4,587 research outputs found
Optimising Simulation Data Structures for the Xeon Phi
In this paper, we propose a lock-free architecture
to accelerate logic gate circuit simulation using SIMD multi-core
machines. We evaluate its performance on different test circuits
simulated on the Intel Xeon Phi and 2 other machines. Comparisons
are presented of this software/hardware combination with
reported performances of GPU and other multi-core simulation
platforms. Comparisons are also given between the lock free
architecture and a leading commercial simulator running on the
same Intel hardware
Massively parallel computing on an organic molecular layer
Current computers operate at enormous speeds of ~10^13 bits/s, but their
principle of sequential logic operation has remained unchanged since the 1950s.
Though our brain is much slower on a per-neuron base (~10^3 firings/s), it is
capable of remarkable decision-making based on the collective operations of
millions of neurons at a time in ever-evolving neural circuitry. Here we use
molecular switches to build an assembly where each molecule communicates-like
neurons-with many neighbors simultaneously. The assembly's ability to
reconfigure itself spontaneously for a new problem allows us to realize
conventional computing constructs like logic gates and Voronoi decompositions,
as well as to reproduce two natural phenomena: heat diffusion and the mutation
of normal cells to cancer cells. This is a shift from the current static
computing paradigm of serial bit-processing to a regime in which a large number
of bits are processed in parallel in dynamically changing hardware.Comment: 25 pages, 6 figure
Optimising Simulation Data Structures for the Xeon Phi
In this paper, we propose a lock-free architecture
to accelerate logic gate circuit simulation using SIMD multi-core
machines. We evaluate its performance on different test circuits
simulated on the Intel Xeon Phi and 2 other machines. Comparisons
are presented of this software/hardware combination with
reported performances of GPU and other multi-core simulation
platforms. Comparisons are also given between the lock free
architecture and a leading commercial simulator running on the
same Intel hardware
Parallel computing for brain simulation
[Abstract] Background: The human brain is the most complex system in the known universe, it is therefore one of the greatest mysteries. It provides human beings with extraordinary abilities. However, until now it has not been understood yet how and why most of these abilities are produced.
Aims: For decades, researchers have been trying to make computers reproduce these abilities, focusing on both understanding the nervous system and, on processing data in a more efficient way than before. Their aim is to make computers process information similarly to the brain. Important technological developments and vast multidisciplinary projects have allowed creating the first simulation with a number of neurons similar to that of a human brain.
Conclusion: This paper presents an up-to-date review about the main research projects that are trying to simulate and/or emulate the human brain. They employ different types of computational models using parallel computing: digital models, analog models and hybrid models. This review includes the current applications of these works, as well as future trends. It is focused on various works that look for advanced progress in Neuroscience and still others which seek new discoveries in Computer Science (neuromorphic hardware, machine learning techniques). Their most outstanding characteristics are summarized and the latest advances and future plans are presented. In addition, this review points out the importance of considering not only neurons: Computational models of the brain should also include glial cells, given the proven importance of astrocytes in information processing.Galicia. Consellería de Cultura, Educación e Ordenación Universitaria; GRC2014/049Galicia. Consellería de Cultura, Educación e Ordenación Universitaria; R2014/039Instituto de Salud Carlos III; PI13/0028
GeNN: a code generation framework for accelerated brain simulations
Large-scale numerical simulations of detailed brain circuit models are important for identifying hypotheses on brain functions and testing their consistency and plausibility. An ongoing challenge for simulating realistic models is, however, computational speed. In this paper, we present the GeNN (GPU-enhanced Neuronal Networks) framework, which aims to facilitate the use of graphics accelerators for computational models of large-scale neuronal networks to address this challenge. GeNN is an open source library that generates code to accelerate the execution of network simulations on NVIDIA GPUs, through a flexible and extensible interface, which does not require in-depth technical knowledge from the users. We present performance benchmarks showing that 200-fold speedup compared to a single core of a CPU can be achieved for a network of one million conductance based Hodgkin-Huxley neurons but that for other models the speedup can differ.
GeNN is available for Linux, Mac OS X and Windows platforms. The source code, user manual, tutorials,
Wiki, in-depth example projects and all other related information can be found on the project website http://genn-team.github.io/genn/
VIOLA - A multi-purpose and web-based visualization tool for neuronal-network simulation output
Neuronal network models and corresponding computer simulations are invaluable
tools to aid the interpretation of the relationship between neuron properties,
connectivity and measured activity in cortical tissue. Spatiotemporal patterns
of activity propagating across the cortical surface as observed experimentally
can for example be described by neuronal network models with layered geometry
and distance-dependent connectivity. The interpretation of the resulting stream
of multi-modal and multi-dimensional simulation data calls for integrating
interactive visualization steps into existing simulation-analysis workflows.
Here, we present a set of interactive visualization concepts called views for
the visual analysis of activity data in topological network models, and a
corresponding reference implementation VIOLA (VIsualization Of Layer Activity).
The software is a lightweight, open-source, web-based and platform-independent
application combining and adapting modern interactive visualization paradigms,
such as coordinated multiple views, for massively parallel neurophysiological
data. For a use-case demonstration we consider spiking activity data of a
two-population, layered point-neuron network model subject to a spatially
confined excitation originating from an external population. With the multiple
coordinated views, an explorative and qualitative assessment of the
spatiotemporal features of neuronal activity can be performed upfront of a
detailed quantitative data analysis of specific aspects of the data.
Furthermore, ongoing efforts including the European Human Brain Project aim at
providing online user portals for integrated model development, simulation,
analysis and provenance tracking, wherein interactive visual analysis tools are
one component. Browser-compatible, web-technology based solutions are therefore
required. Within this scope, with VIOLA we provide a first prototype.Comment: 38 pages, 10 figures, 3 table
Behavioral simulation and synthesis of biological neuron systems using synthesizable VHDL
Neurons are complex biological entities which form the basis of nervous systems. Insight can be gained into neuron behavior through the use of computer models and as a result many such models have been developed. However, there exists a trade-off between biological accuracy and simulation time with the most realistic results requiring extensive computation. To address this issue, a novel approach is described in this paper that allows complex models of real biological systems to be simulated at a speed greater than real time and with excellent accuracy. The approach is based on a specially developed neuron model VHDL library which allows complex neuron systems to be implemented on field programmable gate array (FPGA) hardware. The locomotion system of the nematode Caenorhabditis elegans is used as a case study and the measured results show that the real time FPGA based implementation performs 288 times faster than traditional ModelSim simulations for the same accuracy
- …