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GPUVolt: modeling and characterizing voltage noise in GPU architectures

Published: 11 August 2014 Publication History

Abstract

Voltage noise is a major obstacle in improving processor energy efficiency because it necessitates large operating voltage guardbands that increase overall power consumption and limit peak performance. Identifying the leading root causes of voltage noise is essential to minimize the unnecessary guardband and maximize the overall energy efficiency. We provide the first-ever modeling and characterization of voltage noise in GPUs based on a new simulation infrastructure called GPUVolt. Using it, we identify the key intracore microarchitectural components (e.g., the register file and special functional units) that significantly impact the GPU's voltage noise. We also demonstrate that intercore-aligned microarchitectural activity detrimentally impacts the chip- wide worst-case voltage droops. On the basis of these findings, we propose a combined register-file and execution-unit throttling mechanism that smooths GPU voltage noise and reduces the guardband requirement by as much as 29%.

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cover image ACM Conferences
ISLPED '14: Proceedings of the 2014 international symposium on Low power electronics and design
August 2014
398 pages
ISBN:9781450329750
DOI:10.1145/2627369
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 11 August 2014

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Author Tags

  1. di/dt
  2. gpu architecture
  3. gpu reliability
  4. inductive noise

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ISLPED '14 Paper Acceptance Rate 63 of 184 submissions, 34%;
Overall Acceptance Rate 398 of 1,159 submissions, 34%

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