The spectral transforms of logic functions have numerous applications in logic synthesis, signal processing, pattern recognition, and in many related areas. It is very important to be able to efficiently compute these...
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ISBN:
(纸本)9781665485005
The spectral transforms of logic functions have numerous applications in logic synthesis, signal processing, pattern recognition, and in many related areas. It is very important to be able to efficiently compute these transforms. python frameworks show extremely high computation performance for (Fast Fourier Transform) FFT-based algorithms executed on graphics processing units (GPUs). Thus, this paper presents a comparison of computation time for different implementations of spectral transform of logic functions, performed on GPUs using three different python frameworks. It is used TensorFlow, PyTorch, and CuPy, frameworks. The experiments were performed on Nvidia GeForce RTX 2060 GPU, which belongs to the middle GPU price range. Computational times are compared using randomly-generated truth vectors of size up to 4096. The aim of this paper is to identify the computing platform and python programming framework which produces the fastest spectral transform of logic functions.
Purpose - Many libraries have a need to develop their own data-driven web applications, but their technical staff often lacks the required specialized training - which includes knowledge of SQL, a web application lang...
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Purpose - Many libraries have a need to develop their own data-driven web applications, but their technical staff often lacks the required specialized training - which includes knowledge of SQL, a web application language like PHP, JavaScript, CSS, and jQuery. The web2py framework greatly reduces the learning curve for creating data-driven websites by focussing on three main goals: ease of use;rapid development;and security. web2py follows a strict MVC framework where the controls and web templates are all written in pure python. No additional templating language is required. The paper aims to discuss these issues. Design/methodology/approach - There are many frameworks available for creating database-driven web applications. The author had used ColdFusion for many years but wanted to move to a more complete web framework which was also open source. Findings - After evaluating a number of python frameworks, web2py was found to provide the best combination of functionality and ease of use. This paper focusses on the strengths of web2py and not the specifics of evaluating the different frameworks. Practical implications - Librarians who feel that they do not have the skills to create data-driven websites in other frameworks might find that they can develop them in web2py. It is a good web application framework to start with, which might also provide a gateway to other frameworks. Originality/value - web2py is an open source framework that could have great benefit for those who may have struggled to create database-driven websites in other frameworks or languages.
We present dispel4py a versatile data-intensive kit presented as a standard python library. It empowers scientists to experiment and test ideas using their familiar rapid-prototyping environment. It delivers mappings ...
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ISBN:
(纸本)9781467393256
We present dispel4py a versatile data-intensive kit presented as a standard python library. It empowers scientists to experiment and test ideas using their familiar rapid-prototyping environment. It delivers mappings to diverse computing infrastructures, including cloud technologies, HPC architectures and specialised data-intensive machines, to move seamlessly into production with large-scale data loads. The mappings are fully automated, so that the encoded data analyses and data handling are completely unchanged. The underpinning model is lightweight composition of fine-grained operations on data, coupled together by data streams that use the lowest cost technology available. These fine-grained workflows are locally interpreted during development and mapped to multiple nodes and systems such as MPI and Storm for production. We explain why such an approach is becoming more essential in order that data-driven research can innovate rapidly and exploit the growing wealth of data while adapting to current technical trends. We show how provenance management is provided to improve understanding and reproducibility, and how a registry supports consistency and sharing. Three application domains are reported and measurements on multiple infrastructures show the optimisations achieved. Finally we present the next steps to achieve scalability and performance.
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