18,762 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
Folding model study of the elastic scattering at low energies
The folding model analysis of the elastic scattering at the
incident energies below the reaction threshold of 34.7 MeV (in the lab system)
has been done using the well-tested density dependent versions of the M3Y
interaction and realistic choices for the He density. Because the
absorption is negligible at the energies below the reaction threshold, we were
able to probe the optical potential at low energies quite
unambiguously and found that the overlap density used to
construct the density dependence of the M3Y interaction is strongly distorted
by the Pauli blocking. This result gives possible explanation of a
long-standing inconsistency of the double-folding model in its study of the
elastic and -nucleus scattering at low energies using
the same realistic density dependent M3Y interaction
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Towards Prediction of Non-Radiative Decay Pathways in Organic Compounds I: The Case of Naphthalene Quantum Yields
Many emerging technologies depend on human’s ability to control and manipulate the excited-state properties of molecular systems. These technologies include fluorescent labeling in biomedical imaging, light harvesting in photovoltaics, and electroluminescence in light-emitting devices. All of these systems suffer from non-radiative loss pathways that dissipate electronic energy as heat, which causes the overall system efficiency to be directly linked to quantum yield (Φ) of the molecular excited state. Unfortunately, Φ is very difficult to predict from first principles because the description of a slow non-radiative decay mechanism requires an accurate description of long-timescale excited-state quantum dynamics. In the present study, we introduce an efficient semiempirical method of calculating the fluorescence quantum yield (Φfl) for molecular chromophores, which, based on machine learning, converts simple electronic energies computed using time-dependent density functional theory (TDDFT) into an estimate of Φfl. As with all machine learning strategies, the algorithm needs to be trained on fluorescent dyes for which Φfl’s are known, so as to provide a black-box method which can later predict Φfl’s for chemically similar chromophores that have not been studied experimentally. As a first illustration of how our proposed algorithm can be trained, we examine a family of 25 naphthalene derivatives. The simplest application of the energy gap law is found to be inadequate to explain the rates of internal conversion (IC) or intersystem crossing (ISC) – the electronic properties of at least one higher-lying electronic state (Sn or Tn) or one far-from-equilibrium geometry are typically needed to obtain accurate results. Indeed, the key descriptors turn out to be the transition state between the Franck–Condon minimum a distorted local minimum near an S0/S1 conical intersection (which governs IC) and the magnitude of the spin–orbit coupling (which governs ISC). The resulting Φfl’s are predicted with reasonable accuracy (±22%), making our approach a promising ingredient for high-throughput screening and rational design of the molecular excited states with desired Φ’s. We thus conclude that our model, while semi-empirical in nature, does in fact extract sound physical insight into the challenge of describing non-radiative relaxations
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