Year: 2025
Type: conference-paper
Abstract: With the advent of hundreds of cores on a chip to accelerate applications, the operating system (OS) needs to exploit the existing parallelism provided by the underlying hardware resources to determine the right amount of processes to be mapped on the multi-core systems. However, the existing OS is not scalable and is oblivious to applications. We address these issues by adopting a multi-layer network representation of the dynamic application-to-OS-to-architecture dependencies, namely the NetworkedOS. We adopt a compile-time analysis and construct a network representing the dependencies between dynamic instructions translated from the applications and the kernel and services. We propose an overlapping partitioning scheme to detect the clusters or processes that can potentially run in parallel to be mapped onto cores while reducing the number of messages transferred. At run time, processes are mapped onto the multi-core systems, taking into consideration the process affinity. Our experimental results indicate that NetworkedOS achieves performance improvement as high as 7.11x compared to Linux running on a 128-core system and 2.01x to Barrelfish running on a 64-core system.
Institution: University of Southern California
Language: English
Subfield: Hardware and Architecture
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Awards: CAREER: Embracing Complexity: A Fractal Calculus Approach to the Modeling and Optimization of Medical Cyber-Physical Systems, FMitF: A Novel Framework for Learning Formal Abstractions and Causal Relations from Temporal Behaviors, CENTER FOR COMPLEX PARTICLE SYSTEMS (COMPASS), NEW START GRANT AWARD TITLED, "NON-MARKOVIAN, ADVERSARIAL, AND MULTI-OBJECTIVE REINFORCEMENT LEARNING ALGORITHMS", CPS: Small: Uncertainty-aware Framework for Specifying, Designing and Verifying Cyber-Physical Systems, G6193981907, Collaborative Research: MODULUS: A Novel Spatiotemporal Multifractal Analysis to Evaluate Genome Dynamics, G7452299184
OpenAlex ID: W4408354824
