458 research outputs found
Usage and Scaling of an Open-Source Spiking Multi-Area Model of Monkey Cortex
We are entering an age of `big' computational neuroscience, in which neural
network models are increasing in size and in numbers of underlying data sets.
Consolidating the zoo of models into large-scale models simultaneously
consistent with a wide range of data is only possible through the effort of
large teams, which can be spread across multiple research institutions. To
ensure that computational neuroscientists can build on each other's work, it is
important to make models publicly available as well-documented code. This
chapter describes such an open-source model, which relates the connectivity
structure of all vision-related cortical areas of the macaque monkey with their
resting-state dynamics. We give a brief overview of how to use the executable
model specification, which employs NEST as simulation engine, and show its
runtime scaling. The solutions found serve as an example for organizing the
workflow of future models from the raw experimental data to the visualization
of the results, expose the challenges, and give guidance for the construction
of ICT infrastructure for neuroscience
Many-Task Computing and Blue Waters
This report discusses many-task computing (MTC) generically and in the
context of the proposed Blue Waters systems, which is planned to be the largest
NSF-funded supercomputer when it begins production use in 2012. The aim of this
report is to inform the BW project about MTC, including understanding aspects
of MTC applications that can be used to characterize the domain and
understanding the implications of these aspects to middleware and policies.
Many MTC applications do not neatly fit the stereotypes of high-performance
computing (HPC) or high-throughput computing (HTC) applications. Like HTC
applications, by definition MTC applications are structured as graphs of
discrete tasks, with explicit input and output dependencies forming the graph
edges. However, MTC applications have significant features that distinguish
them from typical HTC applications. In particular, different engineering
constraints for hardware and software must be met in order to support these
applications. HTC applications have traditionally run on platforms such as
grids and clusters, through either workflow systems or parallel programming
systems. MTC applications, in contrast, will often demand a short time to
solution, may be communication intensive or data intensive, and may comprise
very short tasks. Therefore, hardware and software for MTC must be engineered
to support the additional communication and I/O and must minimize task dispatch
overheads. The hardware of large-scale HPC systems, with its high degree of
parallelism and support for intensive communication, is well suited for MTC
applications. However, HPC systems often lack a dynamic resource-provisioning
feature, are not ideal for task communication via the file system, and have an
I/O system that is not optimized for MTC-style applications. Hence, additional
software support is likely to be required to gain full benefit from the HPC
hardware
Brain-Inspired Computing
This open access book constitutes revised selected papers from the 4th International Workshop on Brain-Inspired Computing, BrainComp 2019, held in Cetraro, Italy, in July 2019. The 11 papers presented in this volume were carefully reviewed and selected for inclusion in this book. They deal with research on brain atlasing, multi-scale models and simulation, HPC and data infra-structures for neuroscience as well as artificial and natural neural architectures
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