  The SuiteSparse GraphBLAS C-library implements high performance hypersparse
matrices with bindings to a variety of languages (Python, Julia, and
Matlab/Octave). GraphBLAS provides a lightweight in-memory database
implementation of hypersparse matrices that are ideal for analyzing many types
of network data, while providing rigorous mathematical guarantees, such as
linearity. Streaming updates of hypersparse matrices put enormous pressure on
the memory hierarchy. This work benchmarks an implementation of hierarchical
hypersparse matrices that reduces memory pressure and dramatically increases
the update rate into a hypersparse matrices. The parameters of hierarchical
hypersparse matrices rely on controlling the number of entries in each level in
the hierarchy before an update is cascaded. The parameters are easily tunable
to achieve optimal performance for a variety of applications. Hierarchical
hypersparse matrices achieve over 1,000,000 updates per second in a single
instance. Scaling to 31,000 instances of hierarchical hypersparse matrices
arrays on 1,100 server nodes on the MIT SuperCloud achieved a sustained update
rate of 75,000,000,000 updates per second. This capability allows the MIT
SuperCloud to analyze extremely large streaming network data sets.
