Multi-Architecture Monte-Carlo (MC) Simulation of Soft Coarse-Grained Polymeric Materials: SOft coarse grained Monte-carlo Acceleration (SOMA)

Multi-component polymer systems are important for the development of new materials because of their ability to phase-separate or self-assemble into nano-structures. The Single-Chain-in-Mean-Field (SCMF) algorithm in conjunction with a soft, coarse-grained polymer model is an established technique to investigate these soft-matter systems. Here we present an im- plementation of this method: SOft coarse grained Monte-carlo Accelera- tion (SOMA). It is suitable to simulate large system sizes with up to billions of particles, yet versatile enough to study properties of different kinds of molecular architectures and interactions. We achieve efficiency of the simulations commissioning accelerators like GPUs on both workstations as well as supercomputers. The implementa- tion remains flexible and maintainable because of the implementation of the scientific programming language enhanced by OpenACC pragmas for the accelerators. We present implementation details and features of the program package, investigate the scalability of our implementation SOMA, and discuss two applications, which cover system sizes that are difficult to reach with other, common particle-based simulation methods

Acceleration of Coarse Grain Molecular Dynamics on GPU Architectures

Coarse 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

Parallel stepwise stochastic simulation: Harnessing GPUs to Explore Possible Futures States of a Chromosome Folding Model Thanks to the Possible Futures Algorithm (PFA)

International audienceFor the sake of software compatibility, simulations are often parallelized withoutmuch code rewriting. Performances can be further improved by optimizing codes so that to use themaximum power offered by parallel architectures. While this approach can provide some speed-up,performance of parallelized codes can be strongly limited a priori because traditional algorithmshave been designed for sequential technologies. Thus, additional increase of performance shouldultimately rely on some redesign of algorithms.Here, we redesign an algorithm that has traditionally been used to simulate the folding proper-ties of polymers. We address the issue of performance in the context of biological applications,more particularly in the active field of chromosome modelling. Due to the strong confinementof chromosomes in the cells, simulation of their motion is slowed down by the laborious searchfor the next valid states to progress. Our redesign, that we call the Possible Futures Algorithm(PFA), relies on the parallel computation of possible evolutions of the same state, which effectivelyincreases the probability to obtain a valid state at each step. We apply PFA on a GPU-basedarchitecture, allowing us to optimally reduce the latency induced by the computation overhead ofpossible futures. We show that compared to the initial sequential model the acceptance rate of newstates significantly increases without impacting the execution time. In particular, the stronger theconfinement of the chromosome, the more efficient PFA becomes, making our approach appealingfor biological applications.While most of our results were obtained using Fermi architecture GPUs from NVIDIA, we highlightimproved performance on the cutting-edge Kepler architecture K20 GPUs

An a posteriori verification method for generalized real-symmetric eigenvalue problems in large-scale electronic state calculations

An a posteriori verification method is proposed for the generalized real-symmetric eigenvalue problem and is applied to densely clustered eigenvalue problems in large-scale electronic state calculations. The proposed method is realized by a two-stage process in which the approximate solution is computed by existing numerical libraries and is then verified in a moderate computational time. The procedure returns intervals containing one exact eigenvalue in each interval. Test calculations were carried out for organic device materials, and the verification method confirms that all exact eigenvalues are well separated in the obtained intervals. This verification method will be integrated into EigenKernel (https://github.com/eigenkernel/), which is middleware for various parallel solvers for the generalized eigenvalue problem. Such an a posteriori verification method will be important in future computational science.Comment: 15 pages, 7 figure