Click here to see a PREVIEW of important rules that was posted before the semester started.

This is an undergraduate course on computer operating systems. (But only graduate students are permitted to be in this class. USC undergraduate students must take CS 350 in order to get credit for OS. If you are an undergraduate student, you cannnot be in this class and you cannot get credit for Operating System if you take this class. Please check with your adviser to see which Operating System class you need to take!) In addition to exploring concepts such as synchronization, virtual memory, processes, file systems and virtualization, students will develop elements of a fairly complete operating system during the course of the semester.
 

Instructor Bill Cheng (click to see office hours)
E-mail: <bill.cheng@usc.edu>.  (Please do not send HTML-only e-mails. They will not be read.)
  DEN Section (29945D+29946D) AM Section (30331D) PM Section (29971D)
Time MW 9:30am - 10:50am  TT 9:30am - 10:50am  TT 12:30pm - 1:50pm 
Location OHE 100D  GFS 116  THH 102 
TA Ben Yan, E-mail: <wumoyan@usc.edu>
Office Hours: Fri 8:00am - 10:00am in SAL 200
Chien-Lun Chen, E-mail: <chienlun@usc.edu>
Office Hours: Mon 10:50am - 11:50am in SAL 200
Yue Shi, E-mail: <yueshi@usc.edu>
Office Hours: Thu 2:00pm - 4:00pm in SAL open lab (in front of SAL 126)
Course Producer
Vignendra Jannela, E-mail: <jannela@usc.edu>, Helpdesk Hours: Mon 12-1:00pm, Tue/Wed/Thu 11am-12:00pm in SAL open lab (in front of SAL 126) or one of the rooms in SAL 109 (inside SAL open lab)
Graders
Parth Kapadia, E-mail: <pkapadia@usc.edu>
Vickram Pentyala, E-mail: <vpentyal@usc.edu>
Deepa Sreekumar, E-mail: <dsreekum@usc.edu>
(If needed, the grader will hold office hours the week after the announcement of each assignment's grades.)
Midterm Exam during class time, Wed, 3/20/2019 (firm) during class time, Thu, 3/21/2019 (firm) during class time, Thu, 3/21/2019 (firm)
Final Exam 8am-10am, Fri, 5/3/2019 (firm), in SGM 101,  (SGM is located in section 4B of the campus map). 8am-10am, Tue, 5/7/2019 (firm). 2pm-4pm, Wed, 5/8/2019 (firm).
Class Resources
Description   :   textbooks, topics covered, grading policies, additional resources, etc.
Lectures   :   information about lectures (and lectures slides in PDF format).
Videos   :   information about DEN lectures and discussion sections videos.
Discussions   :   information about discussion sections.
Projects   :   programming assignments (please also see important information about the class projects below.)
Participation   :   how to earn extra credit for class participation.
Newsgroup   :   Google Group for discussing course materials and programming assignments. You are required to be a member of this group. (This group is by invitation only.) Please do not send request to join this group until after the first lecture.
(in reversed chronological order)
  • 5/2/2019:
    • Below is the grade normalization information for kernel2. Please note that this only applies to the grader-dependent part of your grade. If you are graded by Parth Kapadia <pkapadia@usc.edu>, his kernel2 average was 90.14 with a standard deviation of 5.90. If you are graded by Vickram Pentyala <vpentyal@usc.edu>, his kernel2 average was 99.24 with a standard deviation of 6.38. If you are graded by Deepa Sreekumar <dsreekum@usc.edu>, her kernel2 average was 94.57 with a standard deviation of 6.26. The overall class average for kernel2 was 94.55 with a standard deviation of 7.23.

      To figure out your normalized score for kernel2, here's what you can do. If your grader-dependent part of your grade is X and your grader's average is A with a standard deviation of D, then Y=(X-A)/D is the number of standard deviations away your score is from your grader's average. Therefore, your normalized grader-dependent part of your grade would be 94.55+Y*7.23 (i.e., same number of standard deviation away from the overall class average). Your minimum score is still one point if you have submitted something for grading.

      As I have mentioned in Lecture 1, although we assume that we have a bell-shaped curve, when your score is normalized, linear interpolation is used. It's clearly not perfect since the actual curve will never be bell-shaped and linear interpolation is not the same as bell-shaped-curve interpolation. But this is what was announced at the beginning of the semester, and therefore, we will stick to this particular way of normailzation for all the programming assignments for the rest of the semester, knowing that it's not perfect.


  • 4/26/2019:
    • Discussion sections today has been canceled. Everyone will get rollsheet signing credit.

  • 4/25/2019:
    • Ben Yan has to be out of town tomorrow (Friday). His office hour tomorrow is canceled.

