Accelerating Hash-Based Query Processing Operations on FPGAs by a Hash Table Caching Technique

Extracting valuable information from the rapidly growing field of Big Data faces serious performance constraints, especially in the software-based database management systems (DBMS). In a query processing system, hash-based computational primitives such as the hash join and the group-by are the most time-consuming operations, as they frequently need to access the hash table on the high-latency off-chip memories and also to traverse whole the table. Subsequently, the hash collision is an inherent issue related to the hash tables, which can adversely degrade the overall performance.

[1]  Bharat Sukhwani,et al.  Accelerating Join Operation for Relational Databases with FPGAs , 2013, 2013 IEEE 21st Annual International Symposium on Field-Programmable Custom Computing Machines.

[2]  Hideyuki Kawashima,et al.  An Implementation of Handshake Join on FPGA , 2011, 2011 Second International Conference on Networking and Computing.

[3]  Jens Teubner,et al.  Data Processing on FPGAs , 2013, Proc. VLDB Endow..

[4]  Behzad Salami,et al.  HATCH: Hash Table Caching in Hardware for Efficient Relational Join on FPGA , 2015, 2015 IEEE 23rd Annual International Symposium on Field-Programmable Custom Computing Machines.

[5]  Bingsheng He,et al.  Revisiting Co-Processing for Hash Joins on the Coupled CPU-GPU Architecture , 2013, Proc. VLDB Endow..

[6]  Hansjörg Zeller,et al.  An Adaptive Hash Join Algorithm for Multiuser Environments , 1990, VLDB.

[7]  Behzad Salami,et al.  Hardware Acceleration for Query Processing: Leveraging FPGAs, CPUs, and Memory , 2016, Computing in Science & Engineering.

[8]  Babak Falsafi,et al.  Meet the walkers accelerating index traversals for in-memory databases , 2013, 2013 46th Annual IEEE/ACM International Symposium on Microarchitecture (MICRO).

[9]  Jim Tørresen,et al.  FPGASort: a high performance sorting architecture exploiting run-time reconfiguration on fpgas for large problem sorting , 2011, FPGA '11.

[10]  Gustavo Alonso,et al.  A flexible hash table design for 10GBPS key-value stores on FPGAS , 2013, 2013 23rd International Conference on Field programmable Logic and Applications.

[11]  Rajesh Gupta,et al.  Minerva: Accelerating Data Analysis in Next-Generation SSDs , 2013, 2013 IEEE 21st Annual International Symposium on Field-Programmable Custom Computing Machines.

[12]  Kenneth A. Ross,et al.  Q100: the architecture and design of a database processing unit , 2014, ASPLOS.

[13]  Vassilis J. Tsotras,et al.  FPGA-based Multithreading for In-Memory Hash Joins , 2015, CIDR.

[14]  Oscar Palomar,et al.  Future Vector Microprocessor Extensions for Data Aggregations , 2016, 2016 ACM/IEEE 43rd Annual International Symposium on Computer Architecture (ISCA).

[15]  Kunle Olukotun,et al.  Hardware acceleration of database operations , 2014, FPGA.

[16]  Ling Liu,et al.  Achieving 10Gbps Line-rate Key-value Stores with FPGAs , 2013, HotCloud.

[17]  Jens Teubner,et al.  Low-Latency Handshake Join , 2014, Proc. VLDB Endow..

[18]  Mateo Valero,et al.  Vector Extensions for Decision Support DBMS Acceleration , 2012, 2012 45th Annual IEEE/ACM International Symposium on Microarchitecture.

[19]  Eric S. Chung,et al.  LINQits: big data on little clients , 2013, ISCA.

[20]  Jürgen Teich,et al.  On-the-fly Composition of FPGA-Based SQL Query Accelerators Using a Partially Reconfigurable Module Library , 2012, 2012 IEEE 20th International Symposium on Field-Programmable Custom Computing Machines.

[21]  Gustavo Alonso,et al.  Less watts, more performance: an intelligent storage engine for data appliances , 2013, SIGMOD '13.