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Lecture 1

Introduction and Motivation

Lecture

Slides (PDF, PPTX).

Outline

  • Introduction.
  • Information on course administration.
  • Course motivation.
  • Historical perspective.
Example

Measuring performance in different power modes (Linux).

CPU configuration:

(base) andrewt@ThinkPad-T490s:~/Documents/src/hse/hse-acos-course/docs/part1ca/01_Introduction$ lscpu
Architecture:             x86_64
  CPU op-mode(s):         32-bit, 64-bit
  Address sizes:          39 bits physical, 48 bits virtual
  Byte Order:             Little Endian
CPU(s):                   8
  On-line CPU(s) list:    0-7
Vendor ID:                GenuineIntel
  Model name:             Intel(R) Core(TM) i7-8665U CPU @ 1.90GHz
    CPU family:           6
    Model:                142
    Thread(s) per core:   2
    Core(s) per socket:   4
    Socket(s):            1
    Stepping:             12
    CPU(s) scaling MHz:   17%
    CPU max MHz:          4800.0000
    CPU min MHz:          400.0000
    BogoMIPS:             4199.88

Performance:

(base) andrewt@ThinkPad-T490s:~/Documents/src/hse/hse-acos-course/docs/part1ca/01_Introduction$ cat /proc/cpuinfo | grep MHz
cpu MHz		: 4203.633
cpu MHz		: 4200.157
cpu MHz		: 4200.018
cpu MHz		: 4202.291
cpu MHz		: 4200.038
cpu MHz		: 4200.039
cpu MHz		: 4200.745
cpu MHz		: 4200.007

Power Saver:

(base) andrewt@ThinkPad-T490s:~/Documents/src/hse/hse-acos-course/docs/part1ca/01_Introduction$ cat /proc/cpuinfo | grep MHz
cpu MHz		: 798.948
cpu MHz		: 799.999
cpu MHz		: 799.996
cpu MHz		: 799.981
cpu MHz		: 800.033
cpu MHz		: 799.991
cpu MHz		: 400.000
cpu MHz		: 800.000

Workshop

Outline

  • Introduction: the instructor meets the class.
  • Information on workshop organisation (goals, topics). and administration (attendance, homeworks, grading).
  • Introduction to software tools.
    • RARS.
    • Godbolt.
    • VirtualBox.
    • Linux Ubuntu VM (including connecting to it via SSH.
  • Introduction to Git and GitHub.
  • Practice: play with matrix multiplication and see the benchmarks.

Tasks

  • Read documentation on software tools (RARS, Virtual Box, Ubuntu).
  • Download and install the tools.
  • Generate SSH keys for access to Cloud and GitHub.
  • Create a private GitHub repository and give access to it to the workshop instructor and teaching assistant.
    • Name it <group>_<surname>, e.g. 204_tatarnikov
  • Download, compile, and run matrix computation examples:

Notes

VirtualBox

Some laptops might have issues with hardware virtualization. It should be enabled in BIOS. If you face problems running the VM, please refer to this instruction. We have a ready-to-use VirtualBox VM with Linux Ubuntu. The instruction how to get and deploy it is here.

Connecting to the running VirtualBox VM
  1. Run terminal in your host operating system (Git Bash for Windows).
  2. Execute the following command: 
    ssh acos@localhost -p2022
  3. Use the password acos2020.
Bash Commands

Information on useful Linux and MacOS command-line tools can be found here.

Git and GitHub

Notes on how to work with GitHub and Git can be found here.

Example

For example, a MacBook with the following configuration:

Model Name:                 MacBook Pro
Model Identifier:           MacBookPro9,1
Processor Name:             Quad-Core Intel Core i7
Processor Speed:            2,3 GHz
Number of Processors:       1
Total Number of Cores:      4
L2 Cache (per Core):        256 KB
L3 Cache:                   6 MB
Hyper-Threading Technology: Enabled
Memory:                     8 GB

gives these results:

(base) Andreis-MacBook-Pro:01_Introduction andrew$ javac Matrix.java
(base) Andreis-MacBook-Pro:01_Introduction andrew$ gcc matrix.c -o matrix
(base) Andreis-MacBook-Pro:01_Introduction andrew$ gcc matrix_ikj.c -o matrix_ikj
(base) Andreis-MacBook-Pro:01_Introduction andrew$ gcc matrix_jki.c -o matrix_jki
(base) Andreis-MacBook-Pro:01_Introduction andrew$ python3 matrix.py
503.130450
(base) Andreis-MacBook-Pro:01_Introduction andrew$ java Matrix
12.946224373000002
(base) Andreis-MacBook-Pro:01_Introduction andrew$ ./matrix
13.714264
(base) Andreis-MacBook-Pro:01_Introduction andrew$ ./matrix_ikj 
2.739385
(base) Andreis-MacBook-Pro:01_Introduction andrew$ ./matrix_jki 
19.074106

Homework

NOTE: This is a self-study activity, do not need to hand in, no checking, no score.

  • Finish installing all required software.
  • Finish experiments with matrix multiplication and provide benchmarks (for your machine).
    • Run matrix multiplication in Python and C (all versions) in your machine.
    • Provide a table with execution time.
  • Collect information on your CPU (lscpu utility in Linux) and save it to a text file.
  • Commit the file with the table and CPU information to your private GitHub account. Place it into the folder ca/lab01.

References