Managing Parallel and Distributed Monte Carlo Simulations for Computational Finance in a Grid Environment

Stokes-Ress, Ian; Baude, Françoise; Doan, Viet-Dung; Bossy, Mireille

doi:10.1007/978-0-387-78417-5_17

Cited by 1 publication

(1 citation statement)

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“…Stokes-Res [6] discusses the parallelization and distribution of Monte Carlo option pricing in the context of several grid environments. Di culties related to running parallel tasks on the grid were revealed, consisting in synchronization of the start task time and partitioning the computational load.…”

Section: Experiments and Resultsmentioning

confidence: 99%

E-Fast & CloudPower: Towards High Performance Technical Analysis for Small Investors

Moca

Moldovan

Lodygensky

et al. 2016

Economics of Grids, Clouds, Systems, and Services

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About 80% of the financial market investors fail, the main reason for this being their poor investment decisions. Without advanced financial analysis tools and the knowledge to interpret the analysis, the investors can easily make irrational investment decisions. Moreover, investors are challenged by the dynamism of the market and a relatively large number of indicators that must be computed. In this paper we propose E-Fast, an innovative approach for on-line technical analysis for helping small investors to obtain a greater e ciency on the market by increasing their knowledge. The E-Fast technical analysis platform prototype relies on High Performance Computing (HPC), allowing to rapidly develop and extensively validate the most sophisticated finance analysis algorithms. In this work, we aim at demonstrating that the E-Fast implementation, based on the CloudPower HPC infrastructure, is able to provide small investors a realistic, low-cost and secure service that would otherwise be available only to the large financial institutions. We describe the architecture of our system and provide design insights. We present the results obtained with a real service implementation based on the Exponential Moving Average computational method, using CloudPower and Grid5000 for the computations' acceleration. We also elaborate a set of interesting challenges emerging from this work, as next steps towards high performance technical analysis for small investors.

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Section: Experiments and Resultsmentioning

confidence: 99%