Abstract
Emerging cloud applications like machine learning and data analytics need to process huge amount of data. Typical processor architecture cannot achieve efficient processing of the vast amount of data without consuming excessive amount of energy. Therefore, novel architectures have to be adopted in the future data centers in order to face the increased amount of data that needs to be processed. In this paper, we present a novel scheme for the seamless deployment of FPGAs in the data centers under the Spark framework. The proposed scheme, developed in the VINEYARD project, allows the efficient utilization of FPGAs without the need to change the applications. The performance evaluation is based on the KMeans ML algorithm that is widely used in clustering applications. The proposed scheme has been evaluated in a cluster of heterogeneous MPSoCs. The performance evaluation shows that the utilization of FPGAs can be used to speedup the machine learning applications and reduce significantly the energy consumption.
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Acknowledgment
This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 687628 - VINEYARD: Versatile Integrated Heterogeneous Accelerator-based Data Centers.
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Kachris, C., Stamelos, I., Koromilas, E., Soudris, D. (2018). Seamless FPGA Deployment over Spark in Cloud Computing: A Use Case on Machine Learning Hardware Acceleration. In: Voros, N., Huebner, M., Keramidas, G., Goehringer, D., Antonopoulos, C., Diniz, P. (eds) Applied Reconfigurable Computing. Architectures, Tools, and Applications. ARC 2018. Lecture Notes in Computer Science(), vol 10824. Springer, Cham. https://doi.org/10.1007/978-3-319-78890-6_54
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DOI: https://doi.org/10.1007/978-3-319-78890-6_54
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