scholarly journals Computational estimation of ms-sec atomistic folding times

2018 ◽  
Author(s):  
Upendra Adhikari ◽  
Barmak Mostofian ◽  
Jeremy Copperman ◽  
Andrew Petersen ◽  
Daniel M. Zuckerman
Keyword(s):  
Graphics Processing Units ◽  
Gpu Computing ◽  
Md Simulations ◽  
Protein G ◽  
Initial Structure ◽  
Markov Analysis ◽  
Low Viscosity ◽  
Solvent Models ◽  
Massively Parallel Computing ◽  
Graphics Processing

Despite the development of massively parallel computing hardware including inexpensive graphics processing units (GPUs), it has remained infeasible to simulate the folding of atomistic proteins at room temperature using conventional molecular dynamics (MD) beyond the µs scale. Here we report the folding of atomistic, implicitly solvated protein systems with folding times τf ranging from ∼100 µs to ∼1s using the weighted ensemble (WE) strategy in combination with GPU computing. Starting from an initial structure or set of structures, WE organizes an ensemble of GPU-accelerated MD trajectory segments via intermittent pruning and replication events to generate statistically unbiased estimates of rate constants for rare events such as folding; no biasing forces are used. Although the variance among atomistic WE folding runs is significant, multiple independent runs are used to reduce and quantify statistical uncertainty. Folding times are estimated directly from WE probability flux and from history-augmented Markov analysis of the WE data. Three systems were examined: NTL9 at low solvent viscosity (yielding τf = 0.8 − 9.0 μs), NTL9 at water-like viscosity (τf = 0.2 − 1.9 ms), and Protein G at low viscosity (τf = 3.3 - 200 ms). In all cases the folding time, uncertainty, and ensemble properties could be estimated from WE simulation; for Protein G, this characterization required significantly less overall computing than would be required to observe a single folding event with conventional MD simulations. Our results suggest that the use and calibration of force fields and solvent models for precise estimation of kinetic quantities is becoming feasible.

scholarly journals Review of smoothed particle hydrodynamics: towards converged Lagrangian flow modelling

2020 ◽  
Vol 476 (2241) ◽  
pp. 20190801
Author(s):  
Steven J. Lind ◽  
Benedict D. Rogers ◽  
Peter K. Stansby
Keyword(s):  
Smoothed Particle Hydrodynamics ◽  
Graphics Processing Units ◽  
Wave Structure ◽  
Free Form ◽  
Mesh Free ◽  
Weakly Compressible ◽  
Particle Hydrodynamics ◽  
Massively Parallel Computing ◽  
Smoothed Particle ◽  
Graphics Processing

This paper presents a review of the progress of smoothed particle hydrodynamics (SPH) towards high-order converged simulations. As a mesh-free Lagrangian method suitable for complex flows with interfaces and multiple phases, SPH has developed considerably in the past decade. While original applications were in astrophysics, early engineering applications showed the versatility and robustness of the method without emphasis on accuracy and convergence. The early method was of weakly compressible form resulting in noisy pressures due to spurious pressure waves. This was effectively removed in the incompressible (divergence-free) form which followed; since then the weakly compressible form has been advanced, reducing pressure noise. Now numerical convergence studies are standard. While the method is computationally demanding on conventional processors, it is well suited to parallel processing on massively parallel computing and graphics processing units. Applications are diverse and encompass wave–structure interaction, geophysical flows due to landslides, nuclear sludge flows, welding, gearbox flows and many others. In the state of the art, convergence is typically between the first- and second-order theoretical limits. Recent advances are improving convergence to fourth order (and higher) and these will also be outlined. This can be necessary to resolve multi-scale aspects of turbulent flow.

