DocumentCode :
2029568
Title :
Code generation and parallel code execution from business UML models: A case study for an algorithmic trading system
Author :
Hains, Gaetan ; Chong Li ; Atkinson, Daniel ; Redly, Jarrod ; Wilkinson, Nicholas ; Khmelevsky, Youry
Author_Institution :
LACL, Univ. Paris-Est Creteil, Paris, France
fYear :
2015
fDate :
28-30 July 2015
Firstpage :
84
Lastpage :
93
Abstract :
In this paper we discuss several capstone student projects conducted by the students at University of British Columbia, Okanagan campus (UBCO) and at Okanagan College in different years. The aim of the projects was to demonstrate how end-users could update code for an industrial application (an algorithmic trading system) without any programming skills and programming experience. Another goal was to improve performance for the applications collection of stock information from online public sources by introducing parallel code execution on multi-core personal computers. Real algorithmic trading system requirements were used as a case study. An Eclipse Modelling Framework was used to generate Java code from a UML business model, which can be modified by unexperienced business users. Moreover, code execution can be scaled to a specific computer architecture and hardware for better performance and better computer resources utilization, especially if a business user wants to collect and analyze a long list of stocks. The last section of the paper focuses on performance optimization and analysis.
Keywords :
Java; Unified Modeling Language; commerce; parallel processing; program compilers; Eclipse modelling framework; Java; Okanagan College; UBCO; University of British Columbia Okanagan; algorithmic trading system; business UML models; capstone student projects; code generation; multicore personal computers; parallel code execution; performance optimization; stock information; Biological system modeling; Computational modeling; Finance; Instruction sets; Synchronization; Unified modeling language; Algorithmic Trading; BSP; UML; code generation; high performance computing; parallel programming; performance prediction;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Science and Information Conference (SAI), 2015
Conference_Location :
London
Type :
conf
DOI :
10.1109/SAI.2015.7237130
Filename :
7237130
Link To Document :
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