Modular FPGA Acceleration of Data Analytics in Heterogenous Computing
Elias Koromilas1, Christoforos Kachris2,a, Dimitrios Soudris2,b, Francisco J. Ballesteros3, Patricio Martinez4 and Ricardo Jimenez-Peris4
1NTUA, Athens, Greece LenXcale, Spain
elias.koromilas@gmail.com
2ICCS-NTUA Athens, Greece
akachris@microlab.ntua.gr
bdsoudris@microlab.ntua.gr
3Univ. Rey Juan Carlos Spain
4LeanXcale, Spain
ABSTRACT
Emerging cloud applications like machine learning, AI and big data analytics require high performance computing systems that can sustain the increased amount of data processing without consuming excessive power. Towards this end, many cloud operators have started deploying hardware accelerators, like FPGAs, to increase the performance of computationally intensive tasks but increasing the programming complexity to utilize these accelerators. VINEYARD has developed an efficient framework that allows the seamless deployment and utilization of hardware accelerators in the cloud without increasing the programming complexity and offering the flexibility of software packages. This paper presents a modular approach for the acceleration of data analytics using FPGAs. The modular approach allows the automatic development of integrated hardware designs for the acceleration of data analytics. The proposed framework shows the data analytics modules can be used to achieve up to 3.5x speedup compared to high performance generalpurpose processors.
Keywords: Data analytics, Databases, Cloud computing, FPGAs, Heterogeneous computing
