Lecture

Software Performance

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In course

CS-305: Software engineering

This course teaches the basics of modern software development: designing software, working in a team, writing good code, shipping software, and evolving software. It emphasizes building software that meets high standards of quality, reliability, security, ...

Description

This lecture by the instructor covers the definition and quantification of software performance, including how to set performance goals, measure performance metrics, and improve software performance. It discusses latency percentiles, dealing with performance in practice, and various commands related to performance. The lecture also delves into concepts like caching, batching, laziness, speculation, and debugging performance problems. Examples from real-world scenarios, such as Grand Theft Auto, are used to illustrate performance optimization techniques and back-of-the-envelope calculations for latency. Continuous Performance Testing (DevOps) is also explored, emphasizing the importance of performance testing in the development process.

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 (32)

Software metric

In software engineering and development, a software metric is a standard of measure of a degree to which a software system or process possesses some property. Even if a metric is not a measurement (metrics are functions, while measurements are the numbers obtained by the application of metrics), often the two terms are used as synonyms. Since quantitative measurements are essential in all sciences, there is a continuous effort by computer science practitioners and theoreticians to bring similar approaches to software development.

Continuous integration

In software engineering, continuous integration (CI) is the practice of merging all developers' working copies to a shared mainline several times a day. Nowadays it is typically implemented in such a way that it triggers an automated build with testing. Grady Booch first proposed the term CI in his 1991 method, although he did not advocate integrating several times a day. Extreme programming (XP) adopted the concept of CI and did advocate integrating more than once per day – perhaps as many as tens of times per day.

Continuous delivery

Continuous delivery (CD) is a software engineering approach in which teams produce software in short cycles, ensuring that the software can be reliably released at any time and, following a pipeline through a "production-like environment", without doing so manually. It aims at building, testing, and releasing software with greater speed and frequency. The approach helps reduce the cost, time, and risk of delivering changes by allowing for more incremental updates to applications in production.

Software testing

Software testing is the act of examining the artifacts and the behavior of the software under test by validation and verification. Software testing can also provide an objective, independent view of the software to allow the business to appreciate and understand the risks of software implementation. Test techniques include, but are not necessarily limited to: analyzing the product requirements for completeness and correctness in various contexts like industry perspective, business perspective, feasibility and viability of implementation, usability, performance, security, infrastructure considerations, etc.

Test automation

In software testing, test automation is the use of software separate from the software being tested to control the execution of tests and the comparison of actual outcomes with predicted outcomes. Test automation can automate some repetitive but necessary tasks in a formalized testing process already in place, or perform additional testing that would be difficult to do manually. Test automation is critical for continuous delivery and continuous testing.

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