Enabling Resilience in Asynchronous Many-Task Programming Models

Paul, Sanchita; Hayashi, Akira; Slattengren, Nicole Lemaster; Kolla, Hemanth; Whitlock, Matthew; Bak, Seonmyeong; Teranishi, Keita; Mayo, Jackson; Sarkar, Vivek

doi:10.1007/978-3-030-29400-7_25

Cited by 11 publications

(3 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In order to solve the problem of high cost and time-consuming of database detection, we adopt asynchronous task detection mode, so that the system can process multiple tasks at the same time and minimize the queuing delay of detection tasks [2].…”

Section: Asynchronous Task Detectionmentioning

confidence: 99%

Design and implementation of content confidentiality check system

Song,

Chen,

et al. 2024

Third International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2024)

View full text Add to dashboard Cite

Nowadays, companies use databases for infomation storage and it is inevitable that there will be confidential information which can cause severe crises and public relations problems if leaked. In order to solve this problem, we designed and implemented a content confidentiality check system for database. In this paper, the first section introduces the module design of the system. The second section introduces the key technology implementation of the system, which includes how to detect sensitive words and user password strength. The third section shows the running interface and detection results of the system. In short, this system helps users to realize a more convenient self inspection of the confidential content stored in their database, aiming to provide a strong guarantee for data storage security.

show abstract

Section: Asynchronous Task Detectionmentioning

confidence: 99%

Design and implementation of content confidentiality check system

Song,

Chen,

et al. 2024

Third International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2024)

View full text Add to dashboard Cite

show abstract

“…The control-flow resilience layer is a newer focus of research. Kokkos Resilience [8], [15] and Resilient HCLIB [16] both utilize existing parallel control libraries to manage control-flow during recovery, which benefits from preexisting knowledge of the application's default control-flow. The two implement resilience methods in parallel-region and asynchronous-many-task runtimes, respectively.…”

Section: Related Workmentioning

confidence: 99%

Integrating process, control-flow, and data resiliency layers using a hybrid Fenix/Kokkos approach

Whitlock

Morales

Bosilca

et al. 2022

2022 IEEE International Conference on Cluster Computing (CLUSTER)

View full text Add to dashboard Cite

Integrating recent advancements in resilient algorithms and techniques into existing codes is a singular challenge in fault tolerance -in part due to the underlying complexity of implementing resilience in the first place, but also due to the difficulty introduced when integrating the functionality of a standalone new strategy with the preexisting resilience layers of an application. We propose that the answer is not to build integrated solutions for users, but runtimes designed to integrate into a larger comprehensive resilience system and thereby enable the necessary jump to multi-layered recovery. Our work designs, implements, and verifies one such comprehensive system of runtimes. Utilizing Fenix, a process resilience tool with integration into preexisting resilience systems as a design priority, we update Kokkos Resilience and the use pattern of VeloC to support application-level integration of resilience runtimes. Our work shows that designing integrable systems rather than integrated systems allows for user-designed optimization and upgrading of resilience techniques while maintaining the simplicity and performance of all-in-one resilience solutions. More applicationspecific choice in resilience strategies allows for better long-term flexibility, performance, and -importantly -simplicity.

show abstract

“…Recent works on on-node task parallel programming models suggest that a simple extension of the existing task-based programming framework enables efficient localized recovery (Paul et al, 2019; Subasi et al, 2015a, 2015b, 2016).…”

Section: System Infrastructure Techniques For Resiliencementioning

confidence: 99%

Resiliency in numerical algorithm design for extreme scale simulations

Agullo

Altenbernd

Anzt

et al. 2021

The International Journal of High Performance Computing Applica

Self Cite

View full text Add to dashboard Cite

This work is based on the seminar titled ‘Resiliency in Numerical Algorithm Design for Extreme Scale Simulations’ held March 1–6, 2020, at Schloss Dagstuhl, that was attended by all the authors. Advanced supercomputing is characterized by very high computation speeds at the cost of involving an enormous amount of resources and costs. A typical large-scale computation running for 48 h on a system consuming 20 MW, as predicted for exascale systems, would consume a million kWh, corresponding to about 100k Euro in energy cost for executing 1023 floating-point operations. It is clearly unacceptable to lose the whole computation if any of the several million parallel processes fails during the execution. Moreover, if a single operation suffers from a bit-flip error, should the whole computation be declared invalid? What about the notion of reproducibility itself: should this core paradigm of science be revised and refined for results that are obtained by large-scale simulation? Naive versions of conventional resilience techniques will not scale to the exascale regime: with a main memory footprint of tens of Petabytes, synchronously writing checkpoint data all the way to background storage at frequent intervals will create intolerable overheads in runtime and energy consumption. Forecasts show that the mean time between failures could be lower than the time to recover from such a checkpoint, so that large calculations at scale might not make any progress if robust alternatives are not investigated. More advanced resilience techniques must be devised. The key may lie in exploiting both advanced system features as well as specific application knowledge. Research will face two essential questions: (1) what are the reliability requirements for a particular computation and (2) how do we best design the algorithms and software to meet these requirements? While the analysis of use cases can help understand the particular reliability requirements, the construction of remedies is currently wide open. One avenue would be to refine and improve on system- or application-level checkpointing and rollback strategies in the case an error is detected. Developers might use fault notification interfaces and flexible runtime systems to respond to node failures in an application-dependent fashion. Novel numerical algorithms or more stochastic computational approaches may be required to meet accuracy requirements in the face of undetectable soft errors. These ideas constituted an essential topic of the seminar. The goal of this Dagstuhl Seminar was to bring together a diverse group of scientists with expertise in exascale computing to discuss novel ways to make applications resilient against detected and undetected faults. In particular, participants explored the role that algorithms and applications play in the holistic approach needed to tackle this challenge. This article gathers a broad range of perspectives on the role of algorithms, applications and systems in achieving resilience for extreme scale simulations. The ultimate goal is to spark novel ideas and encourage the development of concrete solutions for achieving such resilience holistically.

show abstract

Enabling Resilience in Asynchronous Many-Task Programming Models

Cited by 11 publications

References 22 publications

Design and implementation of content confidentiality check system

Design and implementation of content confidentiality check system

Integrating process, control-flow, and data resiliency layers using a hybrid Fenix/Kokkos approach

Resiliency in numerical algorithm design for extreme scale simulations

Contact Info

Product

Resources

About