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2012 21st International Conference on Parallel Architectures and Compilation Techniques (PACT) (2012)
Minneapolis, MN, USA
Sept. 19, 2012 to Sept. 23, 2012
ISBN: 978-1-5090-6609-4
pp: 411-420
Bharat Sukhwani , IBM T. J. Watson Research Center, Yorktown Heights, NY, 10598, USA
Hong Min , IBM T. J. Watson Research Center, Yorktown Heights, NY, 10598, USA
Mathew Thoennes , IBM T. J. Watson Research Center, Yorktown Heights, NY, 10598, USA
Parijat Dube , IBM T. J. Watson Research Center, Yorktown Heights, NY, 10598, USA
Balakrishna Iyer , IBM Santa Teresa Lab, 555 Bailey Ave, San Jose, CA 95141, USA
Bernard Brezzo , IBM T. J. Watson Research Center, Yorktown Heights, NY, 10598, USA
Donna Dillenberger , IBM T. J. Watson Research Center, Yorktown Heights, NY, 10598, USA
Sameh Asaad , IBM T. J. Watson Research Center, Yorktown Heights, NY, 10598, USA
ABSTRACT
Business growth and technology advancements have resulted in growing amounts of enterprise data. To gain valuable business insight and competitive advantage, businesses demand the capability of performing real-time analytics on such data. This, however, involves expensive query operations that are very time consuming on traditional CPUs. Additionally, in traditional database management systems (DBMS), the CPU resources are dedicated to mission-critical transactional workloads. Offloading expensive analytics query operations to a co-processor can allow efficient execution of analytics workloads in parallel with transactional workloads. In this paper, we present a Field Programmable Gate Array (FPGA) based acceleration engine for database operations in analytics queries. The proposed solution provides a mechanism for a DBMS to seamlessly harness the FPGA compute power without requiring any changes in the application or the existing data layout. Using a software-programmed query control block, the accelerator can be tailored to execute different queries without reconfiguration. Our prototype is implemented in a PCIe-attached FPGA system and is integrated into a commercial DBMS platform. The results demonstrate up to 94% CPU savings on real customer data compared to the baseline software cost with up to an order of magnitude speedup in the offloaded computations and up to 6.2× improvement in end-to-end performance.
INDEX TERMS
Field programmable gate arrays, Acceleration, Business, Structured Query Language, Indexes,acceleration, Relational database, analytics, FPGA
CITATION
Bharat Sukhwani, Hong Min, Mathew Thoennes, Parijat Dube, Balakrishna Iyer, Bernard Brezzo, Donna Dillenberger, Sameh Asaad, "Database analytics acceleration using FPGAs", 2012 21st International Conference on Parallel Architectures and Compilation Techniques (PACT), vol. 00, no. , pp. 411-420, 2012, doi:
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