263-0007-00L  Advanced Systems Lab

SemesterSpring Semester 2021
LecturersM. Püschel, C. Zhang
Periodicityyearly recurring course
Language of instructionEnglish
CommentOnly for master students, otherwise a special permission by the study administration of D-INFK is required.



Courses

NumberTitleHoursLecturers
263-0007-00 VAdvanced Systems Lab Special students and auditors need a special permission from the lecturers.3 hrs
Mon10:15-12:00HG F 3 »
Thu09:15-10:00HG F 3 »
M. Püschel, C. Zhang
263-0007-00 UAdvanced Systems Lab2 hrs
Wed14:15-16:00ETF C 1 »
M. Püschel, C. Zhang
263-0007-00 AAdvanced Systems Lab
Project Work, no fixed presence required.
2 hrsM. Püschel, C. Zhang

Catalogue data

AbstractThis course introduces the student to the foundations and state-of-the-art techniques in developing high performance software for mathematical functionality occurring in various fields in computer science. The focus is on optimizing for a single core and includes optimizing for the memory hierarchy, for special instruction sets, and the possible use of automatic performance tuning.
ObjectiveSoftware performance (i.e., runtime) arises through the complex interaction of algorithm, its implementation, the compiler used, and the microarchitecture the program is run on. The first goal of the course is to provide the student with an understanding of this "vertical" interaction, and hence software performance, for mathematical functionality. The second goal is to teach a systematic strategy how to use this knowledge to write fast software for numerical problems. This strategy will be trained in several homeworks and a semester-long group project.
ContentThe fast evolution and increasing complexity of computing platforms pose a major challenge for developers of high performance software for engineering, science, and consumer applications: it becomes increasingly harder to harness the available computing power. Straightforward implementations may lose as much as one or two orders of magnitude in performance. On the other hand, creating optimal implementations requires the developer to have an understanding of algorithms, capabilities and limitations of compilers, and the target platform's architecture and microarchitecture.

This interdisciplinary course introduces the student to the foundations and state-of-the-art techniques in high performance mathematical software development using important functionality such as matrix operations, transforms, filters, and others as examples. The course will explain how to optimize for the memory hierarchy, take advantage of special instruction sets, and other details of current processors that require optimization. The concept of automatic performance tuning is introduced. The focus is on optimization for a single core; thus, the course complements others on parallel and distributed computing.

Finally a general strategy for performance analysis and optimization is introduced that the students will apply in group projects that accompany the course.
Prerequisites / NoticeSolid knowledge of the C programming language and matrix algebra.

Performance assessment

Performance assessment information (valid until the course unit is held again)
Performance assessment as a semester course
ECTS credits8 credits
ExaminersM. Püschel, C. Zhang
Typegraded semester performance
Language of examinationEnglish
RepetitionRepetition only possible after re-enrolling for the course unit.
Additional information on mode of examinationThe grade for the course is determined by several homeworks (30%), one midterm exam (30%), and one semester-long project with final report and presentation (40%). There is no possibility to repeat the midterm exam!

Last cancellation/deregistration date for this graded semester performance: second Friday in March! Please note that after that date no deregistration will be accepted and the course will be considered as "fail".

Learning materials

 
Main linkInformation
Only public learning materials are listed.

Groups

No information on groups available.

Restrictions

General : Special students and auditors need a special permission from the lecturers
PriorityRegistration for the course unit is only possible for the primary target group
Primary target groupCyber Security MSc (260000)
Cyber Security MSc (EPFL) (260100)
Data Science MSc (261000)
Computer Science MSc (263000)
Computational Science and Engineering MSc (438000)

Offered in

ProgrammeSectionType
Cyber Security MasterInterfocus CoursesWInformation
Data Science MasterCore ElectivesWInformation
Computer Science TCSpecialized Courses in Respective Subject with Educational FocusWInformation
Computer Science Teaching DiplomaSpec. Courses in Resp. Subj. w/ Educ. Focus & Further Subj. DidacticsWInformation
Computer Science MasterInterfocus CoursesOInformation
Computational Science and Engineering MasterCore CoursesWInformation