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VINEYARD Project gets 2x-10x speedup in Apache Spark Machine-Learning App from cloud-based FPGA acceleration

Xilinx Employee
Xilinx Employee
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I’ve written several times about Amazon’s AWS EC2 F1 instance, a cloud-based acceleration services based on multiple Xilinx Virtex UltraScale+ VU9P FPGAs. (See “AWS makes Amazon EC2 F1 instance hardware acceleration based on Xilinx Virtex UltraScale+ FPGAs generally available.”) The VINEYARD Project, a pan-European effort to significantly increase the performance and energy efficiency of data centers by leveraging the advantages of hardware accelerators, is using Amazon’s EC2 F1 instance to develop Apache Spark accelerators. VINEYARD project coordinator Christoforos Kachris from ICCS/NTU, of gave a presentation on "Hardware Acceleration of Apache Spark on Energy Efficient FPGAs" at SPARK Summit 2017 and a video of his presentation appears below.

 

Kachris’ presentation details experiments on accelerating machine-learning (ML) applications running on the Apache Spark cluster-computing framework by developing hardware-accelerated IP. The central idea is to create ML libraries that can be seamlessly invoked by programs simply by calling the appropriate library. No other program changes are needed to get the benefit of hardware acceleration. Raw data passes from a Spark Worker through a pipe, a Python API, and a C API to the FPGA acceleration IP and returns to the Spark Worker over a similar, reverse path.

 

The VINEYARD development team first prototyped their idea by creating a small model of the AWS EC2 F1 could-based system using four Digilent PYNQ-Z1 dev boards networked together via Ethernet and the Python-based, open-source PYNQ software development environment. Digilent’s PYNQ-Z1 dev boards are based on Xilinx Zynq Z-7020 SoCs. Even this small prototype dramatically doubled the performance relative to a Xeon server.

 

Having proved the concept, the VINEYARD development team scaled up to the AWS EC2 F1 and achieved a 3x to 10x performance improvement (cost normalized against an AWS instance with non-accelerated servers).

 

Here’s the 26-minute video presentation: