Lecture

Modularity and Abstraction

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In course

CS-522: Principles of computer systems

This advanced graduate course teaches the key design principles underlying successful computer and communication systems, and shows how to solve real problems with ideas, techniques, and algorithms from operating systems, networks, databases, programming l ...

Description

This lecture delves into the concepts of modularity and abstraction in computer systems design. The instructor emphasizes the importance of creating self-contained units that provide specific functions and can be easily plugged and unplugged from a system. The lecture explores how modularity and abstraction help in simplifying complex systems, improving scalability, and reducing conflicts in memory access. Real-world examples, such as file creation operations and memory access conflicts, are used to illustrate the principles of scalable commutativity. The lecture concludes by highlighting the significance of designing interfaces that allow operations to commute, leading to scalable implementations.

Instructor

George Candea

George Candea heads the Dependable Systems Lab, where he develops techniques and abstractions for building efficient and trustworthy computer systems. His current work focuses on making performance and energy programmable, i.e., providing first-class abstractions to capture a system’s performance and energy behavior (as interfaces do for functionality) and to use them dependably to design, code, and operate efficient systems.In the recent past, George also served as CEO and later Chief Scientist of Cyberhav

Official source

Related concepts (17)

Complexity

Complexity characterises the behaviour of a system or model whose components interact in multiple ways and follow local rules, leading to non-linearity, randomness, collective dynamics, hierarchy, and emergence. The term is generally used to characterize something with many parts where those parts interact with each other in multiple ways, culminating in a higher order of emergence greater than the sum of its parts. The study of these complex linkages at various scales is the main goal of complex systems theory.

User interface design

User interface (UI) design or user interface engineering is the design of user interfaces for machines and software, such as computers, home appliances, mobile devices, and other electronic devices, with the focus on maximizing usability and the user experience. In computer or software design, user interface (UI) design primarily focuses on information architecture. It is the process of building interfaces that clearly communicates to the user what's important. UI design refers to graphical user interfaces and other forms of interface design.

Social complexity

In sociology, social complexity is a conceptual framework used in the analysis of society. In the sciences, contemporary definitions of complexity are found in systems theory, wherein the phenomenon being studied has many parts and many possible arrangements of the parts; simultaneously, what is complex and what is simple are relative and change in time. Contemporary usage of the term complexity specifically refers to sociologic theories of society as a complex adaptive system, however, social complexity and its emergent properties are recurring subjects throughout the historical development of social philosophy and the study of social change.

Kolmogorov complexity

In algorithmic information theory (a subfield of computer science and mathematics), the Kolmogorov complexity of an object, such as a piece of text, is the length of a shortest computer program (in a predetermined programming language) that produces the object as output. It is a measure of the computational resources needed to specify the object, and is also known as algorithmic complexity, Solomonoff–Kolmogorov–Chaitin complexity, program-size complexity, descriptive complexity, or algorithmic entropy.

Complexity class

In computational complexity theory, a complexity class is a set of computational problems "of related resource-based complexity". The two most commonly analyzed resources are time and memory. In general, a complexity class is defined in terms of a type of computational problem, a model of computation, and a bounded resource like time or memory. In particular, most complexity classes consist of decision problems that are solvable with a Turing machine, and are differentiated by their time or space (memory) requirements.

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