A Fault-Model-Relevant Classification of Consensus Mechanisms for MPI and HPC

Large-scale HPC systems experience failures arising from faults in hardware, software, and/or networking. Failure rates continue to grow as systems scale up and out. Crash fault tolerance has up to now been the focus when considering means to augment the Message Passing Interface (MPI) for fault-tol...

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Vydáno v:	International journal of parallel programming Ročník 51; číslo 2-3; s. 128 - 149
Hlavní autoři:	Nansamba, Grace, Altarawneh, Amani, Skjellum, Anthony
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	New York Springer US 01.06.2023 Springer Nature B.V
Témata:	Classification Computer Science Crashes Failure rates Fault detection Fault tolerance Faults Message passing Processor Architectures Software Engineering/Programming and Operating Systems Special Issue on High-Level Parallel Programming and Applications (HLPP 2022) Synchronism System failures Theory of Computation Consensus mechanisms Fault tolerance Replication Fault detection Synchronization Message-passing model
ISSN:	0885-7458, 1573-7640
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Abstract	Large-scale HPC systems experience failures arising from faults in hardware, software, and/or networking. Failure rates continue to grow as systems scale up and out. Crash fault tolerance has up to now been the focus when considering means to augment the Message Passing Interface (MPI) for fault-tolerant operations. This narrow model of faults (usually restricted only to process or node failures) is insufficient. Without a more general model for consensus, gaps in the ability to detect, isolate, mitigate, and recover HPC applications efficiently will arise. Focusing on crash failures is insufficient because a chain of underlying components may lead to system failures in MPI. What is more, clusters and leadership-class machines alike often have Reliability, Availability, and Serviceability Systems to convey predictive and real-time fault and error information, which does not map strictly to process and node crashes. A broader study of failures beyond crash failures in MPI will thus be useful in conjunction with consensus mechanism for developers as they continue to design, develop, and implement fault-tolerant HPC systems that reflect observable faults in actual systems. We describe key factors that must be considered during consensus-mechanism design. We illustrate some of the current MPI fault tolerance models based on use cases. We offer a novel classification of common consensus mechanisms based on these factors such as the network model, failure types, and based on use cases (e.g., fault detection, synchronization) of the consensus in the computation process, including crash fault tolerance as one category.
AbstractList	Large-scale HPC systems experience failures arising from faults in hardware, software, and/or networking. Failure rates continue to grow as systems scale up and out. Crash fault tolerance has up to now been the focus when considering means to augment the Message Passing Interface (MPI) for fault-tolerant operations. This narrow model of faults (usually restricted only to process or node failures) is insufficient. Without a more general model for consensus, gaps in the ability to detect, isolate, mitigate, and recover HPC applications efficiently will arise. Focusing on crash failures is insufficient because a chain of underlying components may lead to system failures in MPI. What is more, clusters and leadership-class machines alike often have Reliability, Availability, and Serviceability Systems to convey predictive and real-time fault and error information, which does not map strictly to process and node crashes. A broader study of failures beyond crash failures in MPI will thus be useful in conjunction with consensus mechanism for developers as they continue to design, develop, and implement fault-tolerant HPC systems that reflect observable faults in actual systems. We describe key factors that must be considered during consensus-mechanism design. We illustrate some of the current MPI fault tolerance models based on use cases. We offer a novel classification of common consensus mechanisms based on these factors such as the network model, failure types, and based on use cases (e.g., fault detection, synchronization) of the consensus in the computation process, including crash fault tolerance as one category.
Author	Skjellum, Anthony Altarawneh, Amani Nansamba, Grace
Author_xml	– sequence: 1 givenname: Grace surname: Nansamba fullname: Nansamba, Grace email: jpp751@mocs.utc.edu organization: University of Tennessee at Chattanooga – sequence: 2 givenname: Amani surname: Altarawneh fullname: Altarawneh, Amani organization: Colorado State University – sequence: 3 givenname: Anthony surname: Skjellum fullname: Skjellum, Anthony organization: University of Tennessee at Chattanooga
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Snippet	Large-scale HPC systems experience failures arising from faults in hardware, software, and/or networking. Failure rates continue to grow as systems scale up...
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StartPage	128
SubjectTerms	Classification Computer Science Crashes Failure rates Fault detection Fault tolerance Faults Message passing Processor Architectures Software Engineering/Programming and Operating Systems Special Issue on High-Level Parallel Programming and Applications (HLPP 2022) Synchronism System failures Theory of Computation
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Title	A Fault-Model-Relevant Classification of Consensus Mechanisms for MPI and HPC
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