基准委员会对新兴和未来计算基准的看法

Jianfeng Zhan
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引用次数: 0

摘要

计算机、管理或金融学科中的工件或对象的可测量属性是外在的,而不是内在的——依赖于它们的问题定义和解决方案实例。问题定义、解决方案实例化和度量的过程是相互纠缠的。只有在实例化之后,才能衡量问题的解决方案。定义、实例化和度量具有复杂的相互影响。同时,技术惯性带来了实例化偏差——被困在一个子空间甚至高维解空间中的一个点上。新兴计算加剧了这些令人生畏的挑战,使得计量学无法适用于基准社区。建立独立的科学与工程标杆迫在眉睫。本文提出了一个统一的基准定义,一个概念框架,以及一个可跟踪和监督的基于学习的基准测试方法,为基准科学和工程奠定了基础。我还讨论了BenchCouncil对新兴和未来计算的计划。正在进行的项目包括定义智能、本能、量子计算机、元宇宙、行星级计算机的挑战,以及重新制定数据中心、科学人工智能和CPU基准套件。此外,BenchCouncil将与ComputerCouncil合作,开发用于行星规模计算的开源计算机系统、用于科学系统的人工智能和元宇宙。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A BenchCouncil view on benchmarking emerging and future computing

The measurable properties of the artifacts or objects in the computer, management, or finance disciplines are extrinsic, not inherent — dependent on their problem definitions and solution instantiations. The processes of problem definition, solution instantiation, and measurement are entangled. Only after the instantiation can the solutions to the problem be measured. Definition, instantiation, and measurement have complex mutual influences. Meanwhile, the technology inertia brings instantiation bias — trapped into a subspace or even a point at a high-dimension solution space. These daunting challenges, which emerging computing aggravates, make metrology cannot work for benchmark communities. It is pressing to establish independent benchmark science and engineering.

This article presents a unifying benchmark definition, a conceptual framework, and a traceable and supervised learning-based benchmarking methodology, laying the foundation for benchmark science and engineering. I also discuss BenchCouncil’s plans for emerging and future computing. The ongoing projects include defining the challenges of intelligence, instinct, quantum computers, Metaverse, planet-scale computers, and reformulating data centers, artificial intelligence for science, and CPU benchmark suites. Also, BenchCouncil will collaborate with ComputerCouncil on open-source computer systems for planet-scale computing, AI for science systems, and Metaverse.

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