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FPGA as a Service: To Accelerate Your Big Data Workloads with FPGA with Weiting Chen (Intel)

1,135 views · Published 10 October 2018 · 31:02 · Indexed 10 November 2026

Channel: Databricks · 2018 · Science & Technology

In Big Data area, ETL(Extract, Load, Transform) is important data processing procedure to transfer raw data from a source server to a data warehouse. Field-programmable gate array(FPGA) with highly customized intellectual property(IP) can not only bring better performance but also lower power consumption to accelerate computation intensive segments for an application.

In this session, we would like to describe how FPGA can help a typical Spark ETL workloads to reduce high CPU utilization issue and release more CPU power to run some compute-intensive jobs. Furthermore, FPGA can also benefits deep learning applications for AI.

We will use micro-benchmarks as examples to identify a typical ETL/AI workload profiling, highlight the hotspots during data format transformation, and figure out which function costs higher CPU utilization in ETL procedure. Leveraging FPGA accelerator, we can move the functions with high CPU usages such as data source parsing or data compression/de-compression to use FPGA IPs and keep the CPU resource to only process some mission critical tasks which can improve the performance dramatically. Finally, we will give a real-world workload as a use case to explain how and when to use these FPGA IPs to optimize your Big Data applications.
In this topic, you will learn
How to identify the hotspots via microbenchmark for ETL/Deep Learning workloads
How to use FPGA to accelerate your ETL/Deep Learning workloads
Why you should use FPGA as an accelerator for Big Data and AI.

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FPGA as a Service: To Accelerate Your Big Data Workloads with FPGA with Weiting Chen (Intel)

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