Application development with hardware description languages (HDLs) such as VHDL or Verilog involves numerous productivity challenges, limiting the potential impact of reconfigurable computing (RC) with FPGAs in highperformance computing. Major challenges with HDL design include steep learning curves, large and complex codes, long compilation times, and lack of development standards across platforms. A relative newcomer to RC, the Open Computing Language (OpenCL) reduces productivity hurdles by providing a platform-independent, C-based programming language. In this study, we conduct a performance and productivity comparison between three image-processing kernels (Canny edge detector, Sobel filter, and SURF feature-extractor) developed using Altera's SDK for OpenCL and traditional VHDL. Our results show that VHDL designs achieved a more efficient use of resources (59% to 70% less logic), however, both OpenCL and VHDL designs resulted in similar timing constraints (255MHz < fmax < 325MHz). Furthermore, we observed a 6× increase in productivity when using OpenCL development tools, as well as the ability to efficiently port the same OpenCL designs without change to three different RC platforms, with similar performance in terms of frequency and resource utilization.

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Multi-asset barrier contracts are path-dependent exotic options consisting of two or more underlying assets. As the dimensions of an option increase, so does the mathematical complexity of a closed form solution. Monte Carlo (MC) methods offer an attractive solution under such conditions. MC methods have an O(n -1/2 ) convergence rate irrespective of the dimension of the integral. However, such methods using conventional computing with CPUs are not scalable enough to enable banks to realize the potential that these exotic options promise. This paper presents an FPGA-based accelerated system architecture to price multi-asset barrier contracts. The architecture consists of a parallel set of Monte Carlo cores, each capable of simulating multiple Monte Carlo paths. Each MC core is designed to be customizable so that the core for the model (i.e., "model" core) can be easily replaced. In our current design, a Heston core based on the full truncation Euler discretization method is used as the model core. Similarly, we can use different payoff calculator kernels to compute various payoffs such as vanilla portfolios, barriers, look-backs, etc. The design leverages an early termination condition of "out" barrier options to efficiently schedule MC paths across multiple cores in a single FPGA and across multiple FPGAs. The target platform for our design is Novo-G, a reconfigurable supercomputer housed at the NSF Center for High-Performance Reconfigurable Computing (CHREC), University of Florida. Our design is validated for the singleasset configuration by comparing our output to option prices calculated analytically and achieves an average speedup of ranging from 123 to 350 on one FPGA as we vary the number of underlying assets from 32 down to 4. For a configuration with 16 underlying assets, the speedup achieved is 7134 when scaled to 48 FPGAs as compared to a single-threaded version of an SSE2-optimized C program running on a single Intel Sandy Bridge E5-2687 core at 3.1 GHz with hyper-threading turned on. Finally, the techniques described in this paper can be applied to other exotic multi-asset option classes, such as lookbacks, rainbows, and Asian-style options.

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Two metabolites of the herbicide dimethyl tetrachloroterephthalate, (DCPA), monomethyl tetrachloroterephthalate (MM) and tetrachloroterephthalic acid (TCPA), are assayed via high-performance liquid chromatography with ion paring. Samples are analyzed via direct injection, without preparation, and analyte detection is accomplished with an ultraviolet photodiode array detector. The metabolites are extracted from positive samples with a petroleum ether/diethyl ether mixture, derivatized with N,O-bis(trimethylsilyl) trifluoracetamide, and confirmed by way of gas chromatography/mass spectrometry. The HPLC analysis of 200 spiked drinking water samples yielded a recovery range of 92-106% with a mean recovery of 101% for TCPA and a recovery range of 92-101% with a mean recovery of 96% for MM. The minimum detection limits for TCPA and MM were 2.4 and 2.7 micrograms/L, respectively. In addition, the GC/MS analysis of spiked reagent water yielded mean recoveries of 91% for MM and 86% for TCPA. Twenty drinking water samples were split and analyzed by the HPLC and GC/MS methods and by USEPA Method 515.1. Comparable results were obtained. The HPLC method, which is amenable to automation, typically allows for the analysis of up to 40 samples overnight.

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