The relational model for database management: version 2
The relational model for database management: version 2
Database machines: an idea whose time has passed? A critique of the future of database machines
Parallel architectures for database systems
Computer Architecture: Complexity and Correctness
Computer Architecture: Complexity and Correctness
Implementing database operations using SIMD instructions
Proceedings of the 2002 ACM SIGMOD international conference on Management of data
Systolic (VLSI) arrays for relational database operations
SIGMOD '80 Proceedings of the 1980 ACM SIGMOD international conference on Management of data
VLSI Accelerators for Large Database Systems
IEEE Micro
Architectural features of CASSM: A Context Addressed Segment Sequential Memory
ISCA '78 Proceedings of the 5th annual symposium on Computer architecture
A methodology for supporting existing CODASYL databases with new database machines
ACM '78 Proceedings of the 1978 annual conference - Volume 2
Hardware acceleration for spatial selections and joins
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
Query co-processing on commodity processors
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
Relational joins on graphics processors
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
Column-stores vs. row-stores: how different are they really?
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
Fine- and Coarse-Grain Reconfigurable Computing
Fine- and Coarse-Grain Reconfigurable Computing
Reconfigurable Computing: Accelerating Computation with Field-Programmable Gate Arrays
Reconfigurable Computing: Accelerating Computation with Field-Programmable Gate Arrays
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The storage model of column-oriented databases is similar in structure to densely packed matrices/vectors found in many high-performance computing applications. Hence, hardware-accelerated vectorized matrix operations using Reconfigurable Logic (RL) coprocessors may find parallels in hardware acceleration of databases. In this article, we explore this hypothesis by proposing a multicontext, coarse-grained Reconfigurable coprocessor Unit (RU) model that is used to accelerate some of the database operations in hardware for column-oriented databases. We then describe the implementation of hardware algorithms for the equi-join, nonequi-join, and inverse-lookup database operations. Finally, we evaluate these algorithms using a microbenchmark query. Our results indicate that the query execution on the proposed RU model is one to two orders of magnitude faster than the software-only query execution.