Comparison of Direct and Indirect Networks for High-Performance FPGA Clusters

Antoniette Mondigo, Tomohiro Ueno, Kentaro Sano, Hiroyuki Takizawa

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

12 Citations (Scopus)


As field programmable gate arrays (FPGAs) become a favorable choice in exploring new computing architectures for the post-Moore era, a flexible network architecture for scalable FPGA clusters becomes increasingly important in high performance computing (HPC). In this paper, we introduce a scalable platform of indirectly-connected FPGAs, where its Ethernet-switching network allows flexibly customized inter-FPGA connectivity. However, for certain applications such as in stream computing, it is necessary to establish a connection-oriented datapath with backpressure between FPGAs. Due to the lack of physical backpressure channel in the network, we utilized our existing credit-based network protocol with flow control to provide receiver FPGA awareness and tailored it to minimize overall communication overhead for the proposed framework. To know its performance characteristics, we implemented necessary data transfer hardware on Intel Arria 10 FPGAs, modeled and obtained its communication performance, and compared it to a direct network. Results show that our proposed indirect framework achieves approximately 3% higher effective network bandwidth than our existing direct inter-FPGA network, which demonstrates good performance and scalability for large HPC applications.

Original languageEnglish
Title of host publicationApplied Reconfigurable Computing. Architectures, Tools, and Applications - 16th International Symposium, ARC 2020, Proceedings
EditorsFernando Rincón, Jesús Barba, Julián Caba, Hayden K.H. So, Pedro Diniz
Number of pages16
ISBN (Print)9783030445331
Publication statusPublished - 2020
Event16th International Symposium on Applied Reconfigurable Computing, ARC 2020 - Toledo, Spain
Duration: 2020 Apr 12020 Apr 3

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12083 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference16th International Symposium on Applied Reconfigurable Computing, ARC 2020


  • Direct network
  • Flexibility
  • Flow control
  • FPGA cluster
  • Indirect network
  • Scalability


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