Overcoming GPU Memory Capacity Limitations in Hybrid MPI Implementations of CFD

2019 ◽  
pp. 100-111
Author(s):  
Jake Choi ◽  
Yoonhee Kim ◽  
Heon-young Yeom
Keyword(s):  
Memory Capacity ◽  
Capacity Limitations

Children's long‐term retention is directly constrained by their working memory capacity limitations

2021 ◽  
Author(s):  
Alicia Forsberg ◽  
Dominic Guitard ◽  
Eryn J. Adams ◽  
Duangporn Pattanakul ◽  
Nelson Cowan
Keyword(s):  
Working Memory ◽  
Working Memory Capacity ◽  
Memory Capacity ◽  
Capacity Limitations ◽  
Term Retention ◽  
Long Term Retention

scholarly journals Chunking as a rational strategy for lossy data compression in visual working memory

2017 ◽  
Author(s):  
Matthew R. Nassar ◽  
Julie C. Helmers ◽  
Michael J. Frank
Keyword(s):  
Working Memory ◽  
Visual Working Memory ◽  
Performance Optimization ◽  
Human Performance ◽  
Storage Capacity ◽  
Working Memory Capacity ◽  
Quantitative Description ◽  
Memory Task ◽  
Memory Capacity ◽  
Capacity Limitations

AbstractThe nature of capacity limits for visual working memory has been the subject of an intense debate that has relied on models that assume items are encoded independently. Here we propose that instead, similar features are jointly encoded through a “chunking” process to optimize performance on visual working memory tasks. We show that such chunking can: 1) facilitate performance improvements for abstract capacity-limited systems, 2) be optimized through reinforcement, 3) be implemented by center-surround dynamics, and 4) increase effective storage capacity at the expense of recall precision. Human performance on a variant of a canonical working memory task demonstrated performance advantages, precision detriments, inter-item dependencies, and trial-to-trial behavioral adjustments diagnostic of performance optimization through center-surround chunking. Models incorporating center-surround chunking provided a better quantitative description of human performance in our study as well as in a meta-analytic dataset, and apparent differences in working memory capacity across individuals were attributable to individual differences in the implementation of chunking. Our results reveal a normative rationale for center-surround connectivity in working memory circuitry, call for re-evaluation of memory performance differences that have previously been attributed to differences in capacity, and support a more nuanced view of visual working memory capacity limitations: strategic tradeoff between storage capacity and memory precision through chunking contribute to flexible capacity limitations that include both discrete and continuous aspects.

scholarly journals Interference and memory capacity limitations.

2017 ◽  
Vol 124 (5) ◽  
pp. 551-571 ◽  
Author(s):  
Ansgar D. Endress ◽  
Szilárd Szabó
Keyword(s):  
Memory Capacity ◽  
Capacity Limitations

scholarly journals The origin of the visual working memory capacity limitations

2016 ◽  
Vol 16 (12) ◽  
pp. 1060
Author(s):  
Marjan Persuh ◽  
Emmanuel Delgado ◽  
Aharon Zarzar
Keyword(s):  
Working Memory ◽  
Visual Working Memory ◽  
Working Memory Capacity ◽  
Memory Capacity ◽  
Capacity Limitations

scholarly journals Under-resourced or overloaded? Rethinking working memory deficits in developmental language disorder

2021 ◽  
Author(s):  
Samuel David Jones ◽  
Gert Westermann
Keyword(s):  
Working Memory ◽  
Language Disorder ◽  
Spoken Word Recognition ◽  
Working Memory Capacity ◽  
Memory Capacity ◽  
Spoken Word ◽  
Developmental Language Disorder ◽  
Capacity Limitations ◽  
Speech Representation ◽  
Internal Speech

Dominant theoretical accounts of developmental language disorder (DLD) are unanimous in assuming working memory capacity limitations. In the current report, we present an alternative view: That working memory in DLD is not under-resourced but overloaded due to operating on speech representations with low discriminability. This account is developed through computational simulations involving deep convolutional neural networks trained on spoken word spectrograms in which frequency information is either retained to mimic typical development or degraded to mimic spectral processing deficits identified among children with DLD. We assess not only spoken word recognition accuracy and predictive probability and entropy (i.e., predictive distribution spread), but also use mean-field-theory based manifold analysis to assess; (i) internal speech representation dimensionality, and (ii) classification capacity, a measure of networks’ ability to isolate any given internal speech representation that is used as a proxy for attentional control. We show that instantiating a low-level frequency discrimination deficit results in the formation of internal speech representations with atypically high dimensionality, and that classification capacity is exhausted due to low representation separability. These representation and control deficits underpin not only lower performance accuracy but also greater uncertainty even when making accurate predictions in a simulated spoken word recognition task (i.e., predictive distributions with low maximum probability and high entropy), which replicates the response delays and word finding difficulties often seen in DLD. Overall, these simulations demonstrate an integrated theoretical account of speech representation and processing in DLD in which working memory capacity limitations play no causal role.

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