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Introduction CS 351: Systems Programming Michael Saelee - PowerPoint PPT Presentation

Mar 23, 2024 •211 likes •547 views

Introduction CS 351: Systems Programming Michael Saelee <lee@iit.edu> Computer Science Science Michael (Sae) Lee - lee@iit.edu - http://moss.cs.iit.edu - Office: SB 226C - Hours: Wed & Fri 3:15PM-5:15PM Computer Science Science

  1. Introduction CS 351: Systems Programming Michael Saelee <lee@iit.edu>

  2. Computer Science Science Michael (Sae) Lee - lee@iit.edu - http://moss.cs.iit.edu - Office: SB 226C - Hours: Wed & Fri 3:15PM-5:15PM

  3. Computer Science Science Agenda - Syllabus & Administrivia - Course overview (“Systems Programming”)

  4. Computer Science Science §Syllabus

  5. Computer Science Science Prerequisites - “substantial” programming experience - data structures: concepts & implementation - basic run-time analysis (big O) - knowledge of (any) assembly language - computer organization essentials

  6. Computer Science Science - computer organization essentials: - data representation (binary, two’s comp, f.p. inaccuracy, etc.) - von Neumann model - CPU, memory, I/O - stack usage / conventions

  7. Computer Science Science Online resources 1.Course website moss.cs.iit.edu/cs351 - static information - lecture calendar, assignment writeups, slides, screencasts, links, etc.

  8. Computer Science Science Online resources 2.Blackboard - only for grade reporting !

  9. Computer Science Science Online resources 3.Vimeo channel: screencasts - vimeo.com/channels/cs351 - walkthroughs & tutorials (check before starting labs!)

  10. Computer Science Science Online resources 4.Piazza: discussion forum - all class-related questions - monitored by TAs - scales way better than e-mail

  11. Computer Science Science Textbooks

  12. Computer Science Science Grading - 40% Labs - 30% Midterm exam - 30% Final exam - exam scores may be normalized to 70%

  13. Computer Science Science Labs - 5-6 fairly substantial machine problems - 100-1000 LOC - real-world application of concepts covered in lecture & textbook

  14. Computer Science Science §Course Overview

  15. Computer Science Science “Systems Programming” system |ˈsistəm| noun 1 a set of connected things or parts forming a complex whole ( New Oxford American Dictionary )

  16. Computer Science Science “Systems Programming” - Programming the operating system - What does that mean?

  17. Computer Science Science OS vs. OS kernel - OS kernel ≈ smallest subset of OS code needed to bootstrap system and provide basic services to user programs - “smallest” is debatable

  18. Computer Science Science How to “program” it? - Require some API - Application Programming Interface - A collection of (documented) functions - e.g., get/put/del for a hashtable

  19. Computer Science Science OS API - a.k.a. “system call” interface - OS as a very low-level library - common purpose: provide services to user level programs - def: program in execution = process

  20. Computer Science Science The Process - A program in execution - Code + Data { global, local, dynamic } +OS kernel data - OS hides complexity of machine from processes by creating abstractions

  21. Computer Science Science “Abstraction” http://xkcd.com/

  22. Computer Science Science Primary Abstractions - Logical control flow - Exceptional (extra-process) control flow - Logical address space - Uniform I/O - Interprocess Communication

  23. Computer Science Science In the old days … - … every program had to include its own implementation of all the above! - Now, OS simplifies life for all of us. - Only need to know how to use them, not how they’re implemented .

  24. Computer Science Science But! - In this class we dig a bit deeper - What facilities are encapsulated by syscalls? - What limitations/restrictions do they have? - Why are they designed the way they are? - How do they work behind the scenes?

  25. Computer Science Science But why should I care?

  26. Computer Science Science - efficiency : know how to use tools optimally; reuse existing features and design/layer new ones appropriately - robustness : avoid bugs/failures & know how to diagnose and fix them

  27. Computer Science Science the real reason: it’s fun to take things apart!

  28. Computer Science Science goal: turn you into a hacker

  29. Computer Science Science (or: make you a better hacker)

  30. Computer Science Science hacker |ˈhakər| noun 1 A person who enjoys exploring the details of programmable systems and how to stretch their capabilities, as opposed to most users, who prefer to learn only the minimum necessary. The Jargon File, version 4.4.7

  31. Computer Science Science Our tools (& approach) - C & Linux - C: low-level language - GNU Linux: open source kernel & tools - GNU gdb & gcc; debugger & compiler

  32. Computer Science Science Fourier - All labs must be tested and submitted on the class Linux server: fourier.cs.iit.edu - You should’ve received an e-mail with account info already — let me know if not! - Log in via SSH client, submit work via Git - BitBucket invitations are coming

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