High-Performance Big Data Analytics Processing Using Hardware Acceleration

In order to address the challenge of increasingly expensive and time-consuming big data analytics pipelines, hardware accelerators such as GPUs and FPGAs are increasingly being used to reduce the overhead associated with data processing, and improve the utilization as well as the cost and power efficiency of compute infrastructure. These systems are being integrated in various cloud services such as Amazon and Nimbix, and have become a prime feature of the Microsoft Azure offering.This talk will give an introduction to FPGAs and discuss their advantages and challenges in the context of big data analytics. We’ll also discuss Fletcher, an open source platform to integrate FPGA accelerators with big data analytics frameworks efficiently. Based on Apache Arrow, Fletcher is intended to tackle the challenges of long development times and poor cross-platform support, and FPGA components are easily integrated into Arrow pipelines. We will present several high-throughput applications where FPGA accelerators are integrated into big data analytics pipelines. This includes regular expression matching achieving up to 60x acceleration, Parquet decompression and Arrow conversion at 3x acceleration allowing real-time Parquet data ingest, and ultra-low latency JSON to Arrow conversion.Finally, we will demonstrate FPGA integration into Dremio, allowing for the transparent acceleration of SQL queries on high-performance accelerators.

Topics Covered

Apache Arrow
In-Memory Formats


Zaid Al-Ars

Zaid Al-Ars

Zaid Al-Ars is an associate professor at Delft University of Technology, where he leads the Accelerated Big Data Systems group, focusing on developing computing infrastructures for efficient processing of big data analytics applications. Zaid is also co-founder of a couple of big data companies specializing in high-performance analytics solutions and AI, and serves on the advisory board of a number of high-tech startups.

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