Harvesting graphics power for MD simulations

We discuss an implementation of molecular dynamics (MD) simulations on a graphic processing unit (GPU) in the NVIDIA CUDA language. We tested our code on a modern GPU, the NVIDIA GeForce 8800 GTX. Results for two MD algorithms suitable for short-ranged and long-ranged interactions, and a congruential shift random number generator are presented. The performance of the GPU's is compared to their main processor counterpart. We achieve speedups of up to 80, 40 and 150 fold, respectively. With newest generation of GPU's one can run standard MD simulations at 10^7 flops/$.Comment: 12 pages, 5 figures. Submitted to Mol. Si

A Framework for Megascale Agent Based Model Simulations on Graphics Processing Units

Agent-based modeling is a technique for modeling dynamic systems from the bottom up. Individual elements of the system are represented computationally as agents. The system-level behaviors emerge from the micro-level interactions of the agents. Contemporary state-of-the-art agent-based modeling toolkits are essentially discrete-event simulators designed to execute serially on the Central Processing Unit (CPU). They simulate Agent-Based Models (ABMs) by executing agent actions one at a time. In addition to imposing an un-natural execution order, these toolkits have limited scalability. In this article, we investigate data-parallel computer architectures such as Graphics Processing Units (GPUs) to simulate large scale ABMs. We have developed a series of efficient, data parallel algorithms for handling environment updates, various agent interactions, agent death and replication, and gathering statistics. We present three fundamental innovations that provide unprecedented scalability. The first is a novel stochastic memory allocator which enables parallel agent replication in O(1) average time. The second is a technique for resolving precedence constraints for agent actions in parallel. The third is a method that uses specialized graphics hardware, to gather and process statistical measures. These techniques have been implemented on a modern day GPU resulting in a substantial performance increase. We believe that our system is the first ever completely GPU based agent simulation framework. Although GPUs are the focus of our current implementations, our techniques can easily be adapted to other data-parallel architectures. We have benchmarked our framework against contemporary toolkits using two popular ABMs, namely, SugarScape and StupidModel.GPGPU, Agent Based Modeling, Data Parallel Algorithms, Stochastic Simulations

Computational Physics on Graphics Processing Units

The use of graphics processing units for scientific computations is an emerging strategy that can significantly speed up various different algorithms. In this review, we discuss advances made in the field of computational physics, focusing on classical molecular dynamics, and on quantum simulations for electronic structure calculations using the density functional theory, wave function techniques, and quantum field theory.Comment: Proceedings of the 11th International Conference, PARA 2012, Helsinki, Finland, June 10-13, 201

Image Processing on the GPU: Implementing the Canny Edge Detection Algorithm

Abstract: In this paper we present a detailed Graphics Processing Unit (GPU)-based implementation of the well known Canny edge detection algorithm. The aim of the paper is to provide an overview on our approach to implement the Canny edge detection algorithm, as it encompasses a set of image processing techniques. The result is an algorithm that can be applied in real-time applications..

Just in time calculations for partially coherent imaging

Algorithms to calculate partially coherent image formation are centered around the classic formula developed by Hopkins. Algebraic methods have been developed to make feasible the integration process, although with strong limitations in the complex degrees of coherence used. In this work we revisit the formula laid by Hopkins and implement it to take full advantage of the parallelization given by Graphics Processing Units. Execution times are compared and the simulations compared with experimental results

The inherent overlapping in the parallel calculation of the Laplacian

Producción CientíficaA new approach for the parallel computation of the Laplacian in the Fourier domain is presented. This numerical problem inherits the intrinsic sequencing involved in the calculation of any multidimensional Fast Fourier Transform (FFT) where blocking communications assure that its computation is strictly carried out dimension by dimension. Such data dependency vanishes when one considers the Laplacian as the sum of n independent one-dimensional kernels, so that computation and communication can be naturally overlapped with nonblocking communications. Overlapping is demonstrated to be responsible for the speedup figures we obtain when our approach is compared to state-of-the-art parallel multidimensional FFTs.Junta de Castilla León (grant number VA296P18

A Dual Stealthy Backdoor: From Both Spatial and Frequency Perspectives

Backdoor attacks pose serious security threats to deep neural networks (DNNs). Backdoored models make arbitrarily (targeted) incorrect predictions on inputs embedded with well-designed triggers while behaving normally on clean inputs. Many works have explored the invisibility of backdoor triggers to improve attack stealthiness. However, most of them only consider the invisibility in the spatial domain without explicitly accounting for the generation of invisible triggers in the frequency domain, making the generated poisoned images be easily detected by recent defense methods. To address this issue, in this paper, we propose a DUal stealthy BAckdoor attack method named DUBA, which simultaneously considers the invisibility of triggers in both the spatial and frequency domains, to achieve desirable attack performance, while ensuring strong stealthiness. Specifically, we first use Discrete Wavelet Transform to embed the high-frequency information of the trigger image into the clean image to ensure attack effectiveness. Then, to attain strong stealthiness, we incorporate Fourier Transform and Discrete Cosine Transform to mix the poisoned image and clean image in the frequency domain. Moreover, the proposed DUBA adopts a novel attack strategy, in which the model is trained with weak triggers and attacked with strong triggers to further enhance the attack performance and stealthiness. We extensively evaluate DUBA against popular image classifiers on four datasets. The results demonstrate that it significantly outperforms the state-of-the-art backdoor attacks in terms of the attack success rate and stealthinessComment: 10 pages, 7 figures. Submit to ACM MM 202