  The Apache Accumulo database excels at distributed storage and indexing and
is ideally suited for storing graph data. Many big data analytics compute on
graph data and persist their results back to the database. These graph
calculations are often best performed inside the database server. The GraphBLAS
standard provides a compact and efficient basis for a wide range of graph
applications through a small number of sparse matrix operations. In this
article, we implement GraphBLAS sparse matrix multiplication server-side by
leveraging Accumulo's native, high-performance iterators. We compare the
mathematics and performance of inner and outer product implementations, and
show how an outer product implementation achieves optimal performance near
Accumulo's peak write rate. We offer our work as a core component to the
Graphulo library that will deliver matrix math primitives for graph analytics
within Accumulo.
