Efficient synthesis of graph methods: a dynamically scheduled architecture.

RDF databases naturally map to a graph representation and employ languages, such as SPARQL, that implements queries as graph pattern matching routines. Graph methods exhibit an irregular behavior: they present unpredictable, fine-grained data accesses, and are synchronization intensive. Graph data structures expose large amounts of dynamic parallelism, but are difficult to partition without generating load unbalance. In this paper, we present a novel architecture to improve the synthesis of graph methods. Our design addresses the issues of these algorithms with two components: a Dynamic Task Scheduler (DTS), which reduces load unbalance and maximize resource utilization, and a Hierarchical Memory Interface controller (HMI), which provides support for concurrent memory operations on multi-ported/multi-banked shared memories. We evaluate our approach by generating the accelerators for a set of SPARQL queries from the Lehigh University Benchmark (LUBM). We first analyze the load unbalance of these queries, showing that execution time among tasks can differ even of order of magnitudes. We then synthesize the queries and compare the performance of the resulting accelerators against the current state of the art. Experimental results show that our solution provides a speedup over the serial implementation close to the theoretical maximum and a speedup up to 3.45 over a baseline parallel implementation. We conclude our study by exploring the design space to achieve maximum memory channels utilization. The best design used at least three of the four memory channels for more than 90% of the execution time.