Many-Core Acceleration of Common Graph Programming Frameworks

Graph analytics is a key component in identifying emerging trends and threats in many real-world applications. Large-scale graph analytics frameworks provide a convenient and highly scalable platform for developing algorithms to analyze large datasets. Although conceptually scalable, these techniques exhibit poor performance on modern computational hardware. Another model of graph computation has emerged that promises improved performance and scalability by using abstract linear algebra operations as the basis for graph analysis as laid out by the GraphBLAS standard. By using sparse linear algebra as the basis, existing highly efficient algorithms can be adapted to perform computations on the graph. This approach, however, is often less intuitive to graph analytics experts, who are accustomed to vertex-centric APIs such as Giraph, GraphX, and Tinkerpop. EM Photonics therefore proposes an implementation of the high-level operations supported by these APIs in terms of linear algebra operations, which will be parallel on each pair of vertices connected by an edge. This implementation will be backed by many-core implementations of the fundamental GraphBLAS operations required. This approach offers the advantages of both the intuitive programming model of a vertex-centric API and the performance of a sparse linear algebra implementation.