Zobrazeno 1 - 10
of 14 385
pro vyhledávání: '"Sasson"'
Publikováno v:
PLoS ONE, Vol 18, Iss 5, p e0283863 (2023)
Reading is considered a non-intuitive, cognitively demanding ability requiring synchronization between several neural networks supporting visual, language processing and higher-order abilities. With the involvement of technology in our everyday life,
Externí odkaz:
https://doaj.org/article/d42219bfdcb74abbb4d9b4eb9ebacb4e
Applications are increasingly written as dynamic workflows underpinned by an execution framework that manages asynchronous computations across distributed hardware. However, execution frameworks typically offer one-size-fits-all solutions for data fl
Externí odkaz:
http://arxiv.org/abs/2410.12092
Autor:
Hudson, Nathaniel, Hayot-Sasson, Valerie, Babuji, Yadu, Baughman, Matt, Pauloski, J. Gregory, Chard, Ryan, Foster, Ian, Chard, Kyle
Federated Learning (FL) is a decentralized machine learning paradigm where models are trained on distributed devices and are aggregated at a central server. Existing FL frameworks assume simple two-tier network topologies where end devices are direct
Externí odkaz:
http://arxiv.org/abs/2409.16495
Autor:
Ward, Logan, Pauloski, J. Gregory, Hayot-Sasson, Valerie, Babuji, Yadu, Brace, Alexander, Chard, Ryan, Chard, Kyle, Thakur, Rajeev, Foster, Ian
Computational workflows are a common class of application on supercomputers, yet the loosely coupled and heterogeneous nature of workflows often fails to take full advantage of their capabilities. We created Colmena to leverage the massive parallelis
Externí odkaz:
http://arxiv.org/abs/2408.14434
Autor:
Pauloski, J. Gregory, Hayot-Sasson, Valerie, Gonthier, Maxime, Hudson, Nathaniel, Pan, Haochen, Zhou, Sicheng, Foster, Ian, Chard, Kyle
Task-based execution frameworks, such as parallel programming libraries, computational workflow systems, and function-as-a-service platforms, enable the composition of distinct tasks into a single, unified application designed to achieve a computatio
Externí odkaz:
http://arxiv.org/abs/2408.07236
Autor:
Pan, Haochen, Chard, Ryan, Zhou, Sicheng, Kamatar, Alok, Vescovi, Rafael, Hayot-Sasson, Valérie, Bauer, André, Gonthier, Maxime, Chard, Kyle, Foster, Ian
Scientific research increasingly relies on distributed computational resources, storage systems, networks, and instruments, ranging from HPC and cloud systems to edge devices. Event-driven architecture (EDA) benefits applications targeting distribute
Externí odkaz:
http://arxiv.org/abs/2407.11432
Autor:
Pauloski, J. Gregory, Hayot-Sasson, Valerie, Ward, Logan, Brace, Alexander, Bauer, André, Chard, Kyle, Foster, Ian
Workflow and serverless frameworks have empowered new approaches to distributed application design by abstracting compute resources. However, their typically limited or one-size-fits-all support for advanced data flow patterns leaves optimization to
Externí odkaz:
http://arxiv.org/abs/2407.01764
Autor:
Kamatar, Alok, Hayot-Sasson, Valerie, Babuji, Yadu, Bauer, Andre, Rattihalli, Gourav, Hogade, Ninad, Milojicic, Dejan, Chard, Kyle, Foster, Ian
Application energy efficiency can be improved by executing each application component on the compute element that consumes the least energy while also satisfying time constraints. In principle, the function as a service (FaaS) paradigm should simplif
Externí odkaz:
http://arxiv.org/abs/2406.17710
Publikováno v:
Concurrency and Computation: Practice and Experience (2023) 35(21):e7635
The general increase in data size and data sharing motivates the adoption of Big Data strategies in several scientific disciplines. However, while several options are available, no particular guidelines exist for selecting a Big Data engine. In this
Externí odkaz:
http://arxiv.org/abs/2406.01409
Neuroimaging open-data initiatives have led to increased availability of large scientific datasets. While these datasets are shifting the processing bottleneck from compute-intensive to data-intensive, current standardized analysis tools have yet to
Externí odkaz:
http://arxiv.org/abs/2404.11556