Robert G. Belleman

High performance direct gravitational N-body simulations on graphics processing units II: An implementation in CUDA

We present the results of gravitational direct N-body simulations using the graphics processing unit (GPU) on a commercial NVIDIA GeForce 8800GTX designed for gaming computers. The force evaluation of the N-body problem is implemented in ‘‘Compute Unified Device Architecture’’ (CUDA) using the GPU to speedup the calculations. We tested the implementation on three different N-body codes: two direct N-body integration codes, using the 4th order predictor–corrector Hermite integrator with block time-steps, and one Barnes-Hut treecode, which uses a 2nd order leapfrog integration scheme. The integration of the equations of motions for all codes is performed on the host CPU. We find that for N > 512 particles the GPU outperforms the GRAPE-6Af, if some softening in the force calculation is accepted. Without softening and for very small integration time-steps the GRAPE still outperforms the GPU. We conclude that modern GPUs offer an attractive alternative to GRAPE-6Af special purpose hardware. Using the same time-step criterion, the total energy of the N-body system was conserved better than to one in 10 6 on the GPU, only about an order of magnitude worse than obtained with GRAPE-6Af. For N J 10 5 the 8800GTX outperforms the host CPU by a factor of about 100 and runs at about the same speed as the GRAPE-6Af.

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 GPUs is compared to their main processor counterpart. We achieve speedups of up to 40, 80 and 150 fold, respectively. With the latest generation of GPUs one can run standard MD simulations at 107 flops/

Flood early warning system: design, implementation and computational modules

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VLAM-G: A Grid-based virtual laboratory

We present a prototype of the flood early warning system (EWS) developed within the UrbanFlood FP7 project. The system monitors sensor networks installed in flood defenses (dikes, dams, embankments, etc.), detects sensor signal abnormalities, calculates dike failure probability, and simulates possible scenarios of dike breaching and flood propagation. All the relevant information and simulation results are fed into an interactive decision support system that helps dike managers and city authorities to make informed decisions in case of emergency and in routine dike quality assessment. In addition to that, a Virtual Dike computational module has been developed for advanced research into dike stability and failure mechanisms, and for training the artificial intelligence module on signal parameters induced by dike instabilities. This paper describes the UrbanFlood EWS generic design and functionality, the computational workflow, the individual modules, their integration via the Common Information Space middleware, and the first results of EWS monitoring and performance benchmarks.

Simulated vascular reconstruction in a virtual operating theatre

The Grid-based Virtual Laboratory AMsterdam (VLAM-G), provides a science portal for distributed analysis in applied scientific research. It offers scientists remote experiment control, data management facilities and access to distributed resources by providing cross-institutional integration of information and resources in a familiar environment. The main goal is to provide a unique integration of existing standards and software packages. This paper describes the design and prototype implementation of the VLAM-G platform. In this testbed we applied several recent technologies such as the Globus toolkit, enhanced federated database systems, and visualization and simulation techniques. Several domain specific case studies are described in some detail. Information management will be discussed separately in a forthcoming paper.

Integrated Support for Medical Image Analysis Methods: From Development to Clinical Application

Abstract We present an experimentation environment that combines interactive visualisation of patient-specific vascular medical data with a flow simulation environment into an interactive exploration environment that provides a virtual operating theatre in which vascular reconstruction procedures can be simulated.

A multi-modal interface for an interactive simulated vascular reconstruction system

Computer-aided image analysis is becoming increasingly important to efficiently and safely handle large amounts of high-resolution images generated by advanced medical imaging devices. The development of medical image analysis (MIA) software with the required properties for clinical application, however, is difficult and labor-intensive. Such development should be supported by systems providing scalable computational capacity and storage space, as well as information management facilities. This paper describes the properties of distributed systems to support and facilitate the development, evaluation, and clinical application of MIA methods. First, the main characteristics of existing systems are presented. Then, the phases in a methods lifecycle are analyzed (development, parameter optimization, evaluation, clinical routine), identifying the types of users, tasks, and related computational issues. A scenario is described where all tasks are performed with the aid of computational tools integrated into an ideal supporting environment. The requirements for this environment are described, proposing a grid-oriented paradigm that emphasizes virtual collaboration among users, pieces of software, and devices distributed among geographically dispersed healthcare, research, and development enterprises. Finally, the characteristics of the existing systems are analyzed according to these requirements. The proposed requirements offer a useful framework to evaluate, compare, and improve the existing systems that support MIA development

The Technologically Integrated Oncosimulator: Combining Multiscale Cancer Modeling With Information Technology in the In Silico Oncology Context

This paper is devoted to multi-modal interface design and implementation of a simulated vascular reconstruction system. It provides multi-modal interaction methods such as speech recognition, hand gestures, direct manipulation of virtual 3D objects and measurement tools. The main challenge is that no general interface scenario in existence today can satisfy all the users of the system (radiologists, vascular surgeons, medical students, etc.). The potential users of the system can vary by their skills, expertise level, habits and psycho-motional characteristics. To make a multimodal interface user-friendly is a crucial issue. In this paper we introduce an approach to develop such an efficient, user-friendly multi-modal interaction system. We focus on adaptive interaction as a possible solution to address the variety of end-users. Based on a user model, the adaptive user interface identifies each individual by means of a set of criteria and generates a customized exploration environment.

The Polder Computing Environment: a system for interactive distributed simulation

This paper outlines the major components and function of the technologically integrated oncosimulator developed primarily within the Advancing Clinico Genomic Trials on Cancer (ACGT) project. The Oncosimulator is defined as an information technology system simulating in vivo tumor response to therapeutic modalities within the clinical trial context. Chemotherapy in the neoadjuvant setting, according to two real clinical trials concerning nephroblastoma and breast cancer, has been considered. The spatiotemporal simulation module embedded in the Oncosimulator is based on the multiscale, predominantly top-down, discrete entity-discrete event cancer simulation technique developed by the In Silico Oncology Group, National Technical University of Athens. The technology modules include multiscale data handling, image processing, invocation of code execution via a spreadsheet-inspired environment portal, execution of the code on the grid, and the visualization of the predictions. A refining scenario for the eventual coupling of the oncosimulator with immunological models is also presented. Parameter values have been adapted to multiscale clinical trial data in a consistent way, thus supporting the predictive potential of the oncosimulator. Indicative results demonstrating various aspects of the clinical adaptation and validation process are presented. Completion of these processes is expected to pave the way for the clinical translation of the system.

GEOPROVE: Geometric Probes for Virtual Environments

The paper provides an overview of an experimental, Grid‐like computing environment, Polder, and its components. Polder offers high‐performance computing and interactive simulation facilities to computational science. It was successfully implemented on a wide‐area cluster system, the Distributed ASCI Supercomputer. An important issue is an efficient management of resources, in particular multi‐level scheduling and migration of tasks that use PVM or sockets. The system can be applied to interactive simulation, where a cluster is used for high‐performance computations, while a dedicated immersive interactive environment (CAVE) offers visualization and user interaction. Design considerations for the construction of dynamic exploration environments using such a system are discussed, in particular the use of intelligent agents for coordination. A case study of simulatedabdominal vascular reconstruction is subsequently presented: the results of computed tomography or magnetic resonance imaging of a patient are displayed in CAVE, and a surgeon can evaluate the possible treatments by performing the surgeries virtually and analysing the resulting blood flow which is simulated using the lattice‐Boltzmann method. Copyright