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- W2293057678 abstract "Due to rapidly expanding data size, there is increasing need for scalable, high-performance, and low-energy frameworks for large- scale data computation. We build a dataflow architecture that harnesses FPGA resources within a distributed analytics platform creating a heterogeneous data analytics framework. This approach leverages the scalability of existing distributed processing environments and provides easy access to custom hardware accelerators for large-scale data analysis. We prototype our framework within the Apache Spark analytics tool running on a CPU-FPGA heterogeneous cluster. As a specific application case study, we have chosen the MapReduce paradigm to implement a multi-purpose, scalable, and customizable RTL accelerator inside the FPGA, capable of incorporating custom High-Level Synthesis (HLS) MapReduce kernels. We demonstrate how a typical MapReduce application can be simply adapted to our distributed framework while retaining the scalability of the Spark platform." @default.
- W2293057678 created "2016-06-24" @default.
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- W2293057678 date "2016-02-21" @default.
- W2293057678 modified "2023-10-16" @default.
- W2293057678 title "A Scalable Heterogeneous Dataflow Architecture For Big Data Analytics Using FPGAs (Abstract Only)" @default.
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- W2293057678 doi "https://doi.org/10.1145/2847263.2847294" @default.
- W2293057678 hasPublicationYear "2016" @default.
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