A co-design approach for accelerated SQL query processing via FPGA-based data filtering

Becher, Andreas; Ziener, Daniel; Meyer-Wegener, Klaus; Teich, Jürgen

doi:10.1109/fpt.2015.7393148

Cited by 19 publications

(12 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Offloading operations for query processing to FPGAbased hardware accelerators has been researched well in [2,3,20,25,28,31,35], because of its small energy footprint and fast execution. Next to traditional co-processor systems, the "bump-in-the-wire" approach (as applied in ReProVide) has also been of great interest in approaches described in [16,28,32].…”

Section: Related Workmentioning

confidence: 99%

Speculative Dynamic Reconfiguration and Table Prefetching Using Query Look-Ahead in the ReProVide Near-Data-Processing System

et al. 2021

Self Cite

View full text Add to dashboard Cite

FPGAs are promising target architectures for hardware acceleration of database query processing, as they combine the performance of hardware with the programmability of software. In particular, they are partially reconfigurable at runtime, which allows for the runtime adaptation to a variety of queries. However, reconfiguration costs some time, and a region of the FPGA is not available for computations during its reconfiguration. Techniques to avoid or at least hide the reconfiguration latencies can improve the overall performance. This paper presents optimizations based on query look-ahead, which follows the idea of exploiting knowledge about subsequent queries for scheduling the reconfigurations such that their overhead is minimized. We evaluate our optimizations with a calibrated model for various parameter settings. Improvements in execution time can be “calculated” even if only being able to look one query ahead.

show abstract

Section: Related Workmentioning

confidence: 99%

Speculative Dynamic Reconfiguration and Table Prefetching Using Query Look-Ahead in the ReProVide Near-Data-Processing System

et al. 2021

Self Cite

View full text Add to dashboard Cite

show abstract

“…Static approaches which use FPGAs for query processing [4], [8], [18] succeeded in implementing query engines capable of doing SQL based processing (select, project join, etc.) with a very high throughput.…”

Section: Motivationmentioning

confidence: 99%

“…HDFS and Spark rely on GPPs and consequently inherit from GPP limitations mentioned previously. Regarding hardware acceleration, [4], [8], [14], [15], [18] have shown that Field Programmable Gate Arrays (FPGAs) could be a better alternative to GPPs in the field of database applications in general and for query-execution purposes with an increasing data throughput with lower energy effort. But not all queries can be easily implemented and processed in a FPGA.…”

Section: Introductionmentioning

confidence: 99%

Toward FPGA-Based Semantic Caching for Accelerating Data Analysis with Spark and HDFS

Maghzaoui

d’Orazio

Lallet

2019

Communications in Computer and Information Science

View full text Add to dashboard Cite

With the increase of data, traditional methods of data processing have become time and power inefficient. As enhancement, we propose a new accelerated architecture for querying big Databases. This architecture combines the advantages of the HDFS for the management of huge amount of data and the fast processing of queries of Spark SQL. It also benefits of the processing efficiency of the hardware acceleration of FPGAs and of the semantic caching architecture to process recently used data stored in the cache.

show abstract

“…However, in the best case, when the read operation of the hash table entry is hit in the cache, (in BRAM) only one cycle is enough to access it. By the way, the hardware-software co-designed bloom-filter-based hash join accelerator is proposed in [23] that can be a complementary solution for the introduced hash table caching technique in this paper. We do not use several hash functions, and we do not run into false positive problems as with the Bloom filter approach.…”

Section: Hash-based Units: Table Join and Groupbymentioning

confidence: 99%

“…In contrast, we utilize almost most of the free BRAMs of the FPGA as a cache to achieve an improved throughput. In [23], as Bloom filters use BRAMs to store the bit vector, the processing of large data tables may be limited by the size of BRAMs.…”

Section: Hash-based Units: Table Join and Groupbymentioning

confidence: 99%

AxleDB: A novel programmable query processing platform on FPGA

Salami

Malazgirt

Arcas-Abella

et al. 2017

Microprocessors and Microsystems

View full text Add to dashboard Cite

With the rise of Big Data, providing high-performance query processing capabilities through the acceleration of the database analytic has gained significant attention. Leveraging Field Programmable Gate Array (FPGA) technology, this approach can lead to clear benefits. In this work, we present the design and implementation of AxleDB: An FPGA-based platform that enables fast query processing for database systems by melding novel database-specific accelerators with commercial-off-the-shelf (COTS) storage using modern interfaces, in a novel, unified, and a programmable environment. AxleDB can perform a large subset of SQL queries through its set of instructions that can map compute-intensive database operations, such as filter, arithmetic, aggregate, group by, table join, or sort, on to the specialized high-throughput accelerators. To minimize the amount of SSD I/O operations required, AxleDB also supports hardware MinMax indexing for databases. We evaluated AxleDB with five decision support queries from the TPC-H benchmark suite and achieved a speedup from 1.8X to 34.2X and energy efficiency from 2.8X to 62.1X, in comparison to the state-of-the-art DBMS, i.e., PostgreSQL and MonetDB.The research leading to these results has received funding from the European Union Seventh Framework Program (FP7) (under the AXLE project GA number 318633), the Ministry of Economy and Competitiveness\ud of Spain (under contract number TIN2015-65316-p), Turkish Ministry of Development TAM Project (number 2007K120610), and Bogazici University Scientific Projects (number 7060).Peer ReviewedPostprint (author's final draft

show abstract

A co-design approach for accelerated SQL query processing via FPGA-based data filtering

Cited by 19 publications

References 9 publications

Speculative Dynamic Reconfiguration and Table Prefetching Using Query Look-Ahead in the ReProVide Near-Data-Processing System

Speculative Dynamic Reconfiguration and Table Prefetching Using Query Look-Ahead in the ReProVide Near-Data-Processing System

Toward FPGA-Based Semantic Caching for Accelerating Data Analysis with Spark and HDFS

AxleDB: A novel programmable query processing platform on FPGA

Contact Info

Product

Resources

About