Computational finance is an area that includes many algorithms in trading and analytics that are both computationally very complex and performance critical. As financial institutions intend to perform a steadily increasing number of computations and obtain the results as quickly as possible, computer systems are expected to satisfy these growing performance demands. However, recent years have brought the end of “free” processors speed-ups, and single-thread performance is no longer the driving force behind automatic performance gains enjoyed by the industry for many decades. Nowadays, high-performance computing systems have to increasingly rely on parallel programming models where the original application has to be modified to exploit many parallel cores. This requires considerable redesign efforts and yet, the desired performance improvements are not guaranteed. Some financial applications may also reach practical physical limits imposed by the space and power provisions available in the data centre. A solution to the above problems can be the use of custom accelerators implemented in reconfigurable hardware. Reconfigurable implementations can deliver both high computational throughput and low compute latency in addition to superior energy efficiency. However, porting applications for such devices requires a special skill set in hardware design, complicating their practical adoption. Maxeler Technologies offers conveniently programmable, high-performance computing systems and a software toolchain that exploit the sheer computational power of reconfigurable devices while abstracting the programming into a high-level data-flow model. Our vision is to empower domain experts with the necessary means to create highly customised, efficient hardware/software implementations for their specific applications. This approach enables vertical optimisations across the different layers of abstraction that are typically not exposed to an application designer. The final result is a productive application development process that often delivers speed-ups by orders of magnitudes over traditional CPU implementations.@en