Tag: Molecular Dynamics
Speed without compromise: A mixed precision model for GPU accelerated molecular dynamics simulations
We present an implementation for NVIDIA GPUs of both generalized Born implicit solvent simulations as well as explicit solvent simulations using the particle mesh Ewald (PME) algorithm for long-range electrostatics
This paper presented a computational methodology to carry out three-dimensional, massively parallel Smoothed Particle Hydrodynamics (SPH) simulations across multiple GPUs
Computational biophysics research group of Professor Samuel Cho from Wake Forest University developed a novel parallel Verlet neighbor list algorithm for performing coarse-grained MD simulations of biologically relevant systems.
The simulations, run using graphical processors (GPUs), were used to investigate the effect of conformational change upon binding of the NA inhibitors oseltamivir and zanamivir in the wild-type and the IR and IRHY mutant strains
Heterogeneous systems with both Central Processing Units (CPUs) and Graphics Processing Units (GPUs) are frequently used to accelerate short-ranged Molecular Dynamics (MD) simulations. The most time-consuming task in short-ranged MD simulations is the computation of particle-to-particle interactions. Beyond a certain distance, these interactions decrease to zero. To minimize the operations to investigate distance, previous works…
An efficient and highly scalable bond-order potential code has been developed for the molecular dynamics simulation of bulk silicon, reaching 1.87 Pflops in single precision on 7168 graphic processing units (GPUs) of the Tianhe-1A system.
We have provided a hybrid massively parallelized molecular dynamic VASP ab initio software for GPUs clusters. To avoid continuously transferring data from CPUs to GPUs, we have ported some functions in CUDA and achieved a balanced combination between CUFFT, CUBLAS, and CUDA.
By studying many current GPU computing applications, we have learned that the limits of an application’s scalability are often related to some combination of memory bandwidth saturation, memory contention, imbalanced data distribution, or data structure/algorithm interactions.