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This thesis describes an environment to evaluate and compare static schedulers for real pipelined streaming applications on massively parallel architectures, such as Intel Single chip Cloud Computer (SCC), Adapteva Epiphany, and Tilera TILE-Gx series. The framework allows performance comparison of schedulers in their execution time, or the energy usage of static schedules with energy models and measurements on real platform. This thesis focuses on the implementation of a framework evaluating the energy consumption of such streaming applications on the SCC. The framework can run streaming applications, built as task collections, with static schedules including dynamic frequency scaling. Streams are handled by the framework with FIFO buffers, connected between tasks. We evaluate the framework by considering a pipelined mergesort implementation with different static schedules. The runtime is compared with the runtime of a previously published task based optimized mergesort implementation. The results show how much overhead the framework adds on to the streaming application. As a demonstration of the energy measuring capabilities, we schedule and analyze a Fast Fourier Transform application, and discuss the results. Future work may include quantitative comparative studies of a range of different static schedulers. This has, to our knowledge, not been done previously.

This thesis describes an environment to evaluate and compare static schedulers for real pipelined streaming applications on massively parallel architectures, such as Intel Single chip Cloud Computer (SCC), Adapteva Epiphany, and Tilera TILE-Gx series. The framework allows performance comparison of schedulers in their execution time, or the energy usage of static schedules with energy models and measurements on real platform. This thesis focuses on the implementation of a framework evaluating the energy consumption of such streaming applications on the SCC. The framework can run streaming applications, built as task collections, with static schedules including dynamic frequency scaling. Streams are handled by the framework with FIFO buffers, connected between tasks. We evaluate the framework by considering a pipelined mergesort implementation with different static schedules. The runtime is compared with the runtime of a previously published task based optimized mergesort implementation. The results show how much overhead the framework adds on to the streaming application. As a demonstration of the energy measuring capabilities, we schedule and analyze a Fast Fourier Transform application, and discuss the results. Future work may include quantitative comparative studies of a range of different static schedulers. This has, to our knowledge, not been done previously.