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An empirical evaluation of High-Level Synthesis languages and tools for database acceleration
Arcas Abella, Oriol; Ndu, Geoffrey; Sonmez, N.; Ghasempour, Mohsen; Armejach, Adrià; Navaridas, Javier; Song, Wei; Mawer, John; Cristal Kestelman, Adrián; Lujan, Mikel
Universitat Politècnica de Catalunya. Departament d'Arquitectura de Computadors
High Level Synthesis (HLS) languages and tools are emerging as the most promising technique to make FPGAs more accessible to software developers. Nevertheless, picking the most suitable HLS for a certain class of algorithms depends on requirements such as area and throughput, as well as on programmer experience. In this paper, we explore the different trade-offs present when using a representative set of HLS tools in the context of Database Management Systems (DBMS) acceleration. More specifically, we conduct an empirical analysis of four representative frameworks (Bluespec SystemVerilog, Altera OpenCL, LegUp and Chisel) that we utilize to accelerate commonly-used database algorithms such as sorting, the median operator, and hash joins. Through our implementation experience and empirical results for database acceleration, we conclude that the selection of the most suitable HLS depends on a set of orthogonal characteristics, which we highlight for each HLS framework.
Peer Reviewed
Àrees temàtiques de la UPC::Informàtica::Sistemes d'informació::Bases de dades
Database management
Database systems
Field programmable gate arrays (FPGA)
Database algorithm
Empirical analysis
Empirical evaluations
High-level synthesis
Median operators
Programmer experiences
Software developer
Bases de dades -- Gestió
Institute of Electrical and Electronics Engineers (IEEE)

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