This dissertation presents research into the development of high performance dataflow middleware and applications on heterogeneous, distributed-memory supercomputers. We present coarse-grained state-of-the-art ad-hoc techniques for optimizing the performance of real-world, data-intensive applications in biomedical image analysis and radar signal analysis on clusters of computational nodes equipped with multi-core microprocessors and accelerator processors, such as the Cell Broadband Engine and graphics processing units. Studying the performance of these applications gives valuable insights into the relevant parameters to tune for achieving efficiency, because being large-scale, data-intensive scientific applications, they are representative of what researchers in these fields will need to conduct innovative science. Our approaches shows that multi-core processors and accelerators can be used cooperatively to achieve application performance which may be many orders of magnitude above naive reference implementations. Additionally, a fine-grained programming framework and runtime system for the development of dataflow applications for accelerator processors such as the Cell is presented, along with an experimental study showing our framework leverages all of the peak performance associated with such architectures, at a fraction of the cognitive cost to developers. Then, we present an adaptive technique for automating the coarse-grained ad-hoc optimizations we developed for tuning the decomposition of application data and tasks for parallel execution on distributed, heterogeneous processors. We show that our technique is able to achieve high performance, while significantly reducing the burden placed on the developer to manually tune the relevant parameters of distributed dataflow applications. We evaluate the performance of our technique on three real-world applications, and show that it performs favorably compared to three state-of-the-art distributed programming frameworks. By bringing our adaptive dataflow middleware to bear on supporting alternative programming paradigms, we show our technique is flexible and has wide applicability.
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