Development of Parallel Algorithms in Data Field Haskell

. Data fields provide a flexible and highly general model for indexed collections of data. Data Field Haskell is a dialect of the functional language Haskell which provides an instance of data fields. We describe Data Field Haskell and exemplify how it can be used in the early phase of parallel program design. 1 Introduction Many computing applications require indexed data structures. The canonical indexed data structure is the array. However, for sparse, distributed applications, other, more dynamic indexed data structures are needed. It is desirable to develop such algorithms on a high level first, in order to get them right, since the low level data representations can be intricate. Data Field Haskell provides an instance of data fields -- a data type for general indexed structures. This Haskell dialect can be used for rapid prototyping of parallel computational algorithms which may involve sparse structures. Various versions of the data field model have been described elsew..

Median separators in d dimensions

Computer Science Departmen

Implementation of a Portable Nested Data-Parallel Language

This paper gives an overview of the implementation of NESL, a portable nested data-parallel language. This language and its implementation are the first to fully support nested data structures as well as nested data-parallel function calls. These features allow the concise description of parallel algorithms on irregular data structures, such as sparse matrices and graphs. In addition, they maintain the advantages of data-parallel languages: a simple programming model and portability. The current NESL implementation is based on an intermediate language called VCODE and a library of vector routines called CVL. It runs on the Connection Machines CM-2 and CM-5, the Cray C90, and serial workstations. We compare initial benchmark results of NESL with those of machine-specific code on these machines for three algorithms: least-squares linefitting, median finding, and a sparse-matrix vector product. These results show that NESL's performance is competitive with that of machine-specific code for regular dense data, and is often superior for irregular data