High‐performance numerical pricing methods

Moritsch, H; Benkner, Siegfried

doi:10.1002/cpe.643

Cited by 5 publications

(4 citation statements)

References 20 publications

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“…For the performance experiments, we used a kernel from a crash-simulation code which originally has been developed for HPF+, and kernel from a numerical pricing module [21] developed in the context of the AURORA Financial Management 4 (1) 8 (2) 16 (4) 32 (8) 64 (16) number of processors (number of nodes) (1) 8 (2) 16 (4) 32 (8) 64 (16) number of processors (number of nodes) System [11]. Both kernels are based on an iterative computational scheme with an outer time-step loop.…”

Section: Resultsmentioning

confidence: 99%

Language and Compiler Support for Hybrid-Parallel Programming on SMP Clusters

Benkner

Sipkova

2002

Lecture Notes in Computer Science

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Abstract. In this paper we present HPF extensions for clusters of SMPs and their implementation within the VFC compiler. The main goal of these extensions is to optimize HPF for clusters of SMPs by enhancing the functionality of the mapping mechanisms and by providing the user with high-level means for controlling key aspects of distributedmemory and shared-memory parallelization. Based on the proposed language extensions, the VFC compiler adopts a hybrid parallelization strategy which closely reflects the hierarchical structure of SMP clusters by exploiting shared-memory parallelism based on OpenMP within nodes and distributed-memory parallelism utilizing MPI across nodes. We describe the language extensions, outline the hybrid parallelization strategy of VFC and present experimental results which show the effectiveness of these techniques.

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Section: Resultsmentioning

confidence: 99%

Language and Compiler Support for Hybrid-Parallel Programming on SMP Clusters

Benkner

Sipkova

2002

Lecture Notes in Computer Science

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“…The HPF kernel from a numerical pricing module [8,9] realizes a backward induction algorithm on a Hull and White interest rate tree. In the HPF code, the interest rate tree is represented by twodimensional arrays and several index vectors which capture the structure of the tree.…”

Section: Financial Optimization Kernelmentioning

confidence: 99%

Compiling data‐parallel programs for clusters of SMPs

Benkner¹,

Brandes²

2004

Concurrency and Computation

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Clusters of shared-memory multiprocessors (SMPs) have become the most promising parallel computing platforms for scientific computing. However, SMP clusters significantly increase the complexity of user application development when using the low-level application programming interfaces MPI and OpenMP, forcing users to deal with both distributed-memory and shared-memory parallelization details. In this paper we present extensions of High Performance Fortran (HPF) for SMP clusters which enable the compiler to adopt a hybrid parallelization strategy, efficiently combining distributed-memory with shared-memory parallelism. By means of a small set of new language features, the hierarchical structure of SMP clusters may be specified. This information is utilized by the compiler to derive inter-node data mappings for controlling distributed-memory parallelization across the nodes of a cluster and intra-node data mappings for extracting shared-memory parallelism within nodes. Additional mechanisms are proposed for specifying inter- and intra-node data mappings explicitly, for controlling specific shared-memory parallelization issues and for integrating OpenMP routines in HPF applications. The proposed features have been realized within the ADAPTOR and VFC compilers. The parallelization strategy for clusters of SMPs adopted by these compilers is discussed as well as a hybrid-parallel execution model based on a combination of MPI and OpenMP. Experimental results indicate the effectiveness of the proposed features

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“…Special parallelization schemes for Asian option structures are presented in [10]. A summary of the topic of financial pricing related to high performance computing is presented by [11]. The idea is to modify pricing algorithms to optimally run on clusters of workstations and some Grid middleware (see [12] for a summary of the importance for the whole financial industry).…”

Section: Introductionmentioning

confidence: 99%

Parallelization of Pricing Path-Dependent Financial Instruments on Bounded Trinomial Lattices

Schabauer

Hochreiter

Pflug

2008

Computational Science – ICCS 2008

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Abstract. Complex financial instruments are a central concept for the survival of financial enterprises in liberalized markets. The need for fast pricing of more complex and exotic financial products led to the development of new algorithms, and to the parallelization of existing algorithms. In this paper, we present a parallelization scheme for pricing path-dependent interest rate products on bounded trinomial lattices. The basic building block presented in this paper can be used to build more complex pricing schemes. The paper is concluded by a set of numerical results concerning the speedup of the proposed parallelization scheme.

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High‐performance numerical pricing methods

Cited by 5 publications

References 20 publications

Language and Compiler Support for Hybrid-Parallel Programming on SMP Clusters

Language and Compiler Support for Hybrid-Parallel Programming on SMP Clusters

Compiling data‐parallel programs for clusters of SMPs

Parallelization of Pricing Path-Dependent Financial Instruments on Bounded Trinomial Lattices

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