This white paper reports on some of the issues in developing data-intensive applications in the Grid environment. In the context of this paper, data-intensive applications are those that explore, analyse, visualize, otherwise manipulate large scientific datasets. The paper presents several

Most high-level synthesis tools focus on exploiting parallelism. However, for data-intensive applications the memory bandwidth or latency may become a bottleneck and improving data access patterns becomes as important as exploiting parallelism. To minimize

The cyberinfrastructure supporting science will include large-scale simulation systems headed to exascale combined with cloud like systems supporting data intensive and high throughput computing, pleasingly parallel jobs and the long tail of science. Clouds offer economies of scale, elasticity

Today’s scientists are quickly moving from in vitro to in silico experimentation: they no longer analyze natural phenomena in a petri dish, but instead they build models and simulate them. Managing and analyzing the massive amounts of data involved in simulations is a major task. Yet, they lack

HAL is a multi-disciplinary open access archive for the deposit and dissemination of sci-entific research documents, whether they are pub-lished or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L’archive ouverte pluridisciplinaire HAL, est destinée au dépôt et a ̀ la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d’enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Advances in computational, storage and network technologies as well as middle ware such as the Globus Toolkit allow scientists to expand the sophistication and scope of data-intensive applications.

Abstract. High disk bandwidth in data-intensive applications is usually achieved with expensive hardware solutions consisting of a large number of disks. In this article we present our current work on software methods for improving disk bandwidth in ColumnBM, a new storage system for MonetDB/X100

The computational grid is becoming the platform of choice for large-scale distributed data-intensive applications. Accurately predicting the transfer times of remote data les, a fundamental component of such applications, is critical to achieving application performance. In this paper, we introduce

Since large-scale and data-intensive applications have been widely deployed, there is a growing demand for high-performance storage systems to support data-intensive applications. Compared with traditional storage systems, next-generation systems will embrace dedicated processor to reduce

S characteristics. In this paper we are interested on the Data-Intensive applications where the QoS attributes are very important for the reliability and performance of these systems. I.