A Preliminary Study on Extending the Apache Spark Framework for FPGA-Based Task Offloading

In recent years, Apache Spark has become one of the most widely used cluster computing frameworks globally, while FPGAs have increasingly been integrated into data centers for energy-efficient acceleration. In this paper, we explore the execution of computational tasks on FPGAs through the Apache Spark environment. To program the FPGA, we utilized the high-level synthesis platform SDSoC, where we developed a custom hardware platform named “zed.” Based on this platform, we implemented two applications: matrix multiplication and RGB-to-HSV image transformation, both written in high-level C++ code, with the goal of achieving faster execution through hardware acceleration. For the FPGA's operating system, we used PetaLinux 2017.4, specifically configured for the ARM Cortex ${ }^{\text {TM }}$-A9 processor. Additionally, we installed the Apache Spark environment on ZedBoard devices, creating a small-scale cluster consisting of two ZedBoards and a general-purpose computer for conducting our experiments. We evaluated the architecture using the two implemented applications. Experimental results show up to $11 \times$ speedup and $6.5 \times$ lower energy consumption for matrix multiplication, and up to $6.1 \times$ lower energy consumption for image conversion. Using two ZedBoards further improved execution time by $1.2 \times$. These results confirm the feasibility of executing Spark-based workloads on FPGA platforms and demonstrate the high performance-per-watt benefits of FPGA acceleration in distributed computing environments.