Abstraction and simulation for strategic design-space exploration in reconfigurable computing

Due to recent trends in computing that favor device technologies and applications exploiting explicit parallelism to achieve greater performance, reconfigurable computing (RC) systems consisting of highly parallel applications executing on platforms featuring reconfigurable devices such as FPGAs are becoming an increasingly important option for accelerating applications in high-performance and embedded computing. Unfortunately, the time and difficulty associated with developing applications for RC platforms is often prohibitive, making it difficult to exploit the potential gains in performance and power savings that RC can offer. In order to facilitate RC productivity, better concepts and tools are needed to allow designers to plan and analyze their designs before coding a specific (and possibly fruitless) implementation, a process we call formulation. The research presented here defines a formal framework and presents a set of techniques to address the need for better RC formulation, which includes (1) a script-based discrete-event simulation framework for rapid analysis of RC systems, (2) an abstract modeling language for conveniently representing RC systems that can be integrated with existing prediction and analysis methods, and (3) an algorithm for automated scheduling and partitioning of applications onto scalable RC platforms. Case studies show the simulation framework to provide performance prediction results across multiple applications and platforms with errors of less than 10% and in a fraction of the time that traditional functional simulators require. The abstract modeling framework is demonstrated in modeling a number of RC systems and serves as an effective interface to the simulation framework. The automated scheduling algorithm for scalable RC systems efficiently provides users with near-optimal partitions and schedules of heterogeneous tasks graphs mapped to potentially large-scale distributed RC-based clusters. Combined, these techniques allow RC designers to efficiently model, analyze, and document their designs early in the development process, which is projected to provide large improvements in overall productivity and thus expand the usage of RC technologies.

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