Paper Info
Title
Hardware acceleration of database operations
Abstract
As the amount of memory in database systems grows, entire database tables, or even databases, are able to fit in the system's memory, making in-memory database operations more prevalent. This shift from disk-based to in-memory database systems has contributed to a move from row-wise to columnar data storage. Furthermore, common database workloads have grown beyond online transaction processing (OLTP) to include online analytical processing and data mining. These workloads analyze huge datasets that are often irregular and not indexed, making traditional database operations like joins much more expensive. In this paper we explore using dedicated hardware to accelerate in-memory database operations. We present hardware to accelerate the selection process of compacting a single column into a linear column of selected data, joining two sorted columns via merging, and sorting a column. Finally, we put these primitives together to accelerate an entire join operation. We implement a prototype of this system using FPGAs and show substantial improvements in both absolute throughput and utilization of memory bandwidth. Using the prototype as a guide, we explore how the hardware resources required by our design change with the desired throughput.
Year
DOI
Venue
2014
10.1145/2554688.2554787
FPGA
Keywords
Field
DocType
common database workloads,hardware acceleration,hardware resource,traditional database operation,dedicated hardware,database system,linear column,entire database table,data storage,data mining,in-memory database operation,fpga,database,sort
Computer science,View,Real-time computing,Physical data model,Database index,Database tuning,Parallel computing,Online transaction processing,Database testing,Hardware acceleration,Online analytical processing,Database,Operating system
Conference
Citations 
PageRank 
References 
56
1.84
16
Authors
2
Name
Order
Citations
PageRank
Jared Casper182434.12
Kunle Olukotun24532373.50