This paper proposes a novel hardware compilation approach targeting dataflow designs. This approach is based on aspect-oriented programming to decouple design development from design optimisation, thus improving portability and developer productivity while enabling automated exploration of design trade-offs to enhance performance. We introduce FAST, a language for specifying dataflow designs that supports our approach. Optimisation strategies for the generated designs are specified in FAST, making use of facilities in the domain-specific aspect-oriented language, LARA. Our approach is demonstrated by implementing various seismic imaging designs for ReverseTime Migration (RTM), which have performance comparable to state-of-the-art FPGA implementations while being produced with improved developer productivity.

Modeling wave propagation through the earth is an important application in geoscience. We present a framework for wave propagation modeling on special-purpose hardware, which dramatically improves the application performance compared to conventional CPUs. We utilize custom hardware platforms consisting of a mix of x86 CPUs and dataflow engines connected by high-bandwidth communication links. Application programmers describe their algorithms in a domain specific language using Java syntax, with special dataflow semantics overlayed on top of the Java language. The application-specific dataflow engines run at hundreds of MHz with massive parallelism and deliver high performance/Watt, up to 30 times more energy efficient than conventional CPUs. The power efficiency of this approach suggests that dataflow computing may have a key role to play in the improvements in power efficiency necessary to reach exascale computing.

Monte-Carlo simulations are used in many applications, such as option pricing and portfolio evaluation. Due to their high computational load and intrinsic parallelism, they are ideal candidates for acceleration using reconfigurable hardware. However, for maximum efficiency the hardware configuration must be parametrised to match the characteristics of both the simulation task and the platform on which it will be executed. This paper presents a methodology for the automatic implementation of Monte-Carlo simulations, starting from a high-level mathematical description of the simulation and resulting in an optimised hardware configuration for a given platform. This process automatically generates fully-pipelined hardware for maximum performance; it also maximises thread-level parallelism by instantiating multiple pipelines to optimise device utilisation. The configured hardware is used by an associated software component to execute simulations using run-time supplied parameters. The proposed methodology is demonstrated by five different Monte-Carlo simulations, including log-normal price movements, correlated asset Value-at-Risk calculation, and price movements under the GARCH model. Our results show that hardware implementations from our approach, on a Xilinx Virtex-4 XC4VSX55 FPGA at 150 MHz, can run on-average 80 times faster than software on a 2.66 GHz Xeon PC.

In current FPGA development environments complex projects often end up in an ad-hoc tangle of programming systems; examples include Perl, Makefiles, and Ver-ilog and/or VHDL. To combat this we develop an approach to FPGA development in which a single specification is used to combine: high-and low-level description of custom hardware, parameterisation of existing IP and project build. In this paper we present an abstract overview of our unified approach and a prototype implementation called YAHDL, composed of a set of libraries written in the object-oriented software language Ruby. To explore YAHDL's effectiveness we apply it to an existing project, creating FPGA hardware designs for floating-point Monte Carlo simulations. With this case-study we show it is possible to use YAHDL to simplify the generation of application specific instances of our Monte Carlo architectures while achieving performance in the 200-300 MHz range.

This paper presents a methodology and the results of implementing Monte-Carlo financial simulations in reconfigurable devices. Five different Monte-Carlo simulations are explored, including log-normal price movements, correlated asset value-at-risk calculation, and price movements under the GARCH model. Our results show that hardware implementations from our approach on a Xilinx Virtex-4 XC4VSX55 device run on-average 80 times faster than software on a 2.66GHz PC