Energy Efficient Acceleration and Evaluation of Financial Computations towards Real-Time Pricing

Schryver, Christian De; Jung, Matthias; Wehn, Norbert; Marxen, Henning; Kostiuk, Anton; Korn, Ralf

doi:10.1007/978-3-642-23866-6_19

Cited by 8 publications

(12 citation statements)

References 13 publications

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“…This is the case for the test parameter set 4 of the benchmark, plotted in Figure 4(b). Higham and Mao and the reflexion scheme also fail to show a convergence in a several cases (3,4,6,12).…”

Section: Negative Volatility Treatmentmentioning

confidence: 78%

“…Even though in a few cases (1,5) it performs better than the latter one, in several situations (3,4,6,12) hardly any convergence can be seen.…”

Section: Parameter Interactionsmentioning

confidence: 79%

“…Figure 4(a) shows the behavior of the systematic error with the absorption, the partial truncation and the full truncation scheme. However, in several tested cases the difference is huge and in fact hardly any convergence can be seen for the absorption scheme (3,4,6,12). This is the case for the test parameter set 4 of the benchmark, plotted in Figure 4(b).…”

Section: Negative Volatility Treatmentmentioning

confidence: 91%

“…We therefore have suggested a benchmark set to be able to fairly compare different algorithms and their implementations [4], which is available online 1 . It consists of a collection of different numbered market scenarios from the literature.…”

Section: The Test Settingmentioning

confidence: 99%

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Algorithmic complexity in the heston model

Marxen

Kostiuk

Korn

et al. 2011

Proceedings of the Fourth Workshop on High Performance Computational Finance

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In this paper, we present an in-depth investigation of the algorithmic parameter influence for barrier option pricing with the Heston model. For that purpose we focus on singleand multi-level Monte Carlo simulation methods. We investigate the impact of algorithmic variations on simulation time and energy consumption, giving detailed measurement results for a state-of-the-art 8-core CPU server and a Nvidia Tesla C2050 GPU. We particularly show that a naive algorithm on a powerful GPU can even increase the energy consumption and computation time, compared to a better algorithm running on a standard CPU. Furthermore we give preliminary results of a dedicated FPGA implementation and comment on the speedup and energy saving potential of this architecture.

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Section: Negative Volatility Treatmentmentioning

confidence: 78%

“…Even though in a few cases (1,5) it performs better than the latter one, in several situations (3,4,6,12) hardly any convergence can be seen.…”

Section: Parameter Interactionsmentioning

confidence: 79%

Section: Negative Volatility Treatmentmentioning

confidence: 91%

Section: The Test Settingmentioning

confidence: 99%

See 2 more Smart Citations

Algorithmic complexity in the heston model

Marxen

Kostiuk

Korn

et al. 2011

Proceedings of the Fourth Workshop on High Performance Computational Finance

Self Cite

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“…Table 4.1 shows the six market parameter selections we have chosen for the benchmark (one exception is T that belongs to the option itself). They are taken from literature and describe both standard and corner settings relevant for real business [7,19]. Derived from the settings in Table 4.1, we have constructed a set of 12 options that makes up our proposed benchmark.…”

Section: The Benchmark Settingsmentioning

confidence: 99%

Towards Automated Benchmarking and Evaluation of Heterogeneous Systems in Finance

Schryver

Nogueira

2015

FPGA Based Accelerators for Financial Applications

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Benchmarking and fair evaluation of computing systems is a challenge for High Performance Computing (HPC) in general, and for financial systems in particular. The reason is that there is no optimal solution for a specific problem in most cases, but the most appropriate models, algorithms, and their implementations depend on the desired accuracy of the result or the input parameters, for instance. In addition, flexibility and development effort of those systems are important metrics for purchasers from the finance domain and thus need to be well-quantified.In this section we introduce a precise terminology for separating the problem, the employed model, and a solution that consists of a selected algorithm and its implementation. We show how the design space (the space of all possible solutions to a problem) can be systematically structured and explored. In order to evaluate and characterize systems independent of their underlying execution platforms, we illustrate the concept of application-level benchmarks and summarize the state-ofthe-art for financial applications.In particular for heterogeneous and Field Programmable Gate Array (FPGA)-accelerated systems, we present a framework structure for automatically executing and evaluating such benchmarks. We describe the framework structure in detail and show how this generic concept can be integrated with existing computing systems. A generic implementation of this framework is freely available for download.

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