Silent Data Corruptions: Microarchitectural Perspectives

Today more than ever before, academia, manufacturers, and hyperscalers acknowledge the major challenge of silent data corruptions (SDCs) and aim on solutions to minimize its impact by avoiding, detecting, and mitigating SDCs. Recent studies on large scale datacenters conducted by Meta and Google rep...

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Bibliographic Details
Published in:IEEE transactions on computers 2023-11, Vol.72 (11), p.1-13
Main Authors: Papadimitriou, George, Gizopoulos, Dimitris
Format: Article
Language:English
Subjects:
Circuit faults
Computer bugs
Data centers
Error correction codes
errors
fault injection
faults
Hardware
Microarchitecture
microprocessor
Microprocessors
Redundancy
Silent data corruptions
Software
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Summary:Today more than ever before, academia, manufacturers, and hyperscalers acknowledge the major challenge of silent data corruptions (SDCs) and aim on solutions to minimize its impact by avoiding, detecting, and mitigating SDCs. Recent studies on large scale datacenters conducted by Meta and Google report an unexpected rate of silent data corruption incidents that are attributed to modern microprocessor generations. Despite the acknowledged severity of the phenomenon, particularly at the datacenter scale, there is no in-depth analysis of the microarchitectural locations in a complex microprocessor that are more likely to generate an SDC at the program outputs. In this paper, we present a detailed analysis of the faulty behavior of many critical microarchitectural structures of a modern out-of-order microprocessor generating silent data corruptions. Our analysis unveils several observations, including: (i) the magnitude of silent data corruptions attributed to different hardware structures, (ii) the instruction-related parameters that are more likely to result in a silent data corruption, (iii) the extent to which the operating system affects the silent data corruption occurrences, and (iv) the byte positions of a word which are more likely to result in silent data corruptions. Collectively, such findings can assist decisions for hardware and software schemes for the reduction of the likelihood of silent data corruptions generation.
ISSN:0018-9340
1557-9956
DOI:10.1109/TC.2023.3285094