A Novel Framework for the Seamless Integration of FPGA Accelerators with Big Data Analytics Frameworks in Heterogeneous Data Centers
| Autor: | Ioannis Stamelos, Christoforos Kachris, Dimitrios Soudris, Elias Koromilas |
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| Rok vydání: | 2018 |
| Předmět: |
Scheme (programming language)
Java business.industry Scala Computer science Big data Cloud computing 02 engineering and technology Computer architecture Software deployment 020204 information systems 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Data center business Field-programmable gate array computer computer.programming_language |
| Zdroj: | HPCS |
| DOI: | 10.1109/hpcs.2018.00090 |
| Popis: | To face the increased network traffic in the cloud, data center operators have started adopting an heterogeneous approach in their infrastructures. Heterogeneous infrastructures, e.g. based on FPGAs, can provide higher performance and better energy-efficiency compared to the contemporary processors. However, FPGAs lack of an easy-to-use framework for the efficient deployment from high-level programming frameworks. In this paper, we present a novel framework that allows the seamless integration of FPGAs from high-level programming languages, like Java and Scala. The proposed approach provides all the required APIs for the utilization of FPGAs from these languages. The proposed scheme has been mapped on Amazon AWS f1 infrastructure and a performance evaluation is presented for two widely used machine learning algorithms. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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