17,453 research outputs found
Interval simulation: raising the level of abstraction in architectural simulation
Detailed architectural simulators suffer from a long development cycle and extremely long evaluation times. This longstanding problem is further exacerbated in the multi-core processor era. Existing solutions address the simulation problem by either sampling the simulated instruction stream or by mapping the simulation models on FPGAs; these approaches achieve substantial simulation speedups while simulating performance in a cycle-accurate manner This paper proposes interval simulation which rakes a completely different approach: interval simulation raises the level of abstraction and replaces the core-level cycle-accurate simulation model by a mechanistic analytical model. The analytical model estimates core-level performance by analyzing intervals, or the timing between two miss events (branch mispredictions and TLB/cache misses); the miss events are determined through simulation of the memory hierarchy, cache coherence protocol, interconnection network and branch predictor By raising the level of abstraction, interval simulation reduces both development time and evaluation time. Our experimental results using the SPEC CPU2000 and PARSEC benchmark suites and the MS multi-core simulator show good accuracy up to eight cores (average error of 4.6% and max error of 11% for the multi-threaded full-system workloads), while achieving a one order of magnitude simulation speedup compared to cycle-accurate simulation. Moreover interval simulation is easy to implement: our implementation of the mechanistic analytical model incurs only one thousand lines of code. Its high accuracy, fast simulation speed and ease-of-use make interval simulation a useful complement to the architect's toolbox for exploring system-level and high-level micro-architecture trade-offs
Big Data in Critical Infrastructures Security Monitoring: Challenges and Opportunities
Critical Infrastructures (CIs), such as smart power grids, transport systems,
and financial infrastructures, are more and more vulnerable to cyber threats,
due to the adoption of commodity computing facilities. Despite the use of
several monitoring tools, recent attacks have proven that current defensive
mechanisms for CIs are not effective enough against most advanced threats. In
this paper we explore the idea of a framework leveraging multiple data sources
to improve protection capabilities of CIs. Challenges and opportunities are
discussed along three main research directions: i) use of distinct and
heterogeneous data sources, ii) monitoring with adaptive granularity, and iii)
attack modeling and runtime combination of multiple data analysis techniques.Comment: EDCC-2014, BIG4CIP-201
Evidence accumulation in a Laplace domain decision space
Evidence accumulation models of simple decision-making have long assumed that
the brain estimates a scalar decision variable corresponding to the
log-likelihood ratio of the two alternatives. Typical neural implementations of
this algorithmic cognitive model assume that large numbers of neurons are each
noisy exemplars of the scalar decision variable. Here we propose a neural
implementation of the diffusion model in which many neurons construct and
maintain the Laplace transform of the distance to each of the decision bounds.
As in classic findings from brain regions including LIP, the firing rate of
neurons coding for the Laplace transform of net accumulated evidence grows to a
bound during random dot motion tasks. However, rather than noisy exemplars of a
single mean value, this approach makes the novel prediction that firing rates
grow to the bound exponentially, across neurons there should be a distribution
of different rates. A second set of neurons records an approximate inversion of
the Laplace transform, these neurons directly estimate net accumulated
evidence. In analogy to time cells and place cells observed in the hippocampus
and other brain regions, the neurons in this second set have receptive fields
along a "decision axis." This finding is consistent with recent findings from
rodent recordings. This theoretical approach places simple evidence
accumulation models in the same mathematical language as recent proposals for
representing time and space in cognitive models for memory.Comment: Revised for CB
Synaptic state matching: a dynamical architecture for predictive internal representation and feature perception
Here we consider the possibility that a fundamental function of sensory cortex is the generation of an internal simulation of sensory environment in real-time. A logical elaboration of this idea leads to a dynamical neural architecture that oscillates between two fundamental network states, one driven by external input, and the other by recurrent synaptic drive in the absence of sensory input. Synaptic strength is modified by a proposed synaptic state matching (SSM) process that ensures equivalence of spike statistics between the two network states. Remarkably, SSM, operating locally at individual synapses, generates accurate and stable network-level predictive internal representations, enabling pattern completion and unsupervised feature detection from noisy sensory input. SSM is a biologically plausible substrate for learning and memory because it brings together sequence learning, feature detection, synaptic homeostasis, and network oscillations under a single parsimonious computational framework. Beyond its utility as a potential model of cortical computation, artificial networks based on this principle have remarkable capacity for internalizing dynamical systems, making them useful in a variety of application domains including time-series prediction and machine intelligence
Hamiltonian dynamics and geometry of phase transitions in classical XY models
The Hamiltonian dynamics associated to classical, planar, Heisenberg XY
models is investigated for two- and three-dimensional lattices. Besides the
conventional signatures of phase transitions, here obtained through time
averages of thermodynamical observables in place of ensemble averages,
qualitatively new information is derived from the temperature dependence of
Lyapunov exponents. A Riemannian geometrization of newtonian dynamics suggests
to consider other observables of geometric meaning tightly related with the
largest Lyapunov exponent. The numerical computation of these observables -
unusual in the study of phase transitions - sheds a new light on the
microscopic dynamical counterpart of thermodynamics also pointing to the
existence of some major change in the geometry of the mechanical manifolds at
the thermodynamical transition. Through the microcanonical definition of the
entropy, a relationship between thermodynamics and the extrinsic geometry of
the constant energy surfaces of phase space can be naturally
established. In this framework, an approximate formula is worked out,
determining a highly non-trivial relationship between temperature and topology
of the . Whence it can be understood that the appearance of a phase
transition must be tightly related to a suitable major topology change of the
. This contributes to the understanding of the origin of phase
transitions in the microcanonical ensemble.Comment: in press on Physical Review E, 43 pages, LaTeX (uses revtex), 22
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Dynamics and statistics of simple models with infinite-range attractive interaction
In this paper we review a series of results obtained for 1D and 2D simple
N-body dynamical models with infinite-range attractive interactions and without
short distance singularities. The free energy of both models can be exactly
obtained in the canonical ensemble, while microcanonical results can be derived
from numerical simulations. Both models show a phase transition from a low
energy clustered phase to a high energy gaseous state, in analogy with the
models introduced in the early 70's by Thirring and Hertel. The phase
transition is second order for the 1D model, first order for the 2D model.
Negative specific heat appears in both models near the phase transition point.
For both models, in the presence of a negative specific heat, a cluster of
collapsed particles coexists with a halo of higher energy particles which
perform long correlated flights, which lead to anomalous diffusion. The
dynamical origin of the "superdiffusion" is different in the two models, being
related to particle trapping and untrapping in the cluster in 1D, while in 2D
the channelling of particles in an egg-crate effective potential is responsible
of the effect. Both models are Lyapunov unstable and the maximal Lyapunov
exponent has a peak just in the region preceeding the phase
transition. Moreover, in the low energy limit increases
proportionally to the square root of the internal energy, while in the high
energy region it vanishes as .Comment: 33 pages, Latex2 - 12 Figs - Proceedings of the Conference "The
Chaotic Universe" held in Rome-Pescara in Feb. 199
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