Analytical performance estimation for large-scale reconfigurable dataflow platforms

Ryota Yasudo, José G.F. Coutinho, Ana Lucia Varbanescu, Wayne Luk, Hideharu Amano, Tobias Becker, Ce Guo

Research output: Contribution to journalArticlepeer-review


Next-generation high-performance computing platforms will handle extreme data- and compute-intensive problems that are intractable with today's technology. A promising path in achieving the next leap in high-performance computing is to embrace heterogeneity and specialised computing in the form of reconfigurable accelerators such as FPGAs, which have been shown to speed up compute-intensive tasks with reduced power consumption. However, assessing the feasibility of large-scale heterogeneous systems requires fast and accurate performance prediction. This article proposes Performance Estimation for Reconfigurable Kernels and Systems (PERKS), a novel performance estimation framework for reconfigurable dataflow platforms. PERKS makes use of an analytical model with machine and application parameters for predicting the performance of multi-accelerator systems and detecting their bottlenecks. Model calibration is automatic, making the model flexible and usable for different machine configurations and applications, including hypothetical ones. Our experimental results show that PERKS can predict the performance of current workloads on reconfigurable dataflow platforms with an accuracy above 91%. The results also illustrate how the modelling scales to large workloads, and how performance impact of architectural features can be estimated in seconds.

Original languageEnglish
Article number3452742
JournalACM Transactions on Reconfigurable Technology and Systems
Issue number3
Publication statusPublished - 2021 Sept


  • FPGAs
  • Heterogeneous systems
  • Performance modelling
  • Reconfigurable dataflow platforms

ASJC Scopus subject areas

  • General Computer Science


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