High Level Synthesis of RDF Queries for Graph Analytics

Conference · Mon Nov 02 04:00:00 EST 2015

DOI:https://doi.org/10.1109/ICCAD.2015.7372587· OSTI ID:1510013

Castellana, Vito G. ^[1]; Minutoli, Marco ^[1]; Morari, Alessandro ^[1]; Tumeo, Antonino ^[1]; Lattuada, Marco ^[2]; Ferrandi, Fabrizio ^[2]

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1. BATTELLE (PACIFIC NW LAB)
2. Politecnico di Milano

In this paper we present a set of techniques that enable the synthesis of efficient custom accelerators for memory intensive, irregular applications. To address irregular applications challenges (large memory footprints, unpredictable fine- grained data accesses, and high synchronization intensity), and exploit their opportunities (thread level parallelism, memory level parallelism), we propose a novel accelerator design which take advantage of an adaptive and Distributed Controller (DC) architecture, and a Memory Interface (MI) that supports parallel memory subsystems. Among the multitude of algorithms that may benefit from our solution, we focus on the acceleration of graph analytics applications, and in particular, on the syn- thesis of SPARQL queries on Resource Description Framework (RDF) databases. We achieve this objective by incorporating the synthesis techniques into Bambu, an Open Source high- level synthesis tools, and interfacing the system with GEMS, the Graph database Engine for Multithreaded Systems. The front- end of GEMS generates optimized C implementations of the input queries, modeled as pattern matching routines, which are then automatically synthesized by Bambu. We validate our approach synthesizing several SPARQL queries from the Lehigh University Benchmark (LUBM).

• Research Organization: Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)
• Sponsoring Organization: USDOE
• DOE Contract Number: AC05-76RL01830
• OSTI ID: 1510013
• Report Number(s): PNNL-SA-113023
• Country of Publication: United States
• Language: English

Related Subjects

HLS
FPGA
Convey
RDF
GEMS
Memory-intensive