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Fortran is still widely used in scientific computing, and a very large corpus of legacy as well as new code is written in FORTRAN 77. In general this code is not type safe, so that incorrect programs can compile without errors. In this paper, we present a formal approach to ensure type safety of legacy Fortran code through automated program transformation. The objective of this work is to reduce programming errors by guaranteeing type safety. We present the first rigorous analysis of the type safety of FORTRAN 77 and the novel program transformation and type checking algorithms required to convert FORTRAN 77 subroutines and functions into pure, side-effect free subroutines and functions in Fortran 90. We have implemented these algorithms in a source-to-source compiler which type checks and automatically transforms the legacy code. We show that the resulting code is type safe and that the pure, side-effect free and referentially transparent subroutines can readily be offloaded to accelerators. [ABSTRACT FROM AUTHOR]

In this paper we present a novel approach to program optimisation based on compiler-based type-driven program transformations and a fast and accurate cost/performance model for the target architecture. We target streaming programs for the problem domain of scientific computing, such as numerical weather prediction. We present our theoretical framework for type-driven program transformation, our target high-level language and intermediate representation languages and the cost model and demonstrate the effectiveness of our approach by comparison with a commercial toolchain. [ABSTRACT FROM AUTHOR]

In a general-purpose computing system, several parallel applications run simultaneously on the same platform. Even if each application is highly tuned for that specific platform, additional performance issues are arising in such a dynamic environment in which multiple applications compete for the resources. Different scheduling and resource management techniques have been proposed either at operating system or user level to improve the performance of concurrent workloads. In this paper, we propose a task-based strategy called 'Steal Locally, Share Globally' implemented in the runtime of our parallel programming model GPRM (Glasgow Parallel Reduction Machine). We have chosen a state-of-the-art manycore parallel machine, the Intel Xeon Phi, to compare GPRM with some well-known parallel programming models, OpenMP, Intel Cilk Plus and Intel TBB, in both single-programming and multiprogramming scenarios. We show that GPRM not only performs well for single workloads, but also outperforms the other models for multiprogramming workloads. There are three considerations regarding our task-based scheme: (i) It is implemented inside the parallel framework, not as a separate layer; (ii) It improves the performance without the need to change the number of threads for each application (iii) It can be further tuned and improved, not only for the GPRM applications, but for other equivalent parallel programming models. [ABSTRACT FROM AUTHOR]