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Summarizing CPU and GPU Design Trends with Product Data

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arxiv 1911.11313 v2 pith:UD2MBEI2 submitted 2019-11-26 cs.DC

classification cs.DC
keywords lawsperformancecpusdatadesignscalingtransistorvalidity
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Moore's Law and Dennard Scaling have guided the semiconductor industry for the past few decades. Recently, both laws have faced validity challenges as transistor sizes approach the practical limits of physics. We are interested in testing the validity of these laws and reflect on the reasons responsible. In this work, we collect data of more than 4000 publicly-available CPU and GPU products. We find that transistor scaling remains critical in keeping the laws valid. However, architectural solutions have become increasingly important and will play a larger role in the future. We observe that GPUs consistently deliver higher performance than CPUs. GPU performance continues to rise because of increases in GPU frequency, improvements in the thermal design power (TDP), and growth in die size. But we also see the ratio of GPU to CPU performance moving closer to parity, thanks to new SIMD extensions on CPUs and increased CPU core counts.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. CarbonSet: A Dataset to Analyze Trends and Benchmark the Sustainability of CPUs and GPUs

    cs.AR 2025-06 conditional novelty 6.0 of 10

    CarbonSet provides lifecycle carbon footprint estimates for over 1,000 CPUs and GPUs and finds that datacenter GPU total CFP grew more than 50x from 2016 to 2023, driven by shipment growth.

  2. EcoServe: Designing Carbon-Aware AI Inference Systems

    cs.DC 2025-02 conditional novelty 6.0 of 10

    EcoServe combines four strategies (reuse, rightsize, reduce, recycle) in an ILP optimizer to cut modeled carbon emissions for LLM serving by up to 47% while keeping SLOs.

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