system performance

Modern software systems frequently exhibit application-level resource contention bottlenecks—such as blocking on custom application events—that evade detection by conventional performance profilers due to complex dependencies and bespoke resource management. To address this, we propose OmniResource Profiling, the first method to jointly leverage system-level metrics and application-level event-waiting relationships. It employs a lightweight LLM-assisted static analysis to automatically identify custom resources and cross-execution-trace runtime variable comparison for precise root-cause localization. Evaluated on 12 known performance issues across five real-world applications, OmniResource achieves 100% diagnostic accuracy and uncovers two previously undetected bottlenecks. Crucially, it requires no intrusive instrumentation, balancing high precision with practical deployability. This work delivers the first end-to-end solution for application-level resource contention analysis.

The high-performance computing (HPC) domain suffers from an abundance of benchmarking tools and the absence of a standardized, unified classification framework. Method: This paper proposes the first standardized benchmark taxonomy for HPC, derived from a systematic literature review and multi-dimensional feature analysis across hardware, software, and algorithmic layers. A structured classification model is constructed, with key attributes—including target workload, portability, scalability, and measurement granularity—concisely tabulated. An interactive web-based platform is further developed to enable dynamic, dimension-driven querying, cross-benchmark comparison, and visual analytics. Contribution/Results: The taxonomy systematically organizes over 100 mainstream HPC benchmarks, significantly enhancing efficiency and consistency for architects, researchers, and scientific users in system evaluation, benchmark selection, and performance optimization. It establishes a foundational framework for standardizing HPC performance assessment and facilitates reproducible, comparable, and interpretable benchmarking practices.

To address the challenges of late detection of performance degradation and delayed scalability analysis in HPC application development, this paper proposes a lightweight, CI-native continuous performance monitoring framework. The method integrates TALP-based real-time performance instrumentation with in-repository regression analysis, enabling low-overhead (no additional tracing) and high-temporal-fidelity monitoring—delivering feedback immediately upon CI build completion. Performance data are stored in a CI-friendly directory structure, and automated HTML reports are generated, visualizing trends in key performance factors as well as strong and weak scaling efficiency. Evaluated in the GENE-X CI environment with zero code modification, the framework demonstrates sensitivity to minute performance improvements (<2%) and reduces post-processing overhead by over 90% compared to conventional tracing tools. This significantly enhances the efficiency and practicality of scalability assessment under resource-constrained conditions.

This work addresses the lack of existing tools capable of continuous performance validation and regression detection across entire datacenter clusters. The authors propose the first cluster-wide continuous benchmarking framework that supports unified scheduling, enabling simultaneous task distribution to all nodes and systematic collection of multidimensional performance metrics—spanning CPU, GPU, memory, interconnects, I/O, power consumption, frequency, and temperature—across both space and time. This framework facilitates performance regression detection under software and hardware changes as well as analysis of hardware variability. Experiments on the NHR@FAU cluster reveal intra-node performance variations below 1% among identically configured nodes, while inter-node differences reach up to 5%. The study further uncovers, for the first time, significant disparities in the performance–power relationship between air-cooled and liquid-cooled nodes.

This work addresses the limitations of existing CPU benchmarks in accurately evaluating the performance of modern heterogeneous, multithreaded processors under diverse workloads. To this end, the authors present the SPEC CPU 2026 benchmark suite, developed through community collaboration and principled methodology, which introduces the Rolling-Round-Robin Rate approach to standardize the execution of heterogeneous multiprogrammed workloads. The suite incorporates newly designed multithreaded benchmarks exhibiting varied microarchitectural characteristics, selected and hardened through an open-source application curation process. Emphasizing workload diversity, portability, and long-term viability, SPEC CPU 2026 establishes a robust, representative, and authoritative standard for performance evaluation, thereby supporting next-generation computer architecture research.

This work addresses the limitations of traditional high-performance computing (HPC), which relies on manual task scripting and scheduling and struggles to meet the automation demands of complex scientific workflows. The authors propose the first large language model–based autonomous agent framework that enables end-to-end automated execution of HPC workflows from descriptive instructions. The framework integrates Slurm/Flux job schedulers, low-latency AWS cloud infrastructure, and event monitoring mechanisms to support task definition, optimization, and scheduling. Experimental results demonstrate that the system efficiently deploys scalable experiments, accurately translates job specifications—with only occasional deviations in processor affinity—and successfully reproduces an expert-level variant calling pipeline, achieving consistent results in 18 out of 19 runs. These findings validate the framework’s feasibility and effectiveness in real-world HPC environments.

To address the low throughput, high latency, and weak security coupling of HPC-oriented proxy-based RESTful APIs under intensive I/O workloads, this paper proposes a novel API architecture tailored for high-performance computing. Methodologically, we introduce the first end-to-end performance modeling and bottleneck attribution framework; deeply integrate security mechanisms (JWT/OAuth 2.1) into stateless service design—rather than applying them as post-hoc hardening—and implement asynchronous I/O, zero-copy data transfer, and load-aware routing in Rust. Our contributions include a 100× throughput improvement and reduction of P99 latency to the millisecond level; rigorous independent peer review; and production deployment across multiple European supercomputing centers, supporting scientific workflows with over one thousand concurrent clients.

This work addresses the limitations of existing performance evaluation approaches for distributed computing continua, which often focus on a single dimension and fail to holistically characterize the behavior of cross-layer heterogeneous systems. The paper presents the first systematic framework that establishes a comprehensive taxonomy of performance metrics spanning three layers—computation, networking, and application/user—as well as emerging non-functional attributes such as sustainability and observability. By integrating mathematical modeling with cross-layer analysis, the study rigorously defines the applicability, measurement phases, and specifications for each metric category. The resulting framework is both clearly structured and extensible, offering a solid theoretical foundation and practical guidance for unified performance assessment in dynamic, heterogeneous environments.