Scientific middleware for abstracted parallelisation.April 2006
In this paper we introduce a class of problems that arise when the analysis of data split into an unknown number of pieces is attempted. Such analysis falls under the definition of Grid computing, but fails to be addressed by the current Grid computing projects, as they do not provide the appropriate abstractions. We then describe a distributed web service based middleware platform, which solves these problems by supporting construction of parallel data analysis functions for datasets with an unknown level of distribution. This analysis is achieved through the combination of Martlet, a workflow language that uses constructs from functional programming to abstract the parallelisation in computations away from the user, and the construction of supporting middleware. To construct such a supporting middleware it is necessary to provide the capability to reason about the data structures held without restricting their nature. Issues covered in the development of this supporting middleware include the ability to handle distributed data transfer and management, function deployment and execution.
Authors: Daniel Goodman
Venue: Oxford University Computing Laboratory