Quantitative Formal Modeling and Worst-Case Performance Analysis

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课程简介

EIT Digital

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In this module/week you will learn to draw a model of a token consumption/production system, and communicate your interpretation of this model with others in an informal manner. At the end of this model, you will be able to draw your own models, and explain your interpretation of them in general terms. Also, you will know about the standard Petri-net interpretation of consumption/production systems, and will be able to point out particular patterns in Petri-net models. Finally, you will be able to refine a consumption/production model into a model that contains sufficient information to allow worst-case performance analysis. This is all tested using a peer-reviewed assignment.

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Welcome to Quantitative Formal Modeling and Worst-Case Performance Analysis. In this course, you will learn about modeling and solving performance problems in a fashion popular in theoretical computer science, and generally train your abstract thinking skills. After finishing this course, you have learned to think about the behavior of systems in terms of token production and consumption, and you are able to formalize this thinking mathematically in terms of prefix orders and counting functions. You have learned about Petri-nets, about timing, and about scheduling of token consumption/production systems, and for the special class of Petri-nets known as single-rate dataflow graphs, you will know how to perform a worst-case analysis of basic performance metrics, like throughput, latency and buffering. Disclaimer: As you will notice, there is an abundance of small examples in this course, but at first sight there are not many industrial size systems being discussed. The reason for this is two-fold. Firstly, it is not my intention to teach you performance analysis skills up to the level of what you will need in industry. Rather, I would like to teach you to think about modeling and performance analysis in general and abstract terms, because that is what you will need to do whenever you encounter any performance analysis problem in the future. After all, abstract thinking is the most revered skill required for any academic-level job in any engineering discipline, and if you are able to phrase your problems mathematically, it will become easier for you to spot mistakes, to communicate your ideas with others, and you have already made a big step towards actually solving the problem. Secondly, although dataflow techniques are applicable and being used in industry, the subclass of single-rate dataflow is too restrictive to be of practical use in large modeling examples. The analysis principles of other dataflow techniques, however, are all based on single-rate dataflow. So this course is a good primer for any more advanced course on the topic. This course is part of the university course on Quantitative Evaluation of Embedded Systems (QEES) as given in the Embedded Systems master curriculum of the EIT-Digital university, and of the Dutch 3TU consortium consisting of TU/e (Eindhoven), TUD (Delft) and UT (Twente). The course material is exactly the same as the first three weeks of QEES, but the examination of QEES is at a slightly higher level of difficulty, which cannot (yet) be obtained in an online course.

定量形式建模和最坏情况性能分析:欢迎来到定量形式建模和最坏情况性能分析。在本课程中,您将学习理论计算机科学中流行的方式来建模和解决性能问题,并通常训练您的抽象思维能力。 完成本课程后,您已经学会了根据令牌的产生和消耗来思考系统的行为,并且您可以根据前缀顺序和计数函数在数学上形式化此思路。您已经了解了Petri网,时间安排以及令牌消费/生产系统的调度,并且已经了解了Petri网的特殊类(称为单速率数据流图),您将知道如何对以下情况进行最坏情况的分析:基本性能指标,例如吞吐量,延迟和缓冲。 免责声明:您会注意到,此课程中有很多小例子,但是乍一看,这里没有讨论太多的工业规模系统。其原因有两个。首先,我并不是要教您达到行业所需水平的性能分析技能。相反,我想教您以一般和抽象的方式来考虑建模和性能分析,因为这是将来遇到任何性能分析问题时都需要做的事情。毕竟,抽象思维是任何工程学科中任何学术级工作所需要的最受推崇的技能,并且如果您能够用数学方式表达问题,那么发现错误,与他人交流思想,并且您已经朝着实际解决问题迈出了一大步。其次,尽管数据流技术适用且已在工业中使用,但是单速率数据流的子类过于严格,无法在大型建模示例中实际使用。但是,其他数据流技术的分析原理都基于单速率数据流。因此,本课程对于该主题的任何高级课程都是不错的入门。 这门课程是EIT数字大学的嵌入式系统硕士课程以及由TU / e(埃因霍温),TUD(德尔福特(Delft))组成的荷兰3TU联盟大学课程中有关嵌入式系统定量评估(QEES)的大学课程的一部分。 )和UT(Twente)。课程材料与QEES的前三周完全相同,但是QEES的考试难度稍高一些,目前尚无法通过在线课程获得。

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