The trend in computing systems is to combine various kinds of processing elements (PEs) to build more parallel architectures. This trend leads to more heterogeneous computing systems, for which abstractions are needed to efficiently program the systems without increasing the programming cost. This has lead to new programming languages and application programming interfaces (APIs). Parallel programming has always been a holy grail in computer science and dataflow programming promises a way to automatically provide parallel constructs for the programmer. This paper provides an approach to translate dataflow process networks (DPNs) into programs running some of the computations on the Open Computing Language (OpenCL) platform, supporting running programs on massively parallel hardware such as graphics processing units (GPUs). We show that certain DPN programs could run very efficiently on dataparallel architectures but also that there are certain patterns in DPN programs that prove problematic.

Webserver farms and datacenters currently use workload consolidation to match the dynamic workload with the available resources since switching off unused machines has been shown to save energy. The workload is placed on the active servers until the servers are saturated. The idea of workload consolidation can be brought also to chip level by the OS scheduler to pack as much workload to as few cores as possible in a manycore system. In this case all idle cores in the system are placed in a sleep state, and are woken up on-demand. Due to the relationship between static power dissipation and temperature, this paper investigates the thermal influence on the energy efficiency of chip level workload consolidation and its potential impact on the scheduling decisions. This work lay down the foundation for the development of a model for energy efficient OS scheduling for manycore processors taking into account external factors such as ambient and core level temperatures.

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