. This must be registered in dask.array.core.concatenate_lookup
3. All ufuncs must support the full ufunc interface, including dtype= and out= parameters (even if they don't function properly)
4. All reductions must support the full axis= and keepdims= keywords and behave like NumPy in this respect
5. The array class should follow the __array_priority__ protocol and be prepared to respond to other arrays of lower priority
6. If dot support is desired, a tensordot function matching the interface of np.tensordot should be registered in dask.array.core.tensordot_lookup

The implementation of other operations like reshape, transpose, etc., should follow standard NumPy conventions regarding shape and dtype. Not implementing these is fine; the parallel dask.array will err at runtime if these operations are attempted.

Mixed Arrays

Dask's Array supports mixing different kinds of in-memory arrays. This relies on the in-memory arrays knowing how to interact with each other when necessary. When two arrays interact, the functions from the array with the highest __array_priority__ will take precedence (for example, for concatenate, tensordot, etc.).