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Raja H. Singh, Lee Barford, Frederick Harris Jr.
Since the introduction of the Modern Portfolio Theory by Markowitz in the Journal of Finance in 1952, it has been the underlying theory in several portfolio optimization techniques. With the advancement of computers, most portfolio optimization are done by CPUs. Over the years, there have been papers that introduce various optimization methods including those introduced […]
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Brian Barnard
The paper discusses an interest rate term structure decomposition method that breaks from the conventional, in that it does not superimpose any model, form or structure on the decomposition output – hence, the term free-form. The premise is simple: if the model does not presuppose any structure beforehand, and if the structure underlying the input […]
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Blesson Varghese, Javier Prades, Carlos Reano, Federico Silla
‘How can GPU acceleration be obtained as a service in a cluster?’ This question has become increasingly significant due to the inefficiency of installing GPUs on all nodes of a cluster. The research reported in this paper is motivated to address the above question by employing rCUDA (remote CUDA), a framework that facilitates Acceleration-as-a-Service (AaaS), […]
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Gordon Inggs, David B. Thomas, Wayne Luk
Users of heterogeneous computing systems face two problems: firstly, understanding the trade-off relationship between the observable characteristics of their applications, such as latency and quality of the result, and secondly, how to exploit knowledge of these characteristics to allocate work to distributed resources efficiently. A domain specific approach addresses both of these problems. By considering […]
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Joris Cramwinckel
In this thesis we present a state-of-the-art approach to accelerate Monte Carlo valuations of embedded options. Due to regulations and improved risk management, nested simulations (scenarios in scenarios) are becoming increasingly important for institutional investors like: insurance companies, pension funds and housing corporations. Preferably one wishes to use a framework in which multiple related problems […]
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Christian Andreetta, Vivien Begot, Jost Berthold, Martin Elsman, Troels Henriksen, Maj-Britt Nordfang, Cosmin E. Oancea
Commodity many-core hardware is now mainstream, driven in particular by the evolution of general purpose graphics programming units (GPGPUs), but parallel programming models are lagging behind in effectively exploiting the available application parallelism. There are two principal reasons. First, real-world applications often exhibit a rich composition of nested parallelism, whose statical extraction requires a set […]
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Blesson Varghese
The risk of reinsurance portfolios covering globally occurring natural catastrophes, such as earthquakes and hurricanes, is quantified by employing simulations. These simulations are computationally intensive and require large amounts of data to be processed. The use of many-core hardware accelerators, such as the Intel Xeon Phi and the NVIDIA Graphics Processing Unit (GPU), are desirable […]
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Giorgis Georgakoudis, Charles J. Gillan, Ahmed Sayed, Ivor Spence, Richard Faloon, Dimitrios S. Nikolopoulos
Energy efficiency has been a daunting challenge for datacenters. The financial industry operates some of the largest datacenters in the world. With increasing energy costs and the financial services sector growth, emerging financial analytics workloads may incur extremely high operational costs, to meet their latency targets. Microservers have recently emerged as an alternative to high-end […]
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Di Zhao
High-accuracy optimizer is the essential part of accuracy-sensitive applications such as computational finance and computational biology, and we developed single-GPU based Iterative Discrete Approximation Monte Carlo Search (IDA-MCS) in our previous research. However, single-GPU IDA-MCS is in low performance or even functionless for optimization problems with large number of peaks because of the capability constrains […]
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Moritz Kreutzer, Georg Hager, Gerhard Wellein, Andreas Pieper, Andreas Alvermann, Holger Fehske
The Kernel Polynomial Method (KPM) is a well-established scheme in quantum physics and quantum chemistry to determine the eigenvalue density and spectral properties of large sparse matrices. In this work we demonstrate the high optimization potential and feasibility of peta-scale heterogeneous CPU-GPU implementations of the KPM. At the node level we show that it is […]
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Robin Kumar, Amandeep Kaur Cheema
Machine learning, a branch of artificial intelligence, concerns the construction and study of systems that can learn from data. Neural network is the well-known branch of machine learning & it has been used extensively by researchers for prediction of data and the prediction accuracy depends upon fine tuning of particular financial data. In this paper […]
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Chuan-Hsiang Han, Yu-Tuan Lin
Monte Carlo simulations have become widely used in computational finance. Standard error (SE in short) is the basic notion to measure the quality of a Monte Carlo estimator, and the square of SE is defined as the variance divided by the total number of simulations. Variance reduction methods have been developed as efficient algorithms by […]
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