Formal Definitions and Performance Comparison of Consistency Models for Parallel File Systems

The semantics of HPC storage systems are defined by the consistency models to which they abide. Storage consistency models have been less studied than their counterparts in memory systems, with the exception of the POSIX standard and its strict consistency model. The use of POSIX consistency imposes...

Celý popis

Uložené v:
Podrobná bibliografia
Vydané v:IEEE transactions on parallel and distributed systems Ročník 35; číslo 6; s. 1092 - 1106
Hlavní autori: Wang, Chen, Mohror, Kathryn, Snir, Marc
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: New York IEEE 01.06.2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:
Access time
Computational modeling
Consistency
Consistency model
Deep learning
Load modeling
MATHEMATICS AND COMPUTING
parallel file system
parallel i/o
Performance evaluation
Program processors
Semantics
storage consistency
Storage systems
Synchronization
Workload
Workloads
Writing
ISSN:1045-9219, 1558-2183
On-line prístup:Získať plný text
Tagy: Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
Popis
Shrnutí:The semantics of HPC storage systems are defined by the consistency models to which they abide. Storage consistency models have been less studied than their counterparts in memory systems, with the exception of the POSIX standard and its strict consistency model. The use of POSIX consistency imposes a performance penalty that becomes more significant as the scale of parallel file systems increases and the access time to storage devices, such as node-local solid storage devices, decreases. While some efforts have been made to adopt relaxed storage consistency models, these models are often defined informally and ambiguously as by-products of a particular implementation. In this work, we establish a connection between memory consistency models and storage consistency models and revisit the key design choices of storage consistency models from a high-level perspective. Further, we propose a formal and unified framework for defining storage consistency models and a layered implementation that can be used to easily evaluate their relative performance for different I/O workloads. Finally, we conduct a comprehensive performance comparison of two relaxed consistency models on a range of commonly seen parallel I/O workloads, such as checkpoint/restart of scientific applications and random reads of deep learning applications. We demonstrate that for certain I/O scenarios, a weaker consistency model can significantly improve the I/O performance. For instance, in small random reads that are typically found in deep learning applications, session consistency achieved a 5x improvement in I/O bandwidth compared to commit consistency, even at small scales.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
LLNL-JRNL-849174
USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR)
National Science Foundation (NSF)
USDOE Laboratory Directed Research and Development (LDRD) Program
AC52-07NA27344; 1763540
USDOE National Nuclear Security Administration (NNSA)
ISSN:1045-9219
1558-2183
DOI:10.1109/TPDS.2024.3391058