On-line soft error correction in matrix–matrix multiplication

Wu, Panruo; Ding, Chong; Chen, Longxiang; Davies, Teresa; Karlsson, Christer

doi:10.1016/j.jocs.2013.05.002

Cited by 19 publications

(18 citation statements)

References 9 publications

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“…Application-level fault tolerance mechanisms, such as algorithm-based fault tolerance [27], [28], [29], are extensively studied as a means to increase application resilience to transient faults on data objects. However, those mechanisms can come with big performance and energy overheads (e.g., 35% performance loss in [3]).…”

Section: Case Studymentioning

confidence: 99%

MOARD: Modeling Application Resilience to Transient Faults on Data Objects

Guo

Liu

2019

2019 IEEE International Parallel and Distributed Processing Symposium (IPDPS)

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Understanding application resilience (or error tolerance) in the presence of hardware transient faults on data objects is critical to ensure computing integrity and enable efficient application-level fault tolerance mechanisms. However, we lack a method and a tool to quantify application resilience to transient faults on data objects. The traditional method, random fault injection, cannot help, because of losing data semantics and insufficient information on how and where errors are tolerated. In this paper, we introduce a method and a tool (called "MOARD") to model and quantify application resilience to transient faults on data objects. Our method is based on systematically quantifying error masking events caused by application-inherent semantics and program constructs. We use MOARD to study how and why errors in data objects can be tolerated by the application. We demonstrate tangible benefits of using MOARD to direct a fault tolerance mechanism to protect data objects.

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Section: Case Studymentioning

confidence: 99%

MOARD: Modeling Application Resilience to Transient Faults on Data Objects

Guo

Liu

2019

2019 IEEE International Parallel and Distributed Processing Symposium (IPDPS)

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“…Existing techniques that can ensure reliability to SDCs comprise two categories: (i) algorithm-based fault tolerance 1 (ABFT)-i.e., methods using checksums specifically tailored to the algorithm under consideration-that can reliably detect (and possibly correct) up to a limited number of SDCs [13], [17], [19], [25], [39], [46], [47], [60]; (ii) systems with dual modular redundancy (DMR), where all non-coinciding SDCs can be detected if the same operation is duplicated in two separate processors (or threads) that cross-validate their results [21], but SDCs cannot be corrected without using triple modular redundancy (TMR) [23].…”

Section: A Summary Of Prior Workmentioning

confidence: 99%

“…All ABFT methods specifically tailored for GEMM computations [13], [17], [25], [60], [61] append the input subblocks with (redundant) checksum vectors (rows or columns), denoted by a c , b r in Figure 1(b) and highlighted in color.…”

Section: A Algorithm-based Fault Tolerancementioning

confidence: 99%

Mitigating Silent Data Corruptions in Integer Matrix Products: Toward Reliable Multimedia Computing on Unreliable Hardware

Anarado

Anam

Verdicchio

et al. 2017

IEEE Trans. Circuits Syst. Video Technol.

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The generic matrix multiply (GEMM) routine comprises the compute and memory-intensive part of many information retrieval, machine learning and object recognition systems that process integer inputs. Therefore, it is of paramount importance to ensure that integer GEMM computations remain robust to silent data corruptions (SDCs), which stem from accidental voltage or frequency overscaling, or other hardware non-idealities. In this paper, we introduce a new method for SDC mitigation based on the concept of numerical packing. The key difference between our approach and all existing methods is the production of redundant results within the numerical representation of the outputs, rather than as a separate set of checksums. Importantly, unlike well-known algorithm-based fault tolerance (ABFT) approaches for GEMM, the proposed approach can reliably detect the locations of the vast majority of all possible SDCs in the results of GEMM computations. An experimental investigation of voltage-scaled integer GEMM computations for visual descriptor matching within state-of-theart image and video retrieval algorithms running on an Intel i7-4578U 3GHz processor shows that SDC mitigation based on numerical packing leads to comparable or lower execution and energy-consumption overhead in comparison to all other alternatives.

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“…Some researchers went in another direction in order to tolerate more faults effectively. Realizing that the offline approach taken by the traditional ABFT techniques have to face catastrophic error propagation at the end, researchers attempted to adapt checksum schemes for online error detection and correction [5,25,24]. The idea is that online ABFT catches errors early on when they are not propagated far away, therefore making it easier to correct.…”

Section: Related Workmentioning

confidence: 99%

“…The fault model is a deciding factor in the design of ABFT codes and adaption to the associated algorithm. However the fault models used in existing ABFT research are either too abstract [8,14,10] or too simplistic [5,24,25] limiting their use where the architectural fault models do not fit. In this work we rethink the fault model and explore the challenges if we use a comprehensive architectural fault model that allows both logic/arithmetic faults and storage faults in main memory, on-chip memory, and other datapaths.…”

Section: Introductionmentioning

confidence: 99%

Towards Practical Algorithm Based Fault Tolerance in Dense Linear Algebra

Guan

DeBardeleben

et al. 2016

Proceedings of the 25th ACM International Symposium on High-Performance Parallel and Distributed Computing

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Algorithm based fault tolerance (ABFT) attracts renewed interest for its extremely low overhead and good scalability. However the fault model used to design ABFT has been either abstract, simplistic, or both, leaving a gap between what occurs at the architecture level and what the algorithm expects. As the fault model is the deciding factor in choosing an effective checksum scheme, the resulting ABFT techniques have seen limited impact in practice. In this paper we seek to close the gap by directly using a comprehensive architectural fault model and devise a comprehensive ABFT scheme that can tolerate multiple architectural faults of various kinds. We implement the new ABFT scheme into high performance linpack (HPL) to demonstrate the feasibility in large scale high performance benchmark. We conduct architectural fault injection experiments and large scale experiments to empirically validate its fault tolerance and demonstrate the overhead of error handling, respectively.

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On-line soft error correction in matrix–matrix multiplication

Cited by 19 publications

References 9 publications

MOARD: Modeling Application Resilience to Transient Faults on Data Objects

MOARD: Modeling Application Resilience to Transient Faults on Data Objects

Mitigating Silent Data Corruptions in Integer Matrix Products: Toward Reliable Multimedia Computing on Unreliable Hardware

Towards Practical Algorithm Based Fault Tolerance in Dense Linear Algebra

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