HEP Benchmark Suite：提升全球 LHC 计算基础设施的效率与可持续性
HEP Benchmark Suite: Enhancing Efficiency and Sustainability in Worldwide LHC Computing Infrastructures
AI总结：
本文提出 HEP Benchmark suite，通过整合 HEPScore23 及功耗等指标，统一测量全球 LHC 计算网格站点性能，有效建立资源利用标准化模型并定位异常，推动兼顾性能与环保的负责任计算实践。
摘要：
随着科学界不断突破计算能力的边界，应对随之而来的能源消耗和碳足迹的责任日益增加。这一责任延伸至全球 LHC 计算网格（WLCG），它涵盖了 40 个国家的 170 多个站点，为 LHC 实验提供关键的计算、磁盘、磁带存储和网络支持。确保这些多样化站点的高效运营实践至关重要，而不仅仅是关注性能指标。本文介绍了 HEP Benchmark suite，这是一个增强型套件，旨在使用 HEPScore23 作为性能单位，统一测量所有 WLCG 站点的计算资源性能。该套件不仅限于通过 HEPScore23 评估执行速度，还整合了机器负载、内存使用、内存交换以及特别是功耗等指标。其适应性和用户友好的界面使得在基准测试的同时能够全面获取系统相关数据。在 2023 年全年，该工具在多个 WLCG 站点进行了严格测试。重点是研究计算作业槽性能并将其与底层架构指标相关联。初步分析揭示了该工具在建立计算资源利用标准化模型方面的有效性，同时能够精准定位异常，这些异常通常源于站点配置错误。本文旨在阐明该工具的功能并展示广泛测试获得的结果。通过传播这些信息，旨在提高社区对该探测模型的认识，促进其更广泛的应用，并鼓励优先考虑性能和减轻环境影响的负责任计算实践。
英文摘要：
As the scientific community continues to push the boundaries of computing capabilities, there is a growing responsibility to address the associated energy consumption and carbon footprint. This responsibility extends to the Worldwide LHC Computing Grid (WLCG), encompassing over 170 sites in 40 countries, supporting vital computing, disk, tape storage and network for LHC experiments. Ensuring efficient operational practices across these diverse sites is crucial beyond mere performance metrics. This paper introduces the HEP Benchmark suite, an enhanced suite designed to measure computing resource performance uniformly across all WLCG sites, using HEPScore23 as performance unit. The suite expands beyond assessing only the execution speed via HEPScore23. In fact the suite incorporates metrics such as machine load, memory usage, memory swap, and notably, power consumption. Its adaptability and user-friendly interface enable comprehensive acquisition of system-related data alongside benchmarking. Throughout 2023, this tool underwent rigorous testing across numerous WLCG sites. The focus was on studying compute job slot performance and correlating these with fabric metrics. Initial analysis unveiled the tool's efficacy in establishing a standardized model for compute resource utilization while pinpointing anomalies, often stemming from site misconfigurations. This paper aims to elucidate the tool's functionality and present the results obtained from extensive testing. By disseminating this information, the objective is to raise awareness within the community about this probing model, fostering broader adoption and encouraging responsible computing practices that prioritize both performance and environmental impact mitigation.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
