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heterogeneous_systems

Hands-on Acceleration on Heterogeneous Computing Systems

Course Description

The increasing difficulty of scaling the performance and efficiency of CPUs every year has created the need for turning computers into heterogeneous systems, i.e., systems composed of multiple types of processors that can suit better different types of workloads or parts of them. More than a decade ago, Graphics Processing Units (GPUs) became general-purpose parallel processors, in order to make their outstanding processing capabilities available to many workloads beyond graphics. GPUs have been critical key to the recent rise of Machine Learning and Artificial Intelligence, which took unrealistic training times before the use of GPUs. Field-Programmable Gate Arrays (FPGAs) are another example computing device that can deliver impressive benefits in terms of performance and energy efficiency. More specific examples are (1) a plethora of specialized accelerators (e.g., Tensor Processing Units for neural networks), and (2) near-data processing architectures (i.e., placing compute capabilities near or inside memory/storage).

Despite the great advances in the adoption of heterogeneous systems in recent years, there are still many challenges to tackle, for example:

  • Heterogeneous implementations (using GPUs, FPGAs, TPUs) of modern applications from important fields such as bioinformatics, machine learning, graph processing, medical imaging, personalized medicine, robotics, virtual reality, etc.
  • Scheduling techniques for heterogeneous systems with different general-purpose processors and accelerators, e.g., kernel offloading, memory scheduling, etc.
  • Workload characterization and programming tools that enable easier and more efficient use of heterogeneous systems.

If you are enthusiastic about working hands-on with different software, hardware, and architecture projects for heterogeneous systems, this is your P&S. You will have the opportunity to program heterogeneous systems with different types of devices (CPUs, GPUs, FPGAs, TPUs), propose algorithmic changes to important applications to better leverage the compute power of heterogeneous systems, understand different workloads and identify the most suitable device for their execution, design optimized scheduling techniques, etc. In general, the goal will be to reach the highest performance reported for a given important application.

Prerequisites of the course:

  • Digital Design and Computer Architecture (or equivalent course).
  • Familiarity with C/C++ programming and strong coding skills.
  • Interest in future computer architectures and computing paradigms.
  • Interest in discovering why things do or do not work and solving problems
  • Interest in making systems efficient and usable

The course is conducted in English.

The course has two main parts:
1. Short weekly lectures on GPU and heterogeneous programming.
2. Hands-on project: Each student develops his/her own project.

Course description page Moodle

Mentors

Name E-mail Office
Lead Supervisor Juan Gómez Luna juan.gomez@safari.ethz.ch ETZ H 64
Supervisor Mohammed Alser alserm@ethz.ch ETZ H 61.1
Supervisor Behzad Salami bsalami@ethz.ch ETZ H 64
Supervisor Gagandeep Singh gagan.gagandeepsingh@safari.ethz.ch ETZ H 64

Lecture Video Playlist on YouTube

Fall 2021 Meetings/Schedule

Week Date Livestream Meeting Learning Materials Assignments
W1 07.10
Thu.
Live
M1: P&S Course Presentation
(PDF) (PPT)
Required Materials
Recommended Materials
HW 0 Out
W2 14.10
Thu.
Live
M2: SIMD Processing and GPUs
(PDF) (PPT)
W3 21.10
Thu.
Live
M3: GPU Software Hierarchy
(PDF) (PPT)
W4 28.10
Thu.
Live
M4: GPU Memory Hierarchy
(PDF) (PPT)
W5 04.11
Thu.
Live
M5: GPU Performance Considerations
(PDF) (PPT)
W6 11.11
Thu.
Live
M6: Parallel Patterns: Reduction
(PDF) (PPT)
W7 18.11
Thu.
Live
M7: Parallel Patterns: Histogram
(PDF) (PPT)
W8 25.11
Thu.
Live
M8: Parallel Patterns: Convolution
(PDF) (PPT)
W9 02.12
Thu.
Live
M9: Parallel Patterns: Prefix Sum (Scan)
(PDF) (PPT)
W10 09.12
Thu.
Live
M10: Parallel Patterns: Sparse Matrices
(PDF) (PPT)
W11 16.12
Thu.
Live
M11: Parallel Patterns: Graph Search
(PDF) (PPT)
W12 22.12
Thu.
Live
M12: Dynamic Parallelism
(PDF) (PPT)
W13 06.01
Thu.
Live
M13: Collaborative Computing
(PDF) (PPT)

Learning Materials

Meeting 1: Required Materials

  • An introduction to SIMD processors and GPUs:
  • An introduction to GPUs and heterogeneous programming:

Meeting 1: Recommended Materials

More Learning Materials

Assignments

HW0: Student Information (Due: 12.10)

heterogeneous_systems.txt · Last modified: 2022/01/06 20:39 by juang