system software

Traditional operating systems suffer from poor scalability on many-core processors and low parallel efficiency due to their inability to perceive application semantics. To address this, we propose NetworkedOS—a novel application-aware, networked OS architecture. Our approach leverages compile-time dynamic instruction dependency analysis to construct a multi-layer network model that explicitly captures runtime dependencies among applications, the kernel, and hardware. We further design an overlapping graph partitioning algorithm to jointly optimize parallel execution and inter-core communication overhead, and implement a runtime process affinity mapping scheduler. Crucially, NetworkedOS breaks the conventional “black-box” OS assumption regarding application semantics for the first time. Experimental evaluation shows that NetworkedOS achieves a 7.11× speedup over Linux on a 128-core system and a 2.01× improvement over Barrelfish on a 64-core system, significantly enhancing scalability and resource utilization under large-scale parallel workloads.

To address critical challenges in SoC design—including ambiguous system-level modeling semantics, poor interoperability across heterogeneous computational models (e.g., dataflow and neural networks), and the decoupling of design-space exploration from verification—this paper proposes a co-communication mechanism ensuring semantic consistency across multiple models. The approach establishes an integrated toolchain supporting system-level modeling, simulation-driven verification, hardware-software co-design space exploration, and joint power-performance analysis. Innovatively, it unifies dataflow modeling with system-level abstractions to enable functional correctness verification and quantitative energy-efficiency evaluation for representative applications such as video processing and AI acceleration. Experimental results demonstrate that the methodology significantly improves early-stage SoC design iteration efficiency and enhances the reliability of architectural decision-making.

To address poor reproducibility, low build efficiency, and insufficient deployment automation in embedded Linux system customization, this paper proposes a three-layer extensible architecture based on the Yocto Project. The architecture integrates GitLab CI and Docker to ensure environment isolation and enable continuous integration and deployment (CI/CD), while incorporating a local hash server (hashserv) and shared sstate cache server to significantly improve build artifact reuse. It supports automated real-time Linux kernel builds, QEMU-based simulation testing, and validation across six distinct boot scenarios. Experimental evaluation demonstrates substantial reduction in build time, markedly enhanced system stability and build reproducibility, and strong scalability and engineering deployability for industrial-grade applications.

This work addresses the challenges in edge and embedded application development—namely, heterogeneous software stacks, multi-language runtimes, and difficult debugging—which lead to rigid deployment workflows and complex fault diagnosis. To overcome these limitations, the paper proposes a novel architecture enabling unified end-edge-cloud development. Its core components include a single programming language, a retargetable runtime system, a local recording and replay mechanism for distributed events, and a cross-platform deployment framework. This design breaks down traditional debugging barriers in edge–cloud collaborative development, facilitating seamless scalability, consistent testing, and flexible deployment across heterogeneous environments. Evaluation of the prototype system demonstrates that the proposed approach significantly simplifies deployment procedures and enhances fault diagnosis efficiency.

This work addresses the lack of a unified evaluation benchmark for software package build repair across heterogeneous instruction set architectures (ISAs) and programming languages. To this end, we present the first standardized benchmark comprising 268 real-world build failure cases spanning multiple ISAs and languages, along with a systematic evaluation protocol. Leveraging this benchmark, we conduct a comprehensive assessment of six state-of-the-art large language models, revealing their limited effectiveness in cross-ISA build repair tasks. Our findings underscore significant shortcomings in current approaches and highlight the technical challenges inherent in this domain. This study establishes a reliable data foundation and evaluation framework to support future research in automated build repair across diverse computational environments.

This work addresses the persistent challenge of inconsistent development and execution environments faced by researchers operating across heterogeneous computing platforms—ranging from laptops and workstations to supercomputers and cloud infrastructures. To overcome this, the authors propose a modular and portable software ecosystem featuring a unified command-line interface that enables seamless orchestration and execution of scientific workflows. The system ensures cross-platform consistency, reproducibility, and scalability, thereby streamlining computational research across diverse hardware configurations. Its practical efficacy has been demonstrated through successful integration into the plan4res project under the European Union’s Horizon 2020 initiative, where it effectively supported complex, large-scale scientific workflows in varied computing environments.

This work addresses the inefficiencies and semantic inconsistencies arising from separately implementing driver and monitor programs in traditional hardware module testing. To overcome this, the authors propose a domain-specific language (DSL) tailored to hardware communication protocols, which enables the unified specification of both driver and monitor logic through an imperative syntax, thereby ensuring their semantic consistency for the first time. Building upon this DSL, they develop a prototype tool that leverages waveform parsing and transaction-level trace inference techniques to accurately reconstruct protocol-compliant transaction sequences from raw signal waveforms. Experimental results demonstrate that the approach significantly improves development efficiency, with further validation planned on real-world interconnect protocols such as Wishbone and AXI-Stream.

This study addresses the underexplored phenomenon of software aging in GPU-accelerated large language model (LLM) services, particularly concerning memory leakage under heterogeneous software-hardware stacks and dynamic workloads. It pioneers the extension of software aging research into GPU-based LLM serving by conducting 216-hour stress tests across six co-deployment configurations, simultaneously monitoring multi-dimensional metrics from the host, GPU, and client perspectives. Employing time-series statistical analysis with autocorrelation correction and multiple hypothesis testing, the work systematically characterizes aging patterns. Significant memory aging is consistently observed across all configurations, revealing that leakage rates are highly sensitive to runtime environments and deployment settings. These findings confirm the prevalence and quantifiability of the issue and establish a reproducible framework bridging software aging and LLM service research.

This study addresses the absence of open standards for CPU pipeline visualization tools and the difficulty in localizing performance bottlenecks. To this end, it proposes an open-source event stream format alongside Catscan, an interactive viewer. Methodologically, this work introduces a structured event stream based on transactional relationships, integrating typed event modeling, persistent highlighting techniques, and domain-specific search algorithms to enable microarchitectural trace analysis from symptoms down to individual instructions. Furthermore, it supports resource-oriented views synchronized with comparative trace alignment. By successfully reproducing industry-grade debugging workflows, this project provides the community with production-validated microarchitectural visualization infrastructure.

This study addresses the lack of systematic guidance for enterprise software teams in choosing between monolithic and microservices architectures. The work proposes a decision-making framework that integrates technical and organizational factors, evaluating the trade-offs of each architecture across dimensions such as scalability, reliability, deployment efficiency, and organizational complexity. The assessment is grounded in system scale, business requirements, operational maturity, and long-term maintainability. Through architectural pattern analysis, a structured evaluation model, and multiple case studies, the authors develop a practical selection methodology tailored to real-world engineering contexts. This approach offers enterprises clear architectural evolution pathways and actionable guidelines aligned with their developmental stages, thereby significantly enhancing the rationality and sustainability of system design decisions.