Understanding the Future of Energy Efficiency in Multi-Module GPUs

A. Arunkumar, Evgeny Bolotin, D. Nellans, Carole-Jean Wu
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引用次数: 24

Abstract

As Moore’s law slows down, GPUs must pivot towards multi-module designs to continue scaling performance at historical rates. Prior work on multi-module GPUs has focused on performance, while largely ignoring the issue of energy efficiency. In this work, we propose a new metric for GPU efficiency called EDP Scaling Efficiency that quantifies the effects of both strong performance scaling and overall energy efficiency in these designs. To enable this analysis, we develop a novel top-down GPU energy estimation framework that is accurate within 10% of a recent GPU design. Being decoupled from granular GPU microarchitectural details, the framework is appropriate for energy efficiency studies in future GPUs. Using this model in conjunction with performance simulation, we show that the dominating factor influencing the energy efficiency of GPUs over the next decade is GPUmodule (GPM) idle time. Furthermore, neither inter-module interconnect energy, nor GPM microarchitectural design is expected to play a key role in this regard. We demonstrate that multi-module GPUs are on a trajectory to become 2⇥ less energy efficient than current monolithic designs; a significant issue for data centers which are already energy constrained. Finally, we show that architects must be willing to spend more (not less) energy to enable higher bandwidth inter-GPM connections, because counter-intuitively, this additional energy expenditure can reduce total GPU energy consumption by as much as 45%, providing a path to energy efficient strong scaling in the future.
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了解未来多模块gpu的能源效率
随着摩尔定律的放缓,gpu必须转向多模块设计,以继续以历史速度扩展性能。先前多模块gpu的工作主要集中在性能上,而在很大程度上忽略了能源效率的问题。在这项工作中,我们提出了一个新的GPU效率指标,称为EDP缩放效率,量化了这些设计中强大的性能缩放和整体能源效率的影响。为了实现这种分析,我们开发了一种新颖的自上而下的GPU能量估计框架,其精度在最新GPU设计的10%以内。该框架与粒度粒度的GPU微架构细节解耦,适用于未来GPU的能效研究。将此模型与性能仿真相结合,我们表明在未来十年影响gpu能效的主要因素是gpu模块(GPM)空闲时间。此外,无论是模块间互连能源,还是GPM微架构设计,预计都不会在这方面发挥关键作用。我们证明,多模块gpu的能效将比目前的单片设计低2 × 2;对于已经受到能源限制的数据中心来说,这是一个重大问题。最后,我们表明架构师必须愿意花费更多(而不是更少)的能量来实现更高带宽的gpm之间的连接,因为与直觉相反,这种额外的能量消耗可以将GPU的总能耗降低多达45%,为未来的节能强扩展提供了一条途径。
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