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Speeding Up Spark with Data Compression on Xeon+FPGA

1,209 views · Published 8 June 2017 · 27:45 · Indexed 12 November 2026

Channel: Databricks · 2017 · Science & Technology

"Data compression is a key aspect in big data processing frameworks, such as Apache Hadoop and Spark, because compression enables the size of the input, shuffle and output data to be reduced, thus potentially speeding up overall processing time by orders of magnitude, especially for large-scale systems. However, since many compression algorithms with good compression ratio are also very CPU-intensive, developers are often forced to use algorithms that are less CPU-intensive at the cost of reduced compression ratio.

In this session, you'll learn about a field-programmable gate array (FPGA)-based approach for accelerating data compression in Spark. By opportunistically offloading compute-heavy, compression tasks to the FPGA, the CPU is freed to perform other tasks, resulting in an improved overall performance for end-user applications. In contrast to existing GPU methods for acceleration, this approach affords more performance/energy efficiency, which can translate to significant savings in power and cooling costs, especially for large datacenters. In addition, this implementation offers the benefit of reconfigurability, allowing for the FPGA to be rapidly reprogrammed with a different algorithm to meet system or user requirements.

Using the Intel Xeon+FPGA platform, Ojika will share how they ported Swif (simplified workload-intuitive framework) to Spark, and the method used to enable an end-to-end, FPGA-aware Spark deployment. Swif is an in-house framework developed to democratize and simplify the deployment of FPGAs in heterogeneous datacenters. Using Swif’s application programmable interface (API), he'll describe how system architects and software developers can seamlessly integrate FPGAs into their Spark workflow, and in particular, deploy FPGA-based compression schemes that achieve improved performance compared to software-only approaches. In general, Swif’s software stack, along with the underlying Xeon+FPGA hardware platform, provides a workload-centric processing environment that streamlines the process of offloading CPU-intensive tasks to shared FPGA resources, while providing improved system throughput and high resource utilization.

Session hashtag: #SFr6"

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Speeding Up Spark with Data Compression on Xeon+FPGA

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