Publisher DOI: | 10.1109/ACCESS.2019.2937730 | Title: | DRACON : a dedicated hardware infrastructure for scalable run-time management on many-core systems | Language: | English | Authors: | Gregorek, Daniel Rust, Jochen Garcia-Ortiz, Alberto |
Keywords: | Computer architecture; dedicated hardware; dynamic run-time management; many-core | Issue Date: | 27-Aug-2019 | Publisher: | IEEE | Journal or Series Name: | IEEE access | Volume: | 7 | Startpage: | 121931 | Endpage: | 121948 | Abstract: | Many-core architectures integrate a large number of comparatively small processing cores into a single chip. However, the high degree of parallelism increases the run-time resource management complexity and overhead. The employment of dedicated hardware enhancements potentially enables a high quality of the resource management while management overhead is mitigated. To exploit the potential of hardware enhancements, we propose a dedicated infrastructure for run-time resource management on homogeneous MIMD many-core processors. For hardware enhanced resource management, a scalable and cluster-based system architecture is implemented. The resulting architecture (DRACON) utilizes message passing based communication, the dedicated infrastructure and hardware accelerators for resource management. A comprehensive evaluation for DRACON and reference architectures is performed using a transaction level simulation framework and dynamic task management as a use case. As benchmarks, synthetic models and task graph models of real-world applications are applied. The results reveal the limited scalability of classical architectures for resource management on many-cores. It is therefore necessary to apply cluster-based or moderately distributed architectures for many-core resource management. Further, the results demonstrate a significant performance improvement for the DRACON architecture at a number of hundreds of processing cores. Our evaluations show that DRACON generally outperforms software-only run-time management on many-core and achieves a performance improvement of up to 15.21% for single-program and more than 6% for mixed workloads. |
URI: | http://hdl.handle.net/20.500.12738/14168 | ISSN: | 2169-3536 | Review status: | This version was peer reviewed (peer review) | Institute: | Universität Bremen | Type: | Article |
Appears in Collections: | Publications without full text |
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