Arbre jeràrquic i gràfic de tesis doctorals dirigides per la Professora Rosa Maria Badia Sala

Aquest informe mostra les 18 tesis doctorals dirigides per Rosa Maria Badia Sala, així com les tesis doctorals dirigides pels investigadors que han tingut a Rosa Maria Badia Sala com a directora de tesis.Postprint (author's final draft

Arbre jeràrquic i gràfic de tesis doctorals dirigides pel Professor Eduard Ayguadé Parra

Aquest informe mostra les 30 tesis doctorals dirigides per Eduard Ayguadé Parra, així com les tesis doctorals dirigides pels investigadors que han tingut a Eduard Ayguadé Parra com a director de tesis.Postprint (published version

OmpSs-OpenCL programming model for heterogeneous systems

The advent of heterogeneous computing has forced programmers to use platform specific programming paradigms in order to achieve maximum performance. This approach has a steep learning curve for programmers and also has detrimental influence on productivity and code re-usability. To help with this situation, OpenCL an open-source, parallel computing API for cross platform computations was conceived. OpenCL provides a homogeneous view of the computational resources (CPU and GPU) thereby enabling software portability across different platforms. Although OpenCL resolves software portability issues, the programming paradigm presents low programmability and additionally falls short in performance. In this paper we focus on integrating OpenCL framework with the OmpSs task based programming model using Nanos run time infrastructure to address these shortcomings. This would enable the programmer to skip cumbersome OpenCL constructs including OpenCL plaform creation, compilation, kernel building, kernel argument setting and memory transfers, instead write a sequential program with annotated pragmas. Our proposal mainly focuses on how to exploit the best of the underlying hardware platform with greater ease in programming and to gain significant performance using the data parallelism offered by the OpenCL run time for GPUs and multicore architectures. We have evaluated the platform with important benchmarks and have noticed substantial ease in programming with comparable performance.Postprint (published version

OmpSs-OpenCL programming model for heterogeneous systems

The advent of heterogeneous computing has forced programmers to use platform specific programming paradigms in order to achieve maximum performance. This approach has a steep learning curve for programmers and also has detrimental influence on productivity and code re-usability. To help with this situation, OpenCL an open-source, parallel computing API for cross platform computations was conceived. OpenCL provides a homogeneous view of the computational resources (CPU and GPU) thereby enabling software portability across different platforms. Although OpenCL resolves software portability issues, the programming paradigm presents low programmability and additionally falls short in performance. In this paper we focus on integrating OpenCL framework with the OmpSs task based programming model using Nanos run time infrastructure to address these shortcomings. This would enable the programmer to skip cumbersome OpenCL constructs including OpenCL plaform creation, compilation, kernel building, kernel argument setting and memory transfers, instead write a sequential program with annotated pragmas. Our proposal mainly focuses on how to exploit the best of the underlying hardware platform with greater ease in programming and to gain significant performance using the data parallelism offered by the OpenCL run time for GPUs and multicore architectures. We have evaluated the platform with important benchmarks and have noticed substantial ease in programming with comparable performance