Supercomputers Architecture

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Credits
6
Types
Specialization complementary (High Performance Computing)
Requirements
This subject has not requirements, but it has got previous capacities
Department
AC
This course introduces the fundamentals of high-performance and parallel computing. It is targeted at scientists and engineers seeking to develop the skills necessary for working with supercomputers, the leading edge in high-performance computing technology.

In the first part of the course, we will cover the basic building blocks of supercomputers and their system software stack. Then, we will introduce their traditional parallel and distributed programming models, which allow one to exploit parallelism, a central element for scaling the applications in these types of high-performance infrastructures.

In the second part of the course, we will motivate the current supercomputing systems developed to support artificial intelligence algorithms required in today's world. This year's syllabus will pay special attention to Deep Learning (DL) algorithms and their scalability using a GPU platform.

This course uses the “learn by doing” approach, based on a set of exercises, made up of programming problems and reading papers, that the students must carry out throughout the course. The course will be marked by a continuous assessment, which ensures constant, steady work.

All in all, this course seeks to enable students to acquire practical skills that can help them as much as possible to adapt and anticipate the new technologies that will undoubtedly emerge in the coming years. For the practical part of the exercises, the student will use supercomputing facilities from the Barcelona Supercomputing Center (BSC-CNS).

UPDATED VERSION: https://torres.ai/sa-miri/

Teachers

Person in charge

  • Jordi Torres Viñals ( )

Weekly hours

Theory
2
Problems
0
Laboratory
2
Guided learning
0.15
Autonomous learning
7.7

Competences

Technical Competences of each Specialization

High performance computing

  • CEE4.1 - Capability to analyze, evaluate and design computers and to propose new techniques for improvement in its architecture.
  • CEE4.2 - Capability to analyze, evaluate, design and optimize software considering the architecture and to propose new optimization techniques.
  • CEE4.3 - Capability to analyze, evaluate, design and manage system software in supercomputing environments.

Generic Technical Competences

Generic

  • CG1 - Capability to apply the scientific method to study and analyse of phenomena and systems in any area of Computer Science, and in the conception, design and implementation of innovative and original solutions.

Transversal Competences

Teamwork

  • CTR3 - Capacity of being able to work as a team member, either as a regular member or performing directive activities, in order to help the development of projects in a pragmatic manner and with sense of responsibility; capability to take into account the available resources.

Basic

  • CB6 - Ability to apply the acquired knowledge and capacity for solving problems in new or unknown environments within broader (or multidisciplinary) contexts related to their area of study.
  • CB8 - Capability to communicate their conclusions, and the knowledge and rationale underpinning these, to both skilled and unskilled public in a clear and unambiguous way.
  • CB9 - Possession of the learning skills that enable the students to continue studying in a way that will be mainly self-directed or autonomous.

Objectives

  1. To train students to follow by themselves the continuous development of supercomputing systems that enable the convergence of advanced analytic algorithms as artificial intelligence.
    Related competences: CB6, CB8, CB9, CTR3, CG1, CEE4.1, CEE4.2, CEE4.3,

Contents

  1. 00. Welcome: Course content and motivation
  2. 01. Supercomputing basics
  3. 02. General purpose supercomputers
  4. 03. Parallel programming languages for shared memory platforms
  5. 04. Parallel programming languages for distributed platforms
  6. 05. Parallel Performance
  7. 06. Heterogeneous supercomputers
  8. 07. Parallel programming languages for heterogeneous platforms
  9. 08. Emerging Trends and Challenges in Supercomputing
  10. 09. Getting started with DL
  11. 10. AI is a Supercomputing problem
  12. 11. Using Supercomputers for DL training
  13. 12. Parallel and Distributed Deep Learning Frameworks
  14. 13. Accelerate the learning with Parallel training on multiple GPUs
  15. 14. Accelerate the learning with Distributed training on multiple servers

Activities

Activity Evaluation act


00. Welcome


Objectives: 1
Theory
2h
Problems
0h
Laboratory
0h
Guided learning
0h
Autonomous learning
2h

01. Supercomputing basics



Theory
1h
Problems
0h
Laboratory
0h
Guided learning
0.1h
Autonomous learning
4h

Exercise 01: Read and present a paper about exascale computers challenges



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Laboratory
1h
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Autonomous learning
2h

02. General purpose supercomputers



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Autonomous learning
4h

Exercise 02: Getting started with Supercomputing



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0.2h
Autonomous learning
2h

03. Parallel programming languages for shared memory platforms



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Autonomous learning
2h

Exercise 03: Getting started with OpenMP



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Autonomous learning
2h

04. Parallel programming languages for distributed platforms



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Laboratory
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Autonomous learning
4h

Exercise 04: Getting started with MPI



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Laboratory
2h
Guided learning
0.1h
Autonomous learning
3h

05. Parallel Performance



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Autonomous learning
4h

Exercise 05: Getting started with parallel performance metrics and models



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Laboratory
1h
Guided learning
0.1h
Autonomous learning
3h

06. Heterogeneous supercomputers



Theory
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Autonomous learning
3h

Exercise 06: Comparing supercomputers performance



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Laboratory
1h
Guided learning
0.1h
Autonomous learning
3h

07. Parallel programming languages for heterogeneous platforms



Theory
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Laboratory
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Autonomous learning
2h

Exercise 07: Getting started with CUDA



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Laboratory
3h
Guided learning
0.1h
Autonomous learning
5h

08. Emerging Trends and Challenges in Supercomputing



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Autonomous learning
1h

Exercise 08: Read and present a paper about emerging trends in supercomputing



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Laboratory
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Guided learning
0.1h
Autonomous learning
3h

