High level synthesis of RDF queries for graph analytics

In this paper we present a set of techniques that enable the synthesis of efficient custom accelerators for memory intensive, irregular applications. To address the challenges of irregular applications (large memory footprint, 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 that employs an adaptive and Distributed Controller (DC) architecture, and a Memory Interface Controller (MIC) that supports concurrent and atomic memory operations on a multi-ported/multi-banked shared memory. 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 synthesis 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 it with GEMS, the Graph database Engine for Multithreaded Systems. The GEMS' front-end generates optimized C implementations of the input queries, modeled as graph pattern matching algorithms, which are then automatically synthesized by Bambu. We validate our approach by synthesizing several SPARQL queries from the Lehigh University Benchmark (LUBM)

Extracting and utilizing hidden structures in large datasets

The hidden structure within datasets --- capturing the inherent structure within the data not explicitly captured or encoded in the data format --- can often be automatically extracted and used to improve various data processing applications. Utilizing such hidden structure enables us to potentially surpass traditional algorithms that do not take this structure into account. In this thesis, we propose a general framework for algorithms that automatically extract and employ hidden structures to improve data processing performance, and discuss a set of design principles for developing such algorithms. We provide three examples to demonstrate the power of this framework in practice, showcasing how we can use hidden structures to either outperform state-of-the-art methods, or enable new applications that are previously impossible. We believe that this framework can offer new opportunities for the design of algorithms that surpass the current limit, and empower new applications in database research and many other data-centric disciplines

Dagstuhl News January - December 2005

"Dagstuhl News" is a publication edited especially for the members of the Foundation "Informatikzentrum Schloss Dagstuhl" to thank them for their support. The News give a summary of the scientific work being done in Dagstuhl. Each Dagstuhl Seminar is presented by a small abstract describing the contents and scientific highlights of the seminar as well as the perspectives or challenges of the research topic