File System Semantics Requirements of HPC Applications

Wang, Chen; Mohror, Kathryn; Snir, Marc

doi:10.1145/3431379.3460637

Cited by 12 publications

(2 citation statements)

References 35 publications

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“…Ad-hoc file systems directly support malleability by adapting to a given use case. Most HPC applications, e.g., do not require strong consistency semantics and rarely use certain I/O operations like rename() or readdir() [104], [105]. Adhoc file systems can thus be configured for these applications by relaxing cache consistency guarantees, increasing the performance of a file system by simplifying its protocols.…”

Section: Malleability On Storage Resourcesmentioning

confidence: 99%

Malleability in Modern HPC Systems: Current Experiences, Challenges, and Future Opportunities

Tarraf,

Schreiber,

Cascajo

et al. 2024

IEEE Trans. Parallel Distrib. Syst.

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With the increase of complex scientific simulations driven by workflows and heterogeneous workload profiles, managing system resources effectively is essential for improving performance and system throughput, especially due to trends like heterogeneous HPC and deeply integrated systems with on-chip accelerators. For optimal resource utilization, dynamic resource allocation can improve productivity across all system and application levels, by adapting the applications' configurations to the system's resources. In this context, malleable jobs, which can change resources at runtime, can increase the system throughput and resource utilization while bringing various advantages for HPC users (e.g., shorter waiting time). Malleability has received much attention recently, even though it has been an active research area for almost two decades [1]. This paper presents the state-of-the-art of malleable implementations in HPC systems, targeting mainly malleability in compute and I/O resources. Based on our experiences, we state our current concerns and list future opportunities for research.

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Section: Malleability On Storage Resourcesmentioning

confidence: 99%

Malleability in Modern HPC Systems: Current Experiences, Challenges, and Future Opportunities

Tarraf,

Schreiber,

Cascajo

et al. 2024

IEEE Trans. Parallel Distrib. Syst.

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“…With this extra information, PFSs can adopt a weaker consistency model, while guaranteeing the same outcome of POSIX consistency. Wang et al, [18] have studied many such PFSs and their consistency models. Here, we briefly discuss the most commonly used models.…”

Section: Relaxed Storage Modelsmentioning

confidence: 99%

Recorder 2.0: Efficient Parallel I/O Tracing and Analysis

Wang

Sun

Snir

et al. 2020

2020 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW)

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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 typically found in deep learning applications, session consistency achieved an 5x improvement in I/O bandwidth compared to commit consistency, even at small scales.

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