What is DistArray?

DistArray aims to bring the strengths of NumPy to data-parallel high-performance computing. It provides distributed multi-dimensional NumPy-like arrays and distributed ufuncs, distributed IO capabilities, and can integrate with external distributed libraries like Trilinos. DistArray works with NumPy and builds on top of it in a flexible and natural way.

0.4 Release

This is the third development release.

Noteworthy improvements in 0.4 include:

• basic slicing support;
• significant performance enhancements;
• reduction methods now support boolean arrays;
• an IPython notebook that demos basic functionality; and
• many bug fixes, API improvements, and refactorings.

DistArray is nearly ready for real-world use. The project is evolving rapidly and input from the larger scientific-Python community is very valuable and helps drive development.

Existing features

DistArray:

• has a client-engine (or master-worker) process design -- data resides on the worker processes, and commands are initiated from master;
• allows full control over what is executed on the worker processes and integrates transparently with the master process;
• allows direct communication between workers, bypassing the master process for scalability;
• integrates with IPython.parallel for interactive creation and exploration of distributed data;
• supports distributed ufuncs (currently without broadcasting);
• builds on and leverages MPI via MPI4Py in a transparent and user-friendly way;
• supports NumPy-like multidimensional arrays;
• has basic support for unstructured arrays;
• supports user-controllable array distributions across workers (block, cyclic, block-cyclic, and unstructured) on a per-axis basis;
• has a straightforward API to control how an array is distributed;
• has basic plotting support for visualization of array distributions;
• separates the array’s distribution from the array’s data -- useful for slicing, reductions, redistribution, broadcasting, and other operations;
• implements distributed random arrays;
• supports .npy-like flat-file IO and hdf5 parallel IO (via h5py); leverages MPI-based IO parallelism in an easy-to-use and transparent way; and
• supports the distributed array protocol [protocol], which allows independently developed parallel libraries to share distributed arrays without copying, analogous to the PEP-3118 new buffer protocol.

Planned features and roadmap

Near-term features and improvements include:

• MPI-only communication for performance and deployment on clusters and supercomputers;
• array re-distribution capabilities;
• interoperation with Trilinos [Trilinos];
• expanded tutorials, examples, and other introductory material; and
• distributed broadcasting support.

The longer-term roadmap includes:

• Lazy evaluation and deferred computation for latency hiding;
• Integration with other packages [petsc] that subscribe to the distributed array protocol [protocol];
• Distributed fancy indexing;
• Out-of-core computations;
• Support for distributed sorting and other non-trivial distributed algorithms; and
• End-user control over communication and temporary array creation, and other performance aspects of distributed computations.

History and funding

Brian Granger started DistArray as a NASA-funded SBIR project in 2008. Enthought picked it up as part of a DOE Phase II SBIR [SBIR] to provide a generally useful distributed array package. It builds on NumPy, MPI, MPI4Py, IPython, IPython.parallel, and interfaces with the Trilinos suite of distributed HPC solvers (via PyTrilinos [Trilinos]).

This material is based upon work supported by the Department of Energy under Award Number DE-SC0007699.

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