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Distributed Interactive Visualization and Exploration of large datasets

Project description


Distributed Interactive Visualization and Exploration of large datasets.

## What is pyDive?

Use pyDive to work with homogeneous, n-dimensional arrays that are too big to fit into your local machine's memory.
pyDive provides containers whose elements are distributed across a cluster or stored in
a large hdf5/adios-file if the cluster is still too small. All computation and data-access is then done in parallel by the cluster nodes in the background.
If you feel like working with [numpy]( arrays pyDive has reached the goal!

pyDive is developed and maintained by the **[Junior Group Computational Radiation Physics](**
at the [Institute for Radiation Physics](
at [HZDR](

- Since all cluster management is given to [IPython.parallel]( you can take your
existing profiles for pyDive. No further cluster configuration needed.
- Save bandwidth by slicing an array in parallel on disk first before loading it into main memory!
- GPU-cluster array available thanks to [pycuda]( with additional support for non-contiguous memory.
- As all of pyDive's distributed array types are auto-generated from local arrays like numpy, hdf5, pycuda, etc...
you can easily make your own local array classes distributed too.

## Dive in!

import pyDive

h5field ="myData.h5", "myDataset", distaxes=(0,1))
ones = pyDive.ones_like(h5field)

# Distribute file i/o and computation across the cluster
h5field[::10,:] = h5field[::10,:].load() + 5.0 * ones[::10,:]

## Documentation

In our [Online Documentation](, [pdf]( you can find
detailed information on all interfaces as well as some [Tutorials](
and a [Quickstart](

## Software License

pyDive is licensed under the **GPLv3+ and LGPLv3+** (it is *dual licensed*).
Licences can be found in [GPL](COPYING) or [LGPL](COPYING.LESSER), respectively.

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