In this session we will present a Configurable FPGA-Based Spark SQL Acceleration Architecture. It is target to leverage FPGA highly parallel computing capability to accelerate Spark SQL Query and for FPGA’s higher power efficiency than CPU we can lower the power consumption at the same time. The Architecture consists of SQL query decomposition algorithms, fine-grained FPGA based Engine Units which perform basic computation of sub string, arithmetic and logic operations. Using SQL query decomposition algorithm, we are able to decompose a complex SQL query into basic operations and according to their patterns each is fed into an Engine Unit. SQL Engine Units are highly configurable and can be chained together to perform complex Spark SQL queries, finally one SQL query is transformed into a Hardware Pipeline. We will present the performance benchmark results comparing the queries with FGPA-Based Spark SQL Acceleration Architecture on XEON E5 and FPGA to the ones with Spark SQL Query on XEON E5 with 10X ~ 100X improvement and we will demonstrate one SQL query workload from a real customer.
Session hashtag: #EUres0
Wang Quanfu is a senior architect of Intel Big Data team. He is working on software optimization and acceleration on IA ARCH and Heterogeneous Computing. He was a lead software engineer at Alcatel-Lucent wireline business group before 2014 and now works for computing virtualization and big data at Intel.
Xie Qi is a senior architect of Intel Big Data team. He once worked for IT Flags at Intel and joined Intel Big Data team in 2016 and has a broad experience across Big Data, Multi Media and Wireless.