high performance computing on graphics processing units: hgpu.org

Dataflow process networks (DPNs) consist of statically defined process nodes with First-In-First-Out (FIFO) buffered point-to-point connections. DPNs are intrinsically data-driven, i.e., node actions are not synchronized among each other and may fire whenever sufficient input operands arrived at a node. In this original form, DPNs are data-driven and therefore a suitable model of computation (MoC) for asynchronous and distributed systems. For DPNs having nodes with only static consumption/production rates, however, one can easily derive an optimal schedule that can then be used to implement the DPN in a time-driven (clock-driven) way, where each node fires according to the schedule. Both data-driven and time-driven MoCs have their own advantages and disadvantages. For this reason, desynchronization techniques are used to convert clock-driven models into data-driven ones in order to more efficiently support distributed implementations. These techniques preserve the functional specification of the synchronous models and moreover preserve properties like deadlock-freedom and bounded memory usage that are otherwise difficult to ensure in DPNs. These desynchronized models are the starting point of this thesis. While the general MoC of DPNs does not impose further restrictions, many different subclasses of DPNs representing different dataflow MoCs have been considered over time like Kahn process networks, cyclo-static and synchronous DPNs. These classes mainly differ in the kinds of behaviors of the processes which affect on the one hand the expressiveness of the DPN class as well as the methods for their analysis (predictability) and synthesis (efficiency). A DPN may be heterogeneous in the sense that different processes in the network may exhibit different kinds of behaviors. A heterogeneous DPN therefore can be effectively used to model and implement different components of a system with different kinds of processes and therefore different dataflow MoCs. Design tools for modeling like Ptolemy and FERAL are used to model and to design parallel embedded systems using well-defined and precise MoCs, including different dataflow MoCs. However, there is a lack of automatic synthesis methods to analyze and to evaluate the artifacts exhibited by particular MoCs. Second, the existing design tools for synthesis are usually restricted to the weakest classes of DPNs, i.e., cyclo-static and synchronous DPNs where each tool only supports a specific dataflow MoC. This thesis presents a model-based design based on different dataflow MoCs including their heterogeneous combinations. This model-based design covers in particular the automatic software synthesis of systems from DPN models. The main objective is to validate, evaluate and compare the artifacts exhibited by different dataflow MoCs at the implementation level of embedded systems under the supervision of a common design tool. We are mainly concerned about how these different dataflow MoCs affect the synthesis, in particular, how they affect the code generation and the final implementation on the target hardware. Moreover, this thesis also aims at offering an efficient synthesis method that targets and exploits heterogeneity in DPNs by generating implementations based on the kinds of behaviors of the processes. The proposed synthesis design flow therefore generally starts from the desynchronized dataflow models and automatically synthesizes them for cross-vendor target hardware. In particular, it provides a synthesis tool chain, including different specialized code generators for specific dataflow MoCs, and a runtime system that finally maps models using a combination of different dataflow MoCs on the target hardware. Moreover, the tool chain offers a platformindependent code synthesis method based on the open computing language (OpenCL) that enables a more generalized synthesis targeting cross-vendor commercial off-the-shelf (COTS) heterogeneous platforms.