7,289 research outputs found
BrainFrame: A node-level heterogeneous accelerator platform for neuron simulations
Objective: The advent of High-Performance Computing (HPC) in recent years has
led to its increasing use in brain study through computational models. The
scale and complexity of such models are constantly increasing, leading to
challenging computational requirements. Even though modern HPC platforms can
often deal with such challenges, the vast diversity of the modeling field does
not permit for a single acceleration (or homogeneous) platform to effectively
address the complete array of modeling requirements. Approach: In this paper we
propose and build BrainFrame, a heterogeneous acceleration platform,
incorporating three distinct acceleration technologies, a Dataflow Engine, a
Xeon Phi and a GP-GPU. The PyNN framework is also integrated into the platform.
As a challenging proof of concept, we analyze the performance of BrainFrame on
different instances of a state-of-the-art neuron model, modeling the Inferior-
Olivary Nucleus using a biophysically-meaningful, extended Hodgkin-Huxley
representation. The model instances take into account not only the neuronal-
network dimensions but also different network-connectivity circumstances that
can drastically change application workload characteristics. Main results: The
synthetic approach of three HPC technologies demonstrated that BrainFrame is
better able to cope with the modeling diversity encountered. Our performance
analysis shows clearly that the model directly affect performance and all three
technologies are required to cope with all the model use cases.Comment: 16 pages, 18 figures, 5 table
Evolution of central pattern generators for the control of a five-link bipedal walking mechanism
Central pattern generators (CPGs), with a basis is neurophysiological
studies, are a type of neural network for the generation of rhythmic motion.
While CPGs are being increasingly used in robot control, most applications are
hand-tuned for a specific task and it is acknowledged in the field that generic
methods and design principles for creating individual networks for a given task
are lacking. This study presents an approach where the connectivity and
oscillatory parameters of a CPG network are determined by an evolutionary
algorithm with fitness evaluations in a realistic simulation with accurate
physics. We apply this technique to a five-link planar walking mechanism to
demonstrate its feasibility and performance. In addition, to see whether
results from simulation can be acceptably transferred to real robot hardware,
the best evolved CPG network is also tested on a real mechanism. Our results
also confirm that the biologically inspired CPG model is well suited for legged
locomotion, since a diverse manifestation of networks have been observed to
succeed in fitness simulations during evolution.Comment: 11 pages, 9 figures; substantial revision of content, organization,
and quantitative result
Reproducible Host Networking Evaluation with End-to-End Simulation
Networking researchers are facing growing challenges in evaluating and
reproducing results for modern network systems. As systems rely on closer
integration of system components and cross-layer optimizations in the pursuit
of performance and efficiency, they are also increasingly tied to specific
hardware and testbed properties. Combined with a trend towards heterogeneous
hardware, such as protocol offloads, SmartNICs, and in-network accelerators,
researchers face the choice of either investing more and more time and
resources into comparisons to prior work or, alternatively, lower the standards
for evaluation.
We aim to address this challenge by introducing SimBricks, a simulation
framework that decouples networked systems from the physical testbed and
enables reproducible end-to-end evaluation in simulation. Instead of
reinventing the wheel, SimBricks is a modular framework for combining existing
tried-and-true simulators for individual components, processor and memory, NIC,
and network, into complete testbeds capable of running unmodified systems. In
our evaluation, we reproduce key findings from prior work, including dctcp
congestion control, NOPaxos in-network consensus acceleration, and the Corundum
FPGA NIC.Comment: 15 pages, 10 figures, under submissio
Data-driven Flood Emulation: Speeding up Urban Flood Predictions by Deep Convolutional Neural Networks
Computational complexity has been the bottleneck of applying physically-based
simulations on large urban areas with high spatial resolution for efficient and
systematic flooding analyses and risk assessments. To address this issue of
long computational time, this paper proposes that the prediction of maximum
water depth rasters can be considered as an image-to-image translation problem
where the results are generated from input elevation rasters using the
information learned from data rather than by conducting simulations, which can
significantly accelerate the prediction process. The proposed approach was
implemented by a deep convolutional neural network trained on flood simulation
data of 18 designed hyetographs on three selected catchments. Multiple tests
with both designed and real rainfall events were performed and the results show
that the flood predictions by neural network uses only 0.5 % of time comparing
with physically-based approaches, with promising accuracy and ability of
generalizations. The proposed neural network can also potentially be applied to
different but relevant problems including flood predictions for urban layout
planning
Non-concave fundamental diagrams and phase transitions in a stochastic traffic cellular automaton
Within the class of stochastic cellular automata models of traffic flows, we
look at the velocity dependent randomization variant (VDR-TCA) whose parameters
take on a specific set of extreme values. These initial conditions lead us to
the discovery of the emergence of four distinct phases. Studying the
transitions between these phases, allows us to establish a rigorous
classification based on their tempo-spatial behavioral characteristics. As a
result from the system's complex dynamics, its flow-density relation exhibits a
non-concave region in which forward propagating density waves are encountered.
All four phases furthermore share the common property that moving vehicles can
never increase their speed once the system has settled into an equilibrium
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