22 papers · ranked by Valyu relevance
Mengli Feng, Abhirup Bandyopadhyay, Jorge F. Mejias
Working memory is a fundamental cognitive function which allows to transiently store and manipulate relevant information in memory. While it has been traditionally linked to activity in specific prefrontal cortical areas, recent electrophysiological and imaging evidence has shown co-occurrent activities in different…
Anil Yelam
Memory disaggregation addresses memory imbalance in a cluster by decoupling CPU and memory allocations of applications while also increasing the effective memory capacity for (memory-intensive) applications beyond the local memory limit imposed by traditional fixed-capacity servers. As the network speeds in the…
Blake Caldwell, Youngbin Im, Sangtae Ha, Richard Han + 1 more
Disaggregating resources in data centers is an emerging trend. Recent work has begun to explore memory disaggregation, but suffers limitations including lack of consideration of the complexity of cloud-based deployment, including heterogeneous hardware and APIs for cloud users and operators. In this paper, we present…
Valentina Mastrorilli, Eleonora Centofante, Federica Antonelli, Arianna Rinaldi + 1 more
Distributed training has long been known to lead to more robust memory formation as compared to massed training. Here we demonstrate that distributed and massed training differentially engage the dorsolateral and dorsomedial striatum and optogenetic priming of dorsolateral striatum can artificially increase the…
Jaewan Hong, Marcos K. Aguilera, Emmanuel Amaro, Vincent Liu + 2 more
'Aurojit Panda' 'Ion Stoica'] Disaggregated memory is an upcoming data center technology that will allow nodes (servers) to share data efficiently. Sharing data creates a debate on the level of cache coherence the system should provide.While current proposals aim to provide coherence for all or parts of the…
Kevin Garner, Polykarpos Thomadakis, Nikos Chrisochoides
This paper presents a distributed memory method for anisotropic mesh adaptation that is designed to avoid the use of collective communication and global synchronization techniques. In the presented method, meshing functionality is separated from performance aspects by utilizing a separate entity for each - a multicore…
Amit Puri, John Jose, Tamarapalli Venkatesh
—Memory disaggregation is being considered as a strong alternative to traditional architecture to deal with the memory under-utilization in data centers. Disaggregated memory can adapt to dynamically changing memory requirements for the data center applications like data analytics, big data, etc., that require…
Christina Giannoula, Kailong Huang, Jonathan Tang, Nectarios Koziris + 3 more
'Georgios Goumas' 'Zeshan Chishti' 'Nandita Vijaykumar'] Resource disaggregation offers a cost effective solution to resource scaling, utilization, and failure-handling in data centers by physically separating hardware devices in a server. Servers are architected as pools of processor, memory, and storage devices…
Christina Giannoula, Kailong Huang, Jonathan Tang, Nectarios Koziris + 3 more
'Georgios Goumas' 'Zeshan Chishti' 'Nandita Vijaykumar'] | | | Christina Giannoula§† | Kailong Huang§ | Jonathan Tang§ | | --- | --- | --- | --- | --- | | s | Nectarios Koziris† | Georgios Goumas† | Zeshan Chishti‡ | Nandita Vijaykumar§ | | | §University of Toronto | | †National Technical University of Athens | ‡ Intel…
Antonio Mastrogiorgio, Francesca Zaninotto, Francesca Maggi, Emiliano Ricciardi + 2 more
'Emiliano Ricciardi' 'Nicola Lattanzi' 'Andrea P. Malizia'] Enhancing cognitive memory through virtual reality represents an issue, that has never been investigated in organizational settings. Here, we compared a virtual memoryscape (treatment) - an immersive virtual environment used by subjects as a shared memory tool…
Beau Sievers, Ida Momennejad
We present the Spreading Activation and Memory PLasticity Model (SAMPL), a computational model of how memory retrieval changes memories. SAMPL restructures memory networks as a function of spreading activation and plasticity. Memory networks are represented as graphs of items in which edge weights capture the strength…
Megha Sehgal, Daniel Almeida Filho, George Kastellakis, Sungsoo Kim + 13 more
Events occurring close in time are often linked in memory, providing an episodic timeline and a framework for those memories. Recent studies suggest that memories acquired close in time are encoded by overlapping neuronal ensembles, but the role of dendritic plasticity mechanisms in linking memories is unknown. Using…
Ahmed M. Mohamed, Nada Mubark, Saad Zagloul
Despite the fact that multicore processors have a better instruction execution speed and lower power consumption, they also encounter a set of design challenges. The appearance of multicore and many core architectures has raised the problem of managing shared hierarchical memory systems. The main focus of this paper is…
Hsin-Hung Li, Wei Ji Ma, Clayton E. Curtis
Working memory is supported by widespread and distributed brain regions spanning across the cortical hierarchy. However, how working memory content evolves and is transmitted across cortical regions remains largely unknown. Here, we investigated the flow of working memory information across the cortex using…
David S. Cerutti, Rafal Wiewiora, Simon Boothroyd, Woody Sherman
The Structure and TOpology Replica Molecular Mechanics (STORMM) code is a next-generation molecular simulation engine and associated libraries optimized for performance on fast, multicore central processor units (CPUs) and graphics processing units (GPUs) with independent memory and tens of thousands of threads. STORMM…
Gangyong Jia, Guangjie Han, Hao Wang, Xuan Yang + 3 more
'Zhipeng Cai' 'Antonio Jara'] In a cloud computing environment, the number of virtual machines (VMs) on a single physical server and the number of applications running on each VM are continuously growing. This has led to an enormous increase in the demand of memory capacity and subsequent increase in the energy…
Lorenzo L. Pesce, Hyong C. Lee, Mark Hereld, Sid Visser + 3 more
'Rick L. Stevens' 'Albert Wildeman' 'Wim van Drongelen'] Our limited understanding of the relationship between the behavior of individual neurons and large neuronal networks is an important limitation in current epilepsy research and may be one of the main causes of our inadequate ability to treat it. Addressing this…
Matthew R. Nassar, Julie C. Helmers, Michael J. Frank
The 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.…
Carlos Fernandez-Musoles, Daniel Coca, Paul Richmond
In the last decade there has been a surge in the number of big science projects interested in achieving a comprehensive understanding of the functions of the brain, using Spiking Neuronal Network (SNN) simulations to aid discovery and experimentation. Such an approach increases the computational demands on SNN…
Tammo Ippen, Jochen M. Eppler, Hans E. Plesser, Markus Diesmann
Recent advances in the development of data structures to represent spiking neuron network models enable us to exploit the complete memory of petascale computers for a single brain-scale network simulation. In this work, we investigate how well we can exploit the computing power of such supercomputers for the creation…
Authors not listed
Machine olfaction—the artificial replication of the sense of smell—faces significant challenges due to the absence of large, standardized training datasets. Unlike vision, language, and audio models, which benefit from extensive corpora such as ImageNet, GLUE, and AudioSet, olfaction lacks scaled equivalents and…
Authors not listed
Computing electrostatic interactions remains the bottleneck of molecular dynamics (MD) simulations despite more than a century of effort in developing methods to accelerate the calculation. Previously we have developed the Spherical Grid and Treecode (SGT) and Gauss-Legendre-Spherical-t (GLST) algorithms for…