Exascale computing is facing a gap between the ever increasing demand for application performance and the underlying chip technology that does no longer deliver the expected exponential increases in CPU performance. The industry is now progressively moving towards dedicated accelerators to deliver high performance and better energy efficiency. However, the question of programmability still remains. To address this challenge we propose a dedicated high-level accelerator programming and execution model where performance and efficiency are primary targets. Our model splits the computation into a conventional CPU-oriented part and a highly efficient fully programmable data flow part. We present a number of systematic transformations and optimisations targeting Maxeler dataflow systems that typically yield one to two orders of magnitude improvements in terms of both performance and energy efficiency. These significant gains are enabled by addressing fundamental algorithmic properties and on-demand numerical requirements. This approach is demonstrated by a case study from computational finance.

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Maxeler Technologies successfully commercialises high-performance computing systems based on dataflow technology. Maxeler dataflow computers have been deployed in a wide range of application domains including financial data analytics, geoscience and low-latency transaction processing. In the context of cloud computing steadily growing acceptance in new domains, we illustrate how Maxeler dataflow systems can be integrated and employed in a self-organising self-managing heterogeneous cloud environment.@en