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 […]

May 20, 2015 by hgpu

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 […]

November 13, 2014 by hgpu

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 […]

September 15, 2014 by hgpu

GPU computing has become popular in computational finance and many financial institutions are moving their CPU based applications to the GPU platform. Since most Monte Carlo algorithms are embarrassingly parallel, they benefit greatly from parallel implementations, and consequently Monte Carlo has become a focal point in GPU computing. GPU speed-up examples reported in the literature […]

August 27, 2014 by hgpu

We advocate a domain specific software development methodology for heterogeneous computing platforms such as Multicore CPUs, GPUs and FPGAs. We argue that three specific benefits are realised from adopting such an approach: portable, efficient implementations across heterogeneous platforms; domain specific metrics of quality that characterise platforms in a form software developers will understand; automatic, optimal […]

August 23, 2014 by hgpu

Problems in many areas give rise to computationally expensive integrals that beg the need of efficient techniques to solve them, e.g., in computational finance for the modeling of cash flows; for the computation of Feynman loop integrals in high energy physics; and in stochastic geometry with applications to computer graphics. We demonstrate feasible numerical approaches […]

August 3, 2014 by hgpu

During times of stock market turbulence, monitoring the intraday clustering behaviour of financial instruments allows one to better understand market characteristics and systemic risks. While genetic algorithms provide a versatile methodology for identifying such clusters, serial implementations are computationally intensive and can take a long time to converge to the global optimum. We implement a […]

March 23, 2014 by hgpu

The QuantLib library is a popular library used for many areas of computational finance. In this work, the parallel processing power of the GPU is used to accelerate QuantLib financial applications. Black-Scholes, Monte-Carlo, Bonds, and Repo code paths in QuantLib are accelerated using hand-written CUDA and OpenCL codes specifically targeted for the GPU. Additionally, HMPP […]

May 6, 2013 by hgpu

The volume of banks data calculation is increasing each year with extraordinary scale and with that, new forms of computation is needed. High performance computing is a very attractive field for optimization such bank calculous, which can give promising results. This paper shows a implementation of know model for assessing the credit risk of a […]

April 26, 2013 by hgpu

We describe a high performance parallel implementation of a derivative pricing model, within which we introduce a new parallel method for the calibration of the industry standard SABR (stochastic-alpha beta rho) stochastic volatility model using three strike inputs. SABR calibration involves a non-linear three dimensional minimisation and parallelisation is achieved by incorporating several assumptions unique […]

January 16, 2013 by hgpu

This paper presents a comparison of parallelization frameworks for efficient execution of computational finance workloads. We use a Value-at-Risk (VaR) workload to evaluate OpenCL and OpenMP parallelization frameworks on multi-core CPUs as opposed to GPUs. In addition, we study the impact of SMT on performance using GCC (4.4) and IBM XLC (11.01) compilers for both […]

September 22, 2012 by hgpu

High performance computing (HPC) is a very attractive and relatively new area of research, which gives promising results in many applications. In this paper HPC is used for pricing of American options. Although the American options are very significant in computational finance; their valuation is very challenging, especially when the Monte Carlo simulation techniques are […]

May 3, 2012 by hgpu