Modeling and Scaling of Generative AI Systems

Today’s computing systems (e.g., Deep learning systems), become ever more complex, due to the rapid development of hardware and software technology. It is challenging to design and run computing systems that guarantee users’ performance requirements in a resource-efficient way. Various quantitative methods are applied to capture such complex system dynamics and predict metrics of interests, from the designing phase of the systems to the runtime performance, e.g., job response times and system anomalies. To optimize the performance of computing systems, a deep understanding of those methods and their applications on the system design is essential. Having practical hand-on experience on designing experiments, deriving models, and validating results with benchmark systems will prepare students to tackle challenges of real-world systems.

Course topics include:

  • Design of experiments and statistical tests.
  • Operational laws and queueing methods for modelling computing systems.
  • Scheduling and load balancing.
  • Machine learning methods for modelling computing systems.
  • System dependability and scalability analysis.
  • Optimization and resource management.

Details

Code 02121
62121
Type Course
ECTS 5
Site Neuchâtel
Track(s) T0 – General
T6 – Data Science
Semester A2024

Teaching

Learning Outcomes
  • Design full/fractional factorial experiments for multi-variate regression analysis, e.g., finding critical parameters for deep learning clusters.
  • Apply queueing theory to analyse and predict the run-time performance of applications, e.g., the average response times of on-line ML training service.
  • Apply machine learning models to analyse and predict the system dependability, e.g, root cause analysis for machine failure.
  • Conduct experiments to profile applications and extract their workload parameters on real systems, e.g., deep learning clusters.
  • Develop resource management policies and validate them on real computing systems, e.g., deep learning clusters.
Lecturer(s) Lydia Chen
Language english
Course Page

The course page in ILIAS can be found at https://ilias.unibe.ch/goto_ilias3_unibe_crs_3102202.html.

Schedules and Rooms

Period Weekly
Schedule Monday, 08:15 - 12:00
Location UniNE, Unimail

Additional information

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First Lecture
The first lecture will be announced later.