A quad-form clustered mapping approach for large-scale applications of reconfigurable computing systems. (January 2022)

• Record Type: Journal Article
• Authors: Mohtavipour, Seyed Mehdi; Shahhoseini, Hadi Shahriar
• Is Part Of: Computers & electrical engineering. Volume 97(2022)
• Publication Date: 2022-01
• Subjects: Resource management -- Resource mapping -- Reconfigurable hardware -- Application compilation -- Distributed computing; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2021.107637
• Languages: English
• ISSNs: 0045-7906

Abstract

Highlights: A quad-form clustering approach targeting the long compilation time overhead in reconfigurable computing systems. Formulating the resource mapping problem in matrix form. Solving the mapping problem in multi-levels including intra and inter-cluster parts. A novel analytical resource distance estimation for computing the cluster mapping cost. A customized application partitioning algorithm matching the proposed quad-form resource clustering. Abstract: Reconfigurable Computing (RC) systems with hardware implementation feature demonstrated a promising solution for the demand of enormous processing power. However, RC systems suffer from long compilation phases when preparing large-scale applications to execute on reconfigurable hardware. In this paper, a novel quad-form clustered approach has been introduced to manage the hardware resources with low overhead and acceptable quality. For this purpose, two quad-form resource clustering and application partitioning algorithms are proposed to solve the entire mapping problem in intra and inter-cluster parts. Moreover, an analytical distance estimation is derived to properly manage clustered resources in the reconfigurable hardware. Several extensive experimental scenarios on both synthetic and real applications have been conducted to evaluate the effectiveness of the proposed approach in comparison with the state-of-art methods and the results showed that significant improvements have been achieved in terms of quality and time overhead for large-scale applications. Graphical abstract: Image, graphical abstract