ARDiS: A Portable and Unified Resource Management Framework in Real Hardware Systems

Designing efficient RM strategies is a cornerstone of modern computing, driving innovations in performance optimization, energy efficiency, and security. While simulators have long been the go-to tools for RM research, they fail to balance accuracy and practicality: high-fidelity simulators are excruciatingly slow, and low-fidelity ones compromise on reliability. Real hardware offers unparalleled precision and accuracy but remains underutilized due to significant barriers, including fragmented implementations, lack of portability, and prohibitive development overhead. We present ARDiS, the first open-source 1 and portable framework to provide a unified, architecture-agnostic platform for running system-level resource management (RM) techniques directly on real hardware. ARDiS eliminates the need to “reinvent the wheel,” enabling researchers to design, implement, and evaluate sophisticated RM strategies—including machine learning-based approaches—with minimal effort and maximum reproducibility. To demonstrate its versatility, we evaluate ARDiS on two real-world hardware platforms: a server-grade heterogeneous processor (Intel i9-12900) and a resource-constrained embedded system (NVIDIA Jetson TX2). Through extensive experimentation, we validate the ability of ARDiS to deliver accurate, scalable, and reproducible results across diverse platforms and application domains. By lowering the barriers to hardware-based RM research, ARDiS empowers the design automation community to explore new frontiers in system-level optimization and innovation.

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Publication Details

Journal
ACM Transactions on Design Automation of Electronic Systems
Published
2026-01-26
DOI
https://doi.org/10.1145/3793861
Primary Topic
Parallel Computing and Optimization Techniques
Type
article
Field-Weighted Citation Impact
0.00
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article

ARDiS: A Portable and Unified Resource Management Framework in Real Hardware Systems

Heba Khdr, Mohammed Bakr Sikal, Jeferson González-Gómez, Joerg Henkel et al.
ACM Transactions on Design Automation of Electronic Systems
Parallel Computing and Optimization Techniques
article

ARDiS: A Portable and Unified Resource Management Framework in Real Hardware Systems

Heba Khdr, Mohammed Bakr Sikal, Jeferson González-Gómez, Joerg Henkel, Andreas Noebel
article en

Abstract

Designing efficient RM strategies is a cornerstone of modern computing, driving innovations in performance optimization, energy efficiency, and security. While simulators have long been the go-to tools for RM research, they fail to balance accuracy and practicality: high-fidelity simulators are excruciatingly slow, and low-fidelity ones compromise on reliability. Real hardware offers unparalleled precision and accuracy but remains underutilized due to significant barriers, including fragmented implementations, lack of portability, and prohibitive development overhead. We present ARDiS, the first open-source 1 and portable framework to provide a unified, architecture-agnostic platform for running system-level resource management (RM) techniques directly on real hardware. ARDiS eliminates the need to “reinvent the wheel,” enabling researchers to design, implement, and evaluate sophisticated RM strategies—including machine learning-based approaches—with minimal effort and maximum reproducibility. To demonstrate its versatility, we evaluate ARDiS on two real-world hardware platforms: a server-grade heterogeneous processor (Intel i9-12900) and a resource-constrained embedded system (NVIDIA Jetson TX2). Through extensive experimentation, we validate the ability of ARDiS to deliver accurate, scalable, and reproducible results across diverse platforms and application domains. By lowering the barriers to hardware-based RM research, ARDiS empowers the design automation community to explore new frontiers in system-level optimization and innovation.

ACM Transactions on Design Automation of Electronic SystemsVol. 31(4)
Karlsruhe Institute of Technology (DE), Instituto Tecnológico de Costa Rica (CR)
Openalex Percentile: Top 86%
Parallel Computing and Optimization Techniques
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