Clustered Cell Parallelization for GPU Computing of Silicon Anisotropic Etching Simulation

2013 ◽  
Author(s):  
Jianhua Li ◽  
Jingyuan Chen ◽  
Yan Wang ◽  
Jianhua Huang
Keyword(s):  
Graphics Processing Units ◽  
Gpu Computing ◽  
Cell Model ◽  
Application Programming Interface ◽  
Simulation Method ◽  
Anisotropic Etching ◽  
Time Step ◽  
Ca Model ◽  
Application Programming ◽  
Graphics Processing

The parallelization of silicon anisotropic etching simulation with the cellular automata (CA) model on graphics processing units (GPUs) is challenging, because the numbers of computational tasks in etching simulation dynamically change and the existing parallel CA mechanisms do not fit in GPU computation well. In this paper, an improved CA model, called clustered cell model, is proposed for GPU-based etching simulation. The model consists of clustered cells, each of which manages a scalable number of atoms. In this model, only the etching and update of states for the atoms on the etching surface and their unexposed neighbors are performed at each CA time step, whereas the clustered cells are reclassified in a longer time step. With this model, a crystal cell parallelization method is given, where clustered cells are allocated to threads on GPUs in the simulation. With the optimizations from the spatial and temporal aspects as well as a proper granularity, this method provides a faster process simulation. The proposed simulation method is implemented with the Compute Unified Device Architecture (CUDA) application programming interface. Several computational experiments are taken to analyze the efficiency of the method.

USE OF MULTIPROCESSOR COMPUTING PLATFORM WITH HETEROGENEOUS ARCHITECTURE FOR SOLVING THE PROBLEMS OF HYDROACOUSTICS AND RADIOLOCATION

2018 ◽  
pp. 7-16 ◽  
Author(s):  
L. D. Baranov ◽  
V. N. Lobanov ◽  
M. I. Cheldiev
Keyword(s):  
Graphics Processing Units ◽  
Low Cost ◽  
Simulation Software ◽  
Actual Problem ◽  
Computing Systems ◽  
Computing Technique ◽  
Computing Platform ◽  
Massively Parallel Computing ◽  
Graphics Processing ◽  
Task Oriented

One of the main directions to increasing efficiency of computing systems related with making of heterogeneous platform which allow more effectively to use computing resources of conventional processors, graphics processing units and coprocessors based on FPGA for performance of massively-parallel computing oriented tasks. The creation of task-oriented solutions, allowing the user to configure computing technique for solving specific application tasks and giving a chance to quickly and with a low cost to reconfigure the system to another type of task is an actual problem. The domestic computing platform that can simultaneously use modules with different architectures in different configurations to solve a common problem is described in the article. The description and results of the simulation software aimed at solving problems in the field of hydroacoustics and radiolocation in order to implement the joint interaction of computing resources of different architecture and to assess the prospects of further application of the platform in resource-intensive applications are presented in the article.

scholarly journals dadi.CUDA: Accelerating population genetic inference with Graphics Processing Units

2020 ◽  
Author(s):  
Ryan N Gutenkunst
Keyword(s):  
Graphics Processing Units ◽  
Population Genetic ◽  
Gpu Computing ◽  
Graphics Processing Unit ◽  
Demographic History ◽  
Processing Unit ◽  
Speed Increase ◽  
Population Genetic Inference ◽  
Computationally Intensive ◽  
Graphics Processing

Extracting insight from population genetic data often demands computationally intensive modeling. dadi is a popular program for fitting models of demographic history and natural selection to such data. Here, I show that running dadi on a Graphics Processing Unit (GPU) can speed computation by orders of magnitude compared to the CPU implementation, with minimal user burden. This speed increase enables the analysis of more complex models, which motivated the extension of dadi to four- and five-population models. Remarkably, dadi performs almost as well on inexpensive consumer-grade GPUs as on expensive server-grade GPUs. GPU computing thus offers large and accessible benefits to the community of dadi users. This functionality is available in dadi version 2.1.0.

scholarly journals Use of GPU Computing for Uncertainty Quantification in Computational Mechanics: A Case Study

