AbstractIn this course, you will learn how to implement some widely-used algorithms as fast and scalable programs on modern hardware, and how to evaluate your implementation using appropriate test cases and performance experiments.
This course introduces the students to some classical examples of widely used algorithms and uses these to show how to implement an algorithm in a correct and scalable way in the context of a particular application.
The focus of the course is the implementation, its correctness, the experimental analysis, and the connection to the performance models.
Examples of well understood computational tasks that can be considered in depth are:
- shortest path for cars on road networks
- sorting: why different variants of quicksort differ in performance, how to sort strings
- cache oblivious search trees as file system or database index
- matrix multiplication
- matrix vector multiplication
- locality sensitive hashing
- k-means clustering
- minimum spanning tree
- edit distance
- approximate neighborhood
- distinct elements
- job scheduling
The course introduces the different algorithmic tasks, the algorithms, the testing methodology and the performance models. The exercises focus on implementation, testing and interpretation of the measurements
Before the course, the student should be able to:
- Perform basic analysis of algorithm correctness and complexity, using invariants and big-Oh notation.
- Use basic algorithms and data structures when programming (e.g., lists, queues, stacks, search trees, hashing, sorting algorithms, and basic graph algorithms).
Intended learning outcomes
After the course, the student should be able to:
- implement a (randomized, parallel) algorithm from a given high-level description (such as pseudo-code). In particular, this includes judging which data structures, libraries, frameworks, programming languages, and hardware platforms are appropriate for the computational task, and using them effectively in the implementation.
- experimentally evaluate the correctness of an implementation by creating and using adequate test cases.
- design, perform, and run a performance and scalability experiment. In particular, this includes creating and using adequate families of synthesized or real-world test cases, measuring the computational resource requirements of a particular implementation, and judging if the experimental results conform to a theoretical performance model.
- Attend lectures to learn the theoretical and practical foundations of Applied Algorithms.
- Attend exercises, in which they implement algorithms, run experiments in a guided fashion, and practice talking and writing about algorithms, implementations, and experiments.
- Work on exercises outside of class, and write reports on the results of these exercises.
Mandatory activitiesThe course has 2 mandatory assignments. You can work in groups of size up to 3.
The student will receive the grade NA (not approved) at the ordinary exam, if the mandatory activities are not approved and the student will use an exam attempt.
The course does not closely follow any textbook. However, some chapters of the following books will be useful:
- "A Guide to Experimental Algorithmics" by Catherine C. McGeoch
- "Computer Architecture: A Quantitative Approach" by Hennessy and Patterson
Student Activity BudgetEstimated distribution of learning activities for the typical student
- Preparation for lectures and exercises: 7%
- Lectures: 14%
- Exercises: 14%
- Assignments: 28%
- Project work, supervision included: 28%
- Exam with preparation: 9%
Ordinary examExam type:
D: Submission of written work with following oral, External (7-point scale)
D2G: Submission for groups with following oral exam supplemented by the submission. Shared responsibility for the report.
Implementation and experimental evaluation of a self-developed algorithm, or an algorithm as described in one or several research papers.