18 research outputs found
Fast and Accurate Simulation Framework Targeting Molecular Dynamics for Cells Substructures
No full textMolecular simulations are continuously shedding light into
a wide range of biological phenomena such as cell structural stability, intracellular processes, cells interaction with drugs
and membrane permeability. Hence, the search for better and faster approaches of molecular simulations to understand these activities related to cells dynamics, is becoming more and more challenging. This thesis firstly proposes a multiscale modeling approach that provides a link between atomistic level and continuum scale modeling concerning subcellular proteins simulations. This methodology represents a first step of a more complex multiscale model able to simulate and predict the effects of specific local changes on the subcellular proteins mechanics due to pathological conditions such as single point mutations, or therapeutic treatments such as the effects of bound pharmacological molecules, and their consequences on the overall properties of the filament. Although this approach takes actin filaments as case study, it devises the guidelines on multiscale modeling of several supramolecular hierarchical assemblies such as microtubules, collagen fibers, and polymers
whose localized nanoscale phenomena reflect on a macroscale level of description. Moreover, this thesis presents optimization strategies for molecular simulations, proposing graphics processing units (GPU) as an excellent high performance computing commodity hardware for this purpose,
paying particular attention to the accuracy of the simulations.
A coarse grain (CG) molecular dynamics (MD) simulator specialized for simulations of lipid bilayers, is considered as a case study and optimized and accelerated for single-GPU environment. A speed-up of 30 fold for water systems and 15 fold for lipids is obtained when exploiting CUDA intrinsic functions for the floating point arithmetic in the GTX480 GPU with respect to CPU simulations. This research performs a detailed analysis of CG features and their impact on the achievable acceleration, to formulate guidelines for writing more efficient CG MD codes. Finally, the CG MD model considered as case study is validated and integrated for multiple-GPU environment and thereafter employed to perform analysis of large-scale systems simulations investigating the phase transition of lipid bilayers of cell membranes in a supercomputing context
Acceleration of Coarse Grain Molecular Dynamics on GPU Architectures
No full textCoarse grain (CG) molecular models have been proposed to simulate complex sys- tems with lower computational overheads and longer timescales with respect to atom- istic level models. However, their acceleration on parallel architectures such as Graphic Processing Units (GPU) presents original challenges that must be carefully evaluated. The objective of this work is to characterize the impact of CG model features on parallel simulation performance. To achieve this, we implemented a GPU-accelerated version of a CG molecular dynamics simulator, to which we applied specic optimizations for CG models, such as dedicated data structures to handle dierent bead type interac- tions, obtaining a maximum speed-up of 14 on the NVIDIA GTX480 GPU with Fermi architecture. We provide a complete characterization and evaluation of algorithmic and simulated system features of CG models impacting the achievable speed-up and accuracy of results, using three dierent GPU architectures as case studie
Optimization of Molecular Dynamics Simulations from a High Performance Computing Viewpoint
No full textGPU Acceleration of Simulation Tool for Lipid-Bilayers
No full textNowadays the need for powerful hardware architectures, which allow for high throughput data analysis and calculus, is fundamental especially for biological applications. We have been focused on utilizing the Graphic Processing Unit (GPU) architectures of NVIDIA for accelerating a lipid bilayer simulation tool for biomembranes. ©2010 IEEE
CHARACTERIZATION OF COARSE GRAIN MOLECULAR DYNAMIC SIMULATION PERFORMANCE ON GRAPHIC PROCESSING UNIT ARCHITECTURES
No full textTowards Low Cost Virtual Biological Laboratories: Molecular Modelling Simulation on Commodity Hardware.
No full textMany essential cell processes, such as the conformation of embedded proteins, membrane permeability, interaction with drugs and signalling, are directly connected to the
molecular dynamics of cell membranes. The importance of this biology has led to an intensifying demand for hardware and software optimized models and tools, implemented
on commodity high performance low-cost hardware, in order to provide the scientific community with virtual low cost laboratories. In the light of these considerations, we implemented an accelerated version of a molecular dynamics coarse-grain lipid bilayers simulator on commodity Graphic Processing Units (GPU) architectures. The characteristics of this molecular dynamics model, such as new force fields for pair potentials that include an unconventional representation for water and charges, were particularly challenging. We introduced new algorithms and data structures required by coarse-grain models compared to atomistic ones, for the modelling of the integration timestep, neighbour list generation, and nonbonded
force interactions. We characterized the impact on performance of biological systems of differing complexity in terms of size, particle type and timestep. We also compared the simulations of many particle-type systems against single particle-type systems, to evaluate the overhead of additional structures needed to model more complex molecules. Moreover, we performed a detailed analysis on the profiling of the simulation code and its execution flows due to the computation of the non-bonded forces. Finally, we characterized the acceleration and accuracy of the simulations on three GPUs having
different computation capabilities and parallelism, achieving one order of magnitude faster simulation execution times
Multiscale Modelling of Cellular Actin Filaments: From Atomistic Molecular to Coarse Grained Dynamics
No full textIn this article, we present a computational multiscale model for the characterization of subcellular proteins. The model is encoded inside a simulation tool that builds coarse-grained (CG) force fields from atomistic simulations. Equilibrium molecular dynamics simulations on an all-atom model of the actin filament are performed. Then, using the statistical distribution of the distances between pairs of selected groups of atoms at the output of the MD simulations, the force field is parameterized using the Boltzmann inversion approach. This CG force field is further used to characterize the dynamics of the protein via Brownian dynamics simulations. This combination of methods into a single computational tool flow enables the simulation of actin filaments with length up to 400 nm, extending the time and length scales compared to state-of-the-art approaches. Moreover, the proposed multiscale modeling approach allows to investigate the relationship between atomistic structure and changes on the overall dynamics and mechanics of the filament and can be easily (i) extended to the characterization of other subcellular structures and (ii) used to investigate the cellular effects of molecular alterations due to pathological conditions
Utilizing Machine Learning for Efficient Parameterization of Coarse Grained Molecular Force Fields
No full textExTASY: Scalable and flexible coupling of MD simulations and advanced sampling techniques
No full textTraining data, model, and results for "Computational tuning of the elastic properties of low- and high-entropy ultra-high temperature ceramics"
No full textThis dataset contains:
(i) training data, in plaintext xyz format, created via density functional theory simulations
(ii) weights for resulting trained MACE-UHTC model (finetuned from MACE-MPA0 as a starting point)
(iii) Results of simulations of the properties of group 4 and 5 transition metal rocksalt carbides using the trained model
which are described in the forthcoming article “Computational tuning of the elastic properties of low- and high-entropy ultra-high temperature ceramics”
If you use the model and/or any of the data, you must cite the associated preprint/article when available
