Dask Array implements a subset of the NumPy ndarray interface using blocked algorithms, cutting up the large array into many small arrays. This lets us compute on arrays larger than memory using all of our cores. We coordinate these blocked algorithms using dask graphs.
Dask arrays coordinate many NumPy arrays arranged into a grid. These NumPy arrays may live on disk or on other machines.
Today Dask array is commonly used in the sort of gridded data analysis that arises in weather, climate modeling, or oceanography, especially when data sizes become inconveniently large. Dask array complements large on-disk array stores like HDF5, NetCDF, and BColz. Additionally Dask array is commonly used to speed up expensive in-memory computations using multiple cores, such as you might find in image analysis or statistical and machine learning applications.
dask.array library supports the following interface from
- Arithmetic and scalar mathematics,
+, *, exp, log, ...
- Reductions along axes,
sum(), mean(), std(), sum(axis=0), ...
- Tensor contractions / dot products / matrix multiply,
- Axis reordering / transpose,
- Fancy indexing along single axes with lists or numpy arrays,
x[:, [10, 1, 5]]
- The array protocol
- Some linear algebra
svd, qr, solve, solve_triangular, lstsq
See the dask.array API for a more extensive list of functionality.
By default Dask array uses the threaded scheduler in order to avoid data transfer costs and because NumPy releases the GIL well. It is also quite effective on a cluster using the dask.distributed scheduler.
Dask array does not implement the entire numpy interface. Users expecting this will be disappointed. Notably, Dask array has the following limitations:
- Dask array does not implement all of
np.linalg. This has been done by a number of excellent BLAS/LAPACK implementations, and is the focus of numerous ongoing academic research projects.
- Dask array with unknown shapes do not support all operations
- Dask array does not attempt operations like
sortwhich are notoriously difficult to do in parallel, and are of somewhat diminished value on very large data (you rarely actually need a full sort). Often we include parallel-friendly alternatives like
- It is very inefficient to iterate over a Dask array with for loops.
- Dask development is driven by immediate need, and so many lesser used functions have not been implemented. Community contributions are encouraged.