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ApproxDup: Developing an Approximate Instruction Duplication Mechanism for Efficient SDC Detection in GPGPUs

文献类型：期刊论文

作者Wei, Xiaohui3; Jiang, Nan3; Yue, Hengshan3; Wang, Xiaonan3; Zhao, Jianpeng3; Li, Guangli2; Qiu, Meikang1
刊名IEEE TRANSACTIONS ON COMPUTER-AIDED DESIGN OF INTEGRATED CIRCUITS AND SYSTEMS
出版日期2024-04-01
卷号43期号:4页码:1051-1064
关键词Instruction sets Reliability Resilience Circuit faults Registers Kernel Graphics processing units Approximate computing GPGPUs instruction duplication silent data corruptions (SDCs) soft error
ISSN号0278-0070
DOI10.1109/TCAD.2023.3330821
英文摘要Nowadays, selective instruction duplication (SelDup) is the typical approach to detect silent data corruption (SDC) in GPGPU. However, owing to the up-to-billions fault sites of parallel GPGPU kernel functions, it usually introduces tremendous overhead to perform fault injections (FIs) for obtaining the duplication-candidate instruction set (although can be conducted in parallel). Moreover, current SelDup typically considers all SDCs severe and tends to duplicate more instructions. The nontrivial duplication overhead seriously restricts the deployment of current SelDup on resource-constrained systems (e.g., embedded GPGPUs). To address the above challenges, this article proposes an approximate instruction duplication (ApproxDup) mechanism for efficient SDC detection in GPGPUs. First, to replace the expensive FI-based duplication-candidate instructions identified method, we drive out a machine learning (ML)-based model (SDC-predictor) for instructionwise SDC proneness and severity estimation. Our key insight is that instruction type/functionality and instruction dependency set can efficaciously characterize the instructionwise SDC proneness in GPGPUs. In contrast, the instruction's original data magnitude, fault propagation range, and error detected features can distinguish its SDC severity. Second, incorporating the concept of approximate computing, we propose ApproxDup that preferentially duplicates severe-SDC-prone instructions while relaxing the detection of minor/detectable SDCs for traditional SelDup overhead reduction. Experimental results exhibit that ApproxDup can cover 92.51% of severe SDCs while merely increasing 38% of dynamic instructions, which achieves a better tradeoff between reliability and performance compared with the state-of-the-art SelDup. Furthermore, we discuss the effectiveness of the proposed method on different ML models/applications/GPGPU architectures.
资助项目National Natural Science Foundation of China (NSFC)
WOS研究方向Computer Science ; Engineering
语种英语
WOS记录号WOS:001188376700021
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
源URL[http://119.78.100.204/handle/2XEOYT63/38800]  
专题中国科学院计算技术研究所期刊论文_英文
通讯作者Yue, Hengshan
作者单位1.Augusta Univ, Sch Comp & Cyber Sci, Augusta, GA 30912 USA
2.Chinese Acad Sci, Inst Comp Technol, Beijing 100190, Peoples R China
3.Jilin Univ, Coll Comp Sci & Technol, Changchun 130012, Jilin, Peoples R China
推荐引用方式
GB/T 7714		 Wei, Xiaohui,Jiang, Nan,Yue, Hengshan,et al. ApproxDup: Developing an Approximate Instruction Duplication Mechanism for Efficient SDC Detection in GPGPUs[J]. IEEE TRANSACTIONS ON COMPUTER-AIDED DESIGN OF INTEGRATED CIRCUITS AND SYSTEMS,2024,43(4):1051-1064.
APA Wei, Xiaohui.,Jiang, Nan.,Yue, Hengshan.,Wang, Xiaonan.,Zhao, Jianpeng.,...&Qiu, Meikang.(2024).ApproxDup: Developing an Approximate Instruction Duplication Mechanism for Efficient SDC Detection in GPGPUs.IEEE TRANSACTIONS ON COMPUTER-AIDED DESIGN OF INTEGRATED CIRCUITS AND SYSTEMS,43(4),1051-1064.
MLA Wei, Xiaohui,et al."ApproxDup: Developing an Approximate Instruction Duplication Mechanism for Efficient SDC Detection in GPGPUs".IEEE TRANSACTIONS ON COMPUTER-AIDED DESIGN OF INTEGRATED CIRCUITS AND SYSTEMS 43.4(2024):1051-1064.

入库方式： OAI收割

来源：计算技术研究所

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