Abstract
The emergence of applications with greater processing and speedup requirements, such as Grand Challenge Applications (GCA), involves new computing and I/O needs. Many of these applications require access to huge data repositories and other I/O sources, being the I/O phase a bottleneck in the computing systems, due to its poor performance. In this sense, parallel I/O is becoming one of the major topics in the area of high-performance systems. Existing data-intensive GCA have been used in several domains, such as high energy physics, climate modeling, biology or visualization. The I/O problem is not solved in this kind of applications. New approaches are required in this scene. This paper presents MAPFS, a multiagent architecture, whose goal is to allow applications to access data in a cluster of workstations in an efficient and flexible fashion, providing formalisms for modifying the topology of the storage system, specifying different data access patterns and selecting additional functionalities.
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