2011 ◽  
Vol 19 (4) ◽  
pp. 199-212 ◽  
Author(s):  
Gaurav ◽  
Steven F. Wojtkiewicz
Keyword(s):  
Parallel Computing ◽  
Uncertainty Quantification ◽  
Graphics Processing Units ◽  
Computational Mechanics ◽  
Gpu Computing ◽  
Single Instruction Multiple Data ◽  
Performance Constraints ◽  
Multiple Data ◽  
Graphics Processing

Graphics processing units (GPUs) are rapidly emerging as a more economical and highly competitive alternative to CPU-based parallel computing. As the degree of software control of GPUs has increased, many researchers have explored their use in non-gaming applications. Recent studies have shown that GPUs consistently outperform their best corresponding CPU-based parallel computing alternatives in single-instruction multiple-data (SIMD) strategies. This study explores the use of GPUs for uncertainty quantification in computational mechanics. Five types of analysis procedures that are frequently utilized for uncertainty quantification of mechanical and dynamical systems have been considered and their GPU implementations have been developed. The numerical examples presented in this study show that considerable gains in computational efficiency can be obtained for these procedures. It is expected that the GPU implementations presented in this study will serve as initial bases for further developments in the use of GPUs in the field of uncertainty quantification and will (i) aid the understanding of the performance constraints on the relevant GPU kernels and (ii) provide some guidance regarding the computational and the data structures to be utilized in these novel GPU implementations.

scholarly journals Simulating Spiking Neural P Systems Without Delays Using GPUs

2014 ◽  
pp. 109-121 ◽  
Author(s):  
F. Cabarle ◽  
H. Adorna ◽  
M. A. Martínez-del-Amor
Keyword(s):  
Video Processing ◽  
Graphics Processing Units ◽  
Gpu Computing ◽  
Parallel Architecture ◽  
General Purpose ◽  
P Systems ◽  
P System ◽  
Spiking Neural P Systems ◽  
Computationally Intensive ◽  
Graphics Processing

In this paper, the authors discuss the simulation of a P system variant known as Spiking Neural P systems (SNP systems), using Graphics Processing Units (GPUs). GPUs are well suited for highly parallel computations because of their intentional and massively parallel architecture. General purpose GPU computing has seen the use of GPUs for computationally intensive applications, not just in graphics and video processing. P systems, including SNP systems, are maximally parallel computing models taking inspiration from the functioning and dynamics of a living cell. In particular, SNP systems take inspiration from a type of cell known as a neuron. The nature of SNP systems allowed for their representation as matrices, which is an elegant step toward their simulation on GPUs. In this paper, the simulation algorithms, design considerations, and implementation are presented. Finally, simulation results, observations, and analyses using a simple but non-trivial SNP system as an example are discussed, including recommendations for future work.

Parallel Population Based Incremental Learning With GPU Acceleration for Nonlinear Optimization

2009 ◽  
Author(s):  
Weihang Zhu
Keyword(s):  
Incremental Learning ◽  
Graphics Processing Units ◽  
Gpu Computing ◽  
Optimization Problems ◽  
Population Based ◽  
Pattern Search ◽  
Processing Unit ◽  
Central Processing ◽  
Computing Platform ◽  
Graphics Processing

This paper presents a GPU-based parallel Population Based Incremental Learning (PBIL) algorithm with a local search on bound constrained optimization problems. The genotype of an entire population is evolved in PBIL, which was derived from Genetic Algorithms. Graphics Processing Units (GPU) is an emerging technology for desktop parallel computing. In this research, the classical PBIL is adapted in the data-parallel GPU computing platform. The global optimal search of the PBIL is enhanced by a local Pattern Search method. The hybrid PBIL method is implemented in the GPU environment, and compared to a similar implementation in the common computing environment with a Central Processing Unit (CPU). Computational results indicate that GPU-accelerated PBIL method is effective and faster than the corresponding CPU implementation.

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