Learning cost-effective sampling strategies for empirical performance modeling
M Ritter, A Calotoiu, S Rinke, T Reimann… - 2020 IEEE …, 2020 - ieeexplore.ieee.org
Identifying scalability bottlenecks in parallel applications is a vital but also laborious and
expensive task. Empirical performance models have proven to be helpful to find such …
expensive task. Empirical performance models have proven to be helpful to find such …
Structural plasticity on the spinnaker many-core neuromorphic system
The structural organization of cortical areas is not random, with topographic maps
commonplace in sensory processing centers. This topographical organization allows …
commonplace in sensory processing centers. This topographical organization allows …
Building a realistic, scalable memory model with independent engrams using a homeostatic mechanism
M Kaster, F Czappa, M Butz-Ostendorf… - Frontiers in …, 2024 - frontiersin.org
Memory formation is usually associated with Hebbian learning and synaptic plasticity, which
changes the synaptic strengths but omits structural changes. A recent study suggests that …
changes the synaptic strengths but omits structural changes. A recent study suggests that …
[图书][B] Political plasticity: The future of democracy and dictatorship
FM Moghaddam - 2023 - books.google.com
Political plasticity refers to limitations on how fast, how much, and in what ways political
behavior does (or does not) change. In a number of important areas of behavior, such as …
behavior does (or does not) change. In a number of important areas of behavior, such as …
Lightweight requirements engineering for exascale co-design
A Calotoiu, A Graf, T Hoefler, D Lorenz… - 2018 IEEE …, 2018 - ieeexplore.ieee.org
Given the tremendous cost of an exascale system, its architecture must match the
requirements of the applications it is supposed to run as precisely as possible. Conversely …
requirements of the applications it is supposed to run as precisely as possible. Conversely …
[PDF][PDF] Extrapeak: Advanced automatic performance modeling for HPC applications
Performance models are powerful tools allowing developers to understand the behavior of
their applications, and empower them to address performance issues already during the …
their applications, and empower them to address performance issues already during the …
Noise-resilient empirical performance modeling with deep neural networks
Empirical performance modeling is a proven instrument to analyze the scaling behavior of
HPC applications. Using a set of smaller-scale experiments, it can provide important insights …
HPC applications. Using a set of smaller-scale experiments, it can provide important insights …
Simulating structural plasticity of the brain more scalable than expected
Structural plasticity of the brain describes the creation of new and the deletion of old
synapses over time. Rinke et al.(JPDC 2018) introduced a scalable algorithm that simulates …
synapses over time. Rinke et al.(JPDC 2018) introduced a scalable algorithm that simulates …
2023 IEEE Scientific Visualization Contest Winner: VisAnywhere: Developing Multi-platform Scientific Visualization Applications
T Marrinan, VA Mateevitsi, M Moeller… - IEEE Computer …, 2024 - ieeexplore.ieee.org
Scientists often explore and analyze large-scale scientific simulation data by leveraging two-
and three-dimensional visualizations. The data and tasks can be complex and therefore best …
and three-dimensional visualizations. The data and tasks can be complex and therefore best …
VisAnywhere: Developing Multi-platform Scientific Visualization Applications
T Marrinan, M Moeller, A Kanayinkal… - arXiv preprint arXiv …, 2024 - arxiv.org
Scientists often explore and analyze large-scale scientific simulation data by leveraging two-
and three-dimensional visualizations. The data and tasks can be complex and therefore best …
and three-dimensional visualizations. The data and tasks can be complex and therefore best …