Introdution to Power-Aware HPC

This course will be held in English!

Welcome to the course webpage for Introdution to Power-Aware HPC for winter-term 2019/20 at LMU Munich. Here you will be able to find the details on the lecture and the accompanying practical project.

Some of the current High Performance Computing (HPC) systems already consume more than 15 MW of power - a sufficient amount of power for sustaining a small city. Energy consumption is becoming a dominating factor for the Total Cost of Ownership of many HPC systems, making high-performance design and energy-efficient design in many ways synonymous.

Apart from the high power bills, power consumptions of these magnitudes act as a limiting factor in building and operating Exascale systems, i.e. next generation of HPC systems that are capable of performing 10¹⁸ floating point operations per second. This could already cause the entire data center's power delivery and cooling infrastructures to breach the safety limits as well as affect the environmental sustainability by producing high carbon footprint. Therefore, it is important to be preemptive in improving energy/power efficiency of HPC data centers.

This course explores different energy consumption issues in modern HPC data centers, discusses their impacts on the design of new computing systems and presents different strategies that aim to reduce the overall power consumption.

The lecture will cover the main concepts of energy consumption paradigms that should remain valid despite the continuous technological changes in the area.

Upon completion of this course the participants should acquire knowledge on:

• the importance of power/energy-efficiency for modern data centers
• the theory behind a variety of impacts that power dissipation in a CMOS chip has on HPC data centers
• contemporary tools for monitoring different power consumption related metrics
• diverse techniques on energy-efficiency tuning
• power-related challenges for next generation HPC systems
• contemporary resource management and scheduling techniques that are tuned for energy-efficiency
• power variation in homogeneous HPC systems and the potential of possible cost savings
• Intel's Model Specific Registers (MSRs) used for power management support
• principles of various machine learning techniques and their applications for intelligent power management
• high-frequency data collection techniques
• datacenter basics (understand the building blocks of modern datacenters and learn about possible architectures)

The course is intended for master students of computer science and related fields. The lecture and the project work have a cumulative weight of 6 ECTS.

More formally, in German:
Die Vorlesung richtet sich an Master-Studierende der Informatik. Für die Vorlesung und die Projektarbeit werden 6 ECTS-Punkte vergeben.

The number of students will be limited to 20. The registration will open 15.08.2019 from 8:00 via UNI2WORK and will close on 17.10.2019 at 13:00.

Prerequisites:

• Python knowledge
• Interest in energy-efficient supercomputing
• Interest in developing machine-learning frameworks

• Lecture: Wednesdays, 18:15 to 19:45 in room 115 in Oettingenstraße 67. The first lecture is held on 16th of October 2019.
• Guided Tour at Leibniz Supercomputing Centre (LRZ): Wednesday, 08.01.2020, 18:15 to 19:45. Meeting point: seminar room 2, LRZ, 85748 Garching bei Muenchen.
• Exam: Tuesday, 11.02.2020 (see the exam section for more details)
• Repeat Exam: Wednesday, 01.04.2020 (see the exam section for more details)

This project aims at building Machine-Learning (ML) based models for predicting the power consumption of a HPC data center's cooling loop. Participants will form groups, where each group will be assigned with an annual operational data obtained at Leibniz Supercomputing Centre (LRZ).

The training data can be found here Project Section.

DATE: Tuesday, 11.02.2020

TIME: 10:30 - 11:30

ROOM: A 021, see: LMU Room Finder

The retake of the exam is scheduled for:

DATE: Monday, 20.07.2020

TIME: 14:30 - 15:30 CEST

ROOM: M 018, LMU

The lecture notes are available in the Download Section.