Search results for "Speedup"
showing 10 items of 97 documents
Equilibrating Glassy Systems with Parallel Tempering
2001
We discuss the efficiency of the so-called parallel tempering method to equilibrate glassy systems also at low temperatures. The main focus is on two structural glass models, SiO2 and a Lennard-Jones system, but we also investigate a fully connected 10 state Potts-glass. By calculating the mean squared displacement of a tagged particle and the spin-autocorrelation function, we find that for these three glass-formers the parallel tempering method is indeed able to generate, at low temperatures, new independent configurations at a rate which is O(100) times faster than more traditional algorithms, such as molecular dynamics and single spin flip Monte Carlo dynamics. In addition we find that t…
Accelerating collision detection for large-scale crowd simulation on multi-core and many-core architectures
2013
The computing capabilities of current multi-core and many-core architectures have been used in crowd simulations for both enhancing crowd rendering and simulating continuum crowds. However, improving the scalability of crowd simulation systems by exploiting the inherent parallelism of these architectures is still an open issue. In this paper, we propose different parallelization strategies for the collision check procedure that takes place in agent-based simulations. These strategies are designed for exploiting the parallelism in both multi-core and many-core architectures like graphic processing units (GPUs). As for the many-core implementations, we analyse the bottlenecks of a previous G…
Suffix Array Construction on Multi-GPU Systems
2019
Suffix arrays are prevalent data structures being fundamental to a wide range of applications including bioinformatics, data compression, and information retrieval. Therefore, various algorithms for (parallel) suffix array construction both on CPUs and GPUs have been proposed over the years. Although providing significant speedup over their CPU-based counterparts, existing GPU implementations share a common disadvantage: input text sizes are limited by the scarce memory of a single GPU. In this paper, we overcome aforementioned memory limitations by exploiting multi-GPU nodes featuring fast NVLink interconnects. In order to achieve high performance for this communication-intensive task, we …
QuBiLS-MIDAS: A parallel free-software for molecular descriptors computation based on multilinear algebraic maps
2014
The present report introduces the QuBiLS-MIDAS software belonging to the ToMoCoMD-CARDD suite for the calculation of three-dimensional molecular descriptors (MDs) based on the two-linear (bilinear), three-linear, and four-linear (multilinear or N-linear) algebraic forms. Thus, it is unique software that computes these tensor-based indices. These descriptors, establish relations for two, three, and four atoms by using several (dis-)similarity metrics or multimetrics, matrix transformations, cutoffs, local calculations and aggregation operators. The theoretical background of these N-linear indices is also presented. The QuBiLS-MIDAS software was developed in the Java programming language and …
A semi-Lagrangian AMR scheme for 2D transport problems in conservation form
2013
In this paper, we construct a semi-Lagrangian (SL) Adaptive-Mesh-Refinement (AMR) solver for 1D and 2D transport problems in conservation form. First, we describe the a-la-Harten AMR framework: the adaptation process selects a hierarchical set of grids with different resolutions depending on the features of the integrand function, using as criteria the point value prediction via interpolation from coarser meshes, and the appearance of large gradients. We integrate in time by reconstructing at the feet of the characteristics through the Point-Value Weighted Essentially Non-Oscillatory (PV-WENO) interpolator. We propose, then, an extension to the 2D setting by making the time integration dime…
Newton Method for Minimal Learning Machine
2021
Minimal Learning Machine (MLM) is a distance-based supervised machine learning method for classification and regression problems. Its main advances are simple formulation and fast learning. Computing the MLM prediction in regression requires a solution to the optimization problem, which is determined by the input and output distance matrix mappings. In this paper, we propose to use the Newton method for solving this optimization problem in multi-output regression and compare the performance of this algorithm with the most popular Levenberg–Marquardt method. According to our knowledge, MLM has not been previously studied in the context of multi-output regression in the literature. In additio…
Quantum Search with Multiple Walk Steps per Oracle Query
2015
We identify a key difference between quantum search by discrete- and continuous-time quantum walks: a discrete-time walk typically performs one walk step per oracle query, whereas a continuous-time walk can effectively perform multiple walk steps per query while only counting query time. As a result, we show that continuous-time quantum walks can outperform their discrete-time counterparts, even though both achieve quadratic speedups over their corresponding classical random walks. To provide greater equity, we allow the discrete-time quantum walk to also take multiple walk steps per oracle query while only counting queries. Then it matches the continuous-time algorithm's runtime, but such …
Breaking adiabatic quantum control with deep learning
2020
In the era of digital quantum computing, optimal digitized pulses are requisite for efficient quantum control. This goal is translated into dynamic programming, in which a deep reinforcement learning (DRL) agent is gifted. As a reference, shortcuts to adiabaticity (STA) provide analytical approaches to adiabatic speed up by pulse control. Here, we select single-component control of qubits, resembling the ubiquitous two-level Landau-Zener problem for gate operation. We aim at obtaining fast and robust digital pulses by combining STA and DRL algorithm. In particular, we find that DRL leads to robust digital quantum control with operation time bounded by quantum speed limits dictated by STA. I…
Fourier-Accelerated Polymer Dynamics
1994
Fourier acceleration methods are applied to simulations of two-dimensional isolated ring polymers of up to N = 64 monomers. Three simulation schemes are compared: (i) a simple Langevin simulation with local updating, (ii) a Langevin algorithm with Fourier acceleration, and (iii) a Fourier accelerated Langevin algorithm combined with Metropolis acceptance of the moves (Force Biased Monte Carlo). In contrast to (i) and (ii), method (iii) is not hampered by systematic discretization errors, which, in case (ii), seem to grow systematically with chain length N. The results on the correlation time 4 are not very accurate, however, the data are in rough agreement with τ s N z with z= 2.5 (Rouse mo…
Generalized Langevin dynamics: construction and numerical integration of non-Markovian particle-based models.
2018
We propose a generalized Langevin dynamics (GLD) technique to construct non-Markovian particle-based coarse-grained models from fine-grained reference simulations and to efficiently integrate them. The proposed GLD model has the form of a discretized generalized Langevin equation with distance-dependent two-particle contributions to the self- and pair-memory kernels. The memory kernels are iteratively reconstructed from the dynamical correlation functions of an underlying fine-grained system. We develop a simulation algorithm for this class of non-Markovian models that scales linearly with the number of coarse-grained particles. Our GLD method is suitable for coarse-grained studies of syste…