Midterm



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Autonomous learning
2h

09. Getting started with DL



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Autonomous learning
3h

Exercise 09: First contact with Deep Learning



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Laboratory
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Guided learning
0.1h
Autonomous learning
4h

10. AI is a Supercomputing problem



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1h

Exercise 10: The new edition of the TOP500



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Autonomous learning
4h

11. Using Supercomputers for DL training



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Autonomous learning
2h

Exercise 11: Using a supercomputer for Deep Learning training



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Laboratory
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Guided learning
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Autonomous learning
6h

12. Parallel and Distributed Deep Learning Frameworks



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Autonomous learning
1h

Exercise 12: Using prebuild models for Deep Learning training



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Laboratory
3h
Guided learning
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Autonomous learning
8h

13. Accelerate the learning with Parallel training on multiple GPUs



Theory
1h
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Autonomous learning
1h

Exercise 13: Getting started with parallel Deep Learning training



Theory
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Laboratory
3h
Guided learning
0.2h
Autonomous learning
8h

14. Accelerate the learning with Distributed training on multiple servers



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Autonomous learning
1h

Exercise 14: Getting started with distributed Deep Learning training



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Laboratory
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Guided learning
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Autonomous learning
8h

Final remarks



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Autonomous learning
2h

Teaching methodology

Class attendance and participation: Regular attendance is expected, and is required to be able to discuss concepts that will be covered during class.

Lab activities: Some exercises will be conducted as hands-on sessions during the course using supercomputing facilities. The student's own laptop will be required to access these resources during the theory class. Each hands-on session will involve writing a lab report with all the results. There are no days for theory classes and days for laboratory classes. Theoretical and practical activities will be interspersed during the same session to facilitate the learning process.

Reading/presentation assignments: Some exercise assignments will consist of reading documentation/papers that expand the concepts introduced during lectures. Some exercises will involve student presentations (randomly chosen).

Assessment: There will be one midterm exam in the middle of the course. The student is allowed to use any type of documentation (also digital via the student's laptop)

Evaluation methodology

The evaluation of this course can be obtained by continuous assessment. This assessment will take into account the following:

25% Attendance + participation
10% Midterm exam
65% Exercises (+ exercise presentations) and Lab exercises (+ Lab reports)
Details of the weight of each component of the course in the grade are described in the tentative scheduling section.

Course Exam: For those students who have not benefited from the continuous assessment, a course exam will be announced during the course. This exam includes evaluating the knowledge of the entire course (practical part, theoretical part, and self-learning part). During this exam, the student is not allowed to use any documentation (neither on paper nor digital).

Bibliography

Basic:

  • Class handouts and materials associated with this class - Torres, J, 2019.
  • Understanding Supercomputing, to speed up machine learning algorithms (Course notes) - Torres, J, 2018.
  • Marenostrum4 User's guide - BSC documentation, Operations department, 2019.
  • High performance computing : modern systems and practices - Sterling, T.; Anderson, M.; Brodowicz, M, Morgan Kaufmann, 2018. ISBN: 9780124201583
    http://cataleg.upc.edu/record=b1519884~S1*cat
  • Dive into deep learning - Zhang, A.; Lipton, Z.C.; Li, M.; Smola, A.J, 2020.
  • First contact with Deep learning: practical introduction with Keras - Torres, J, Kindle Direct Publishing, 2018. ISBN: 9781983211553
    http://cataleg.upc.edu/record=b1510639~S1*cat

Web links

Previous capacities

Programming in C and Linux basics will be expected in the course. In addition, prior exposure to parallel programming constructions, Python language, experience with linear algebra/matrices, or machine learning knowledge will be helpful.

Addendum

Contents

THERE ARE NOT CHANGES IN THE CONTENTS REGARDING THE INFORMATION IN THE COURSE GUIDE.

Teaching methodology

THE COURSE IS PLANNED WITH 100% PRESENTIALITY, SO THERE ARE NOT CHANGES IN THE METHODOLOGY REGARDING THE INFORMATION IN THE COURSE GUIDE.

Evaluation methodology

THERE ARE NOT CHANGES IN THE EVALUATION METHOD REGARDING THE INFORMATION IN THE COURSE GUIDE.

Contingency plan

IF THE COURSE HAS TO BE OFFERED WITH REDUCED PRESENTIALITY OR NOT PRESENTIALLY, THERE WILL BE NOT CHANGES IN THE CONTENTS AND THE EVALUATION METHOD, BUT THE METHODOLOGY WILL BE ADAPTED TO ALLOW FOLLOWING THE COURSE REMOTELY, INCLUDING AMONG OTHERS: * USE THE 'RACÓ' TO DOWNLOAD THE SLIDES, EXERCISES, PRACTICAL ASSIGNMENTS, AND OTHER DOCUMENTATION * USE VIDEO AND/OR SCREENCAST MATERIAL FOR ASYNCHRONOUS LECTURES AND PRACTICAL CLASSES * USE VIDEOCONFERENCE FOR SYNCHRONOUS LECTURES AND PRACTICAL CLASSES * USE THE 'RACÓ' FOR ASSIGNMENT SUBMISSIONS * USE MAIL AND/OR THE FORUM FOR ASYNCHRONOUS CONSULTATION * USE CHAT AND/OR VIDEOCONFERENCE FOR SYNCHRONOUS CONSULTATION * TENTATIVE TO USE "ATENEA" OR "RACÓ" FOR TWO MIDTERM EVALUATION