QuantCloud: A Software with Automated Parallel Python for Quantitative Finance Applications

Zhang, Peng; Gao, Yuxiang; Shi, Xiang

doi:10.1109/qrs.2018.00052

Cited by 4 publications

(1 citation statement)

References 17 publications

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“…The library SALib Python was used as an example of the more complex tests given in a Python GUI in Sobol's variance-based structural reliability analysis of the model. For better results in the study, the ipyparallel library was used to parallel sequential simulations.Zhang et al,[38] This paper proposed Quant Cloud, a program that integrates a parallel Python framework with a C++-coded Big Data system. This extensive data framework is built in C++, and the user methods are written in Python.…”

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confidence: 99%

Python Parallel Processing and Multiprocessing: A Rivew

et al. 2021

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Parallel and multiprocessing algorithms break down significant numerical problems into smaller subtasks, reducing the total computing time on multiprocessor and multicore computers. Parallel programming is well supported in proven programming languages such as C and Python, which are well suited to “heavy-duty” computational tasks. Historically, Python has been regarded as a strong supporter of parallel programming due to the global interpreter lock (GIL). However, times have changed. Parallel programming in Python is supported by the creation of a diverse set of libraries and packages. This review focused on Python libraries that support parallel processing and multiprocessing, intending to accelerate computation in various fields, including multimedia, attack detection, supercomputers, and genetic algorithms. Furthermore, we discussed some Python libraries that can be used for this purpose.

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confidence: 99%