A parallel HDF5-based library for storage and processing of large-scale graphs and neural cell model attributes.
The neuroh5 library implements an HDF5-based format for storing neuronal morphology information, synaptic and connectivity information of large neural networks, and perform parallel graph partitioning and analysis.
neuroh5 assumes that synaptic connectivity between neurons in neuronal network models is represented as directed graphs stored as adjacency lists, where the vertices represent the neurons in the network and are identified by unsigned integers called unique global identifiers (gid).
Building and installing NeuroH5
NeuroH5 requires parallel HDF5, MPI, cmake. The Python module requires Python 3 and numpy.
To build the NeuroH5 C++ library and applications:
git clone https://github.com/iraikov/neuroh5.git
cd neuroh5
cmake .
make
To build the python module:
git clone https://github.com/iraikov/neuroh5.git
cd neuroh5
CMAKE_BUILD_PARALLEL_LEVEL=8 \
CMAKE_MPI_C_COMPILER=$(which mpicc) \
CMAKE_MPI_CXX_COMPILER=$(which mpicxx) \
pip install .
Connectivity between neurons is described in terms of vertices and directed edges, where each vertex has an integer id associated with it, which corresponds to the id of the respective neuron in the network simulation. Each edge is identified by source and destination vertex, and a number of additional attributes, such as distance and synaptic weight. In addition, the vertices are organized in populations of neurons, where each population is comprised of the set of neurons that belong to the same biological type of neuron (such as granule cell or basket cell). Connectivity is organized in projections, where a projection is the set of connections between two populations, or withing the same population. A projection is identified by its source and destination populations, and all edges between those two populations.
An adjacency matrix is a square matrix where the elements of the matrix indicate whether pairs of vertices are connected or not in the graph. A sparse adjacency matrix representation explicitly stores only the source or only the destination vertices, and uses range data structures to indicate which vertices are connected. For example, in one type of sparse format the source dataset will contain only how many destination vertices are associated with a given source, but will not explicitly store the source vertex ids.
Our initial implementation of an HDF5-based graph representation includes two types of representation. The first one is a direct edge-list-type representation where source and destination indices are explicitly represented as HDF5 datasets. The second one is what we refer to as Destination Block Sparse format, which is a type of sparse adjacency matrix representation where the connectivity is additionally divided into blocks of contiguous indices to account for potential gaps in connectivity (i.e. ranges of indices that are not connected). In the next section, we present the Destination Block Sparse format, and present details of the implementation and initial performance metrics.
In the Destination Block Sparse format the destination indices are stored in blocks (of destinations). The following invariants hold:
- The destination indices in a block are contiguous.
- The number of destinations per block may vary from block to block.
The Destination Block Sparse format consists of the following datasets:
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Source Index : This array holds the indices of all source vertices in the projection. It's length is equal to the number of edges in the projection.
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Destination Index : This array holds the first destination index in each block. Its length is equal to the number of blocks.
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Destination Block Pointer : This array holds offsets into the Destination Pointer array. Its length is equal to the number of blocks plus one. The number of destinations in block i equals: Destination Block Pointer[i + 1] – Destination Block Pointer[i] The destination index of destination j in block i is Destination Index[i] + j.
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Destination Pointer : This array holds offsets into the Source Index and edge attribute datasets. Its length is equal to the sum of the destination counts in all blocks plus one. For each destination block, Destination Pointer stores one offset per destination in the block. The number of source entries for destination j of block i equals:
Destination Pointer[Destination Block Pointer[i] + j + 1] - estination Pointer[Destination Block Pointer[i] + j]
Several datasets are defined that hold the non-zero edge attributes of a projection. Each edge attribute dataset is of the same length as the Source Index datasets (i.e. the number of edges in the projection).