Parallel Sparse Supports for Array Intrinsic Functions of Fortran 90

Fortran 90 provides a rich set of array intrinsic functions. Each of these array intrinsic functions operates on the elements of multi-dimensional array objects concurrently. They provide a rich source of parallelism and play an increasingly important role in automatic support of data parallel programming. However, there is no such support if these intrinsic functions are applied to sparse data sets. In this paper, we address this open gap by presenting an efficient library for parallel sparse computations with Fortran 90 array intrinsic operations. Our method provides both compression schemes and distribution schemes on distributed memory environments applicable to higher-dimensional sparse arrays. This way, programmers need not worry about low-level system details when developing sparse applications. Sparse programs can be expressed concisely using array expressions, and parallelized with the help of our library. Our sparse libraries are built for array intrinsics of Fortran 90, and they include an extensive set of array operations such as CSHIFT, EOSHIFT, MATMUL, MERGE, PACK, SUM, RESHAPE, SPREAD, TRANSPOSE, UNPACK, and section moves. Our work, to our best knowledge, is the first work to give sparse and parallel sparse supports for array intrinsics of Fortran 90. In addition, we provide a complete complexity analysis for our sparse implementation. The complexity of our algorithms is in proportion to the number of nonzero elements in the arrays, and that is consistent with the conventional design criteria for sparse algorithms and data structures. Our current testbed is an IBM SP2 workstation cluster. Preliminary experimental results with numerical routines, numerical applications, and data-intensive applications related to OLAP (on-line analytical processing) show that our approach is promising in speeding up sparse matrix computations on both sequential and distributed memory environments if the programs are expressed with Fortran 90 array expressions.     

Building GML-native web-based geographic information systems

Disaster response systems are designed to facilitate decision-making based on large amounts of heterogeneous geographic information. Most geographic information systems (GISs) use relational databases to manipulate information efficiently. However, they suffer from interoperability issues because they need to expend significant effort mapping heterogeneous geographic information, which may have complicated structures, into relational data models, and vice versa. Geography Markup Language (GML) is regarded as a standard for expressing, storing, and exchanging geospatial data, and has been applied to help solve interoperability problems. Interestingly, no GIS has been built on native XML/GML technologies so far. There are two possible reasons for this: current XML processors are incapable of processing geospatial information, and they are inefficient in manipulating large XML documents. In this paper, we resolve these two difficulties and move forward to realizing GML-native web-based geographic information systems.