结果可扩展性:随着HPC选择的社会影响的演变
Sally Ellingson1, Guillaume Pallez2
1University of Kentucky.
The international journal of high performance computing applications
|August 18, 2025
概括
我们提出了新的指标来衡量高性能计算 (HPC) 系统如何促进社会进步,重点是计算输出而不是输入. 这些动态,社区驱动的指标评估社会影响,并促进机器多样性.
科学领域:
- 计算科学 计算科学
- 社会影响评估的社会影响评估
- 高性能计算 (HPC) 是一种高性能计算.
背景情况:
- 传统的高性能计算 (HPC) 性能指标,如TOP500,HPCG,Graph500和IO500,并没有量化社会贡献.
- 现有的指标侧重于计算输入 (例如,HPL性能),而不是从HPC获得的有形社会效益.
研究的目的:
- 引入新的指标来评估高性能计算 (HPC) 系统所实现的社会进步.
- 将重点从计算输入转移到实际的社会输出和通过HPC取得的进步.
- 通过考虑尺寸和结果的帕雷托前面来促进HPC架构的多样性.
主要方法:
- 制定一个新的框架,跟踪HPC系统和研究在应对社会挑战方面的增长.
- 实施衡量HPC计算产生的具体社会进步的指标.
- 分析HPC系统的大小与所取得的社会成果之间形成的帕雷托阵线.
主要成果:
- 展示了一种新的方法来衡量高性能计算 (HPC) 的社会影响.
- 强调了当前以输入为中心的指标在捕捉社会贡献方面的局限性.
- 展示了新的指标如何评估HPC能力和社会进步.
结论:
- 目前的HPC性能指标不足以评估社会进步.
- 需要新的,以输出为中心的和动态的指标来衡量HPC的社会贡献.
- 社区驱动的指标对于适应不断变化的社会需求和最大限度地提高HPC的积极影响至关重要.
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