Molecular dynamics (MD) is a powerful computational method for simulating molecular behavior. Deep neural networks provide a novel method of generating MD data efficiently, but there is no architecture that mitigates the well-known exposure bias accumulated by multi-step generations. In this paper, we propose a multi-step time series generator using a deep neural network based on Wasserstein generative adversarial nets. Instead of sparse real data, our model evolves a latent variable z that is densely distributed in a low-dimensional space. This novel framework successfully mitigates the exposure bias. Moreover, our model can evolve part of the system (Feature extraction) with any time step (Step skip), which accelerates the efficient generation of MD data. The applicability of this model is evaluated through three different systems: harmonic oscillator, bulk water, and polymer melts. The experimental results demonstrate that our model can generate time series of the MD data with sufficient accuracy to calculate the physical and important dynamical statistics.

Electrostatic potentials (ESPs) are frequently used in structural biology for the characterization of biomolecules. Here we study the potential employment of hardware accelerators like the graphics processing unit or the application-specific integrated circuit MD-GRAPE-3 for the purpose of efficient computation of ESPs. An algorithm closely coupled to the general description of molecular surfaces is ported to both specialized architectures. The high-level interface library MR1/3 is used, which greatly simplifies the porting process. Hardware-accelerated versions show significant Speed-Up factors reaching values of up to 27×. Once ESP computations have become a matter of seconds, the underlying application can be offered in the form of a web service. © The Author 2009. Published by Oxford University Press on behalf of The British Computer Society. All rights reserved.

As an entry for the 2009 Gordon Bell price/performance prize, we present the results of two different hierarchical N-body simulations on a cluster of 256 graphics processing units (GPUs). Unlike many previous N-body simulations on GPUs that scale as O(N2), the present method calculates the O(N log N) treecode and O(N) fast multipole method (FMM) on the GPUs with unprecedented efficiency. We demonstrate the performance of our method by choosing one standard application --a gravitational N-body simulation-- and one non-standard application --simulation of turbulence using vortex particles. The gravitational simulation using the treecode with 1,608,044,129 particles showed a sustained performance of 42.15 TFlops. The vortex particle simulation of homogeneous isotropic turbulence using the periodic FMM with 16,777,216 particles showed a sustained performance of 20.2 TFlops. The overall cost of the hardware was 228,912 dollars. The maximum corrected performance is 28.1TFlops for the gravitational simulation, which results in a cost performance of 124 MFlops/$. This correction is performed by counting the Flops based on the most efficient CPU algorithm. Any extra Flops that arise from the GPU implementation and parameter differences are not included in the 124 MFlops/$.

Molecular Dynamics (MD) simulation requires huge computational power, as each atom interacts with the others by long range forces such as the Coulomb or van der Waals forces. Recently, a video game computer, such as SONY PLAYSTATION 3 (PS3) or NVIDIApsilas Graphics Processing Unit (GPU) has become a candidate hardware for accelerating MD simulations as well as an MDGRAPE-3 special-purpose computer for their better performance than current CPU of the PC, and also for their cost-effectiveness. Especially the latest GPU has much more peak performance than a CPU of the PC or an MDGRAPE-3, though a GPU has much more overheads in accelerating MD simulations. When the number of particles is small or the calculation kernel becomes complicated, the performance of the GPU drops dramatically as low as that of the MDGRAPE-3. However, the acceleration ratio of the GPU and the PS3 per cost exceeds that of the MDGRAPE-3.

We performed molecular dynamics (MD) simulation of 9 million pairs of NaCl ions with the Ewald summation and obtained a calculation speed of 1.34 Tflops. In this calculation we used a special-purpose computer, MDM, which we are developing for the calculations of the Coulomb and van der Waals forces. The MDM enabled us to perform large scale MD simulations without truncating the Coulomb force. It is composed of WINE-2, MDGRAPE-2 and a host computer. WINE-2 accelerates the calculation for wavenumber-space part of the Coulomb force, while MDGRAPE-2 accelerates the calculation for real-space part of the Coulomb and van der Waals forces. The host computer performs other calculations. We performed MD simulation with the early version of the MDM system: 45 Tflops of WINE-2 and 1 Tflops of MDGRAPE-2. The peak performance of the final MDM system will reach 75 Tflops in total by the end of the year 2000.