  • 4/24/2019: The final exam will be closed book, closed notes, and closed everything (and no "cheat sheet"). Also, no calculators, cell phones, or any electronic gadgets are allowed. Please bring a photo ID. Your ID will be collected at the beginning of the exam and will be returned to you when you turn in your exam. Please only go to the exam for the section you are registered. Also, no matter how late you show up for the exam, your exam must end at the same time as everyone else in your section. There will be assigned seating.

    The final exam will cover everything from slide 22 of Lecture 13 to slide 31 of Lecture 15 PLUS from slide 32 of Lecture 17 to the last slide of Lecture 31. Also included are discussion section slides from Week 9 through Week 13.

    Since the 2nd part of the course depends on stuff covered by the midterm, I cannot say that I will not ask anything covered by the midterm and you do need to know the material covered by the midterm. Therefore, it would be more appropriate to say that the final exam will focus on the material not covered by the midterm.

    Regarding what types of questions will be on the exam, please see the Exams section of the course description web page. Regarding regrade policy, please see the Regrade section of the course description web page.

    Please note that if you are asked to run the Stride Scheduling algorithm, to get any credit, you must run the one described in Lecture 30 (and not the one in the textbook).

    Here is a quick summary of the final exam topics (not all topics covered may be listed):

    • Ch 3 - Basic Concepts
      • shared libraries
    • Ch 4 - Operating-System Design
      • devices
      • virtual machines, microkernels
    • Ch 5 - Processor Management
      • threads implementations
      • interrupts
      • scheduling
    • Ch 6 - File Systems
      • the basics of file systems
      • performance improvements
      • crash resiliency
      • directories and naming
      • RAID, flash memory, case studies
    • Ch 7 - Memory Management
      • virtual memory
      • OS issues
    • Kernel assignments 2 & 3
      • spec
      • FAQ
      • my posts to class Google Group

  • 4/9/2019:
    • Below is the grade normalization information for kernel1. Please note that this only applies to the grader-dependent part of your grade. If you are graded by Parth Kapadia <pkapadia@usc.edu>, his kernel1 average was 88.06 with a standard deviation of 13.23. If you are graded by Vickram Pentyala <vpentyal@usc.edu>, his kernel1 average was 96.23 with a standard deviation of 5.87. If you are graded by Deepa Sreekumar <dsreekum@usc.edu>, her kernel1 average was 91.04 with a standard deviation of 7.14. The overall class average for kernel1 was 91.91 with a standard deviation of 9.88.

      To figure out your normalized score for kernel1, here's what you can do. If your grader-dependent part of your grade is X and your grader's average is A with a standard deviation of D, then Y=(X-A)/D is the number of standard deviations away your score is from your grader's average. Therefore, your normalized grader-dependent part of your grade would be 91.91+Y*9.88 (i.e., same number of standard deviation away from the overall class average). Your minimum score is still one point if you have submitted something for grading.

      As I have mentioned in Lecture 1, although we assume that we have a bell-shaped curve, when your score is normalized, linear interpolation is used. It's clearly not perfect since the actual curve will never be bell-shaped and linear interpolation is not the same as bell-shaped-curve interpolation. But this is what was announced at the beginning of the semester, and therefore, we will stick to this particular way of normailzation for all the programming assignments for the rest of the semester, knowing that it's not perfect.


  • 4/4/2019:
    • I am sick today and I won't be able to come to campus today. So, today's AM and PM sections lectures are canceled. If you are in the AM and PM sections, please watch the DEN lecture video that was recorded yesterday. Next week's lecture will pick up where yesterday's lecture left off. Sorry about the inconvenience.

  • 4/1/2019:
    • Vignendra Jannela's helpdesk hour this Thursday is cancelled since he will be out of town. He has moved that office to Tuesday (tomorrow) from 12pm to 1pm (right before his regular helpdesk hour).

  • 3/28/2019:
    • Vignendra Jannela's helpdesk hour today will be in SAL, Room 109A (further inside SAL 126) since the open area is completely occupied.

  • 3/23/2019:
    • Chien-Lun Chen's office hour on Monday, 3/25/2019 will only go from 10:50am to 11:20am. Sorry about the inconvenience.

  • 3/17/2019:
    • Below is the grade normalization information for warmup2. Please note that this only applies to the grader-dependent part of your grade. If you are graded by Parth Kapadia <pkapadia@usc.edu>, his warmup2 average was 81.67 with a standard deviation of 28.54. If you are graded by Vickram Pentyala <vpentyal@usc.edu>, his warmup2 average was 89.75 with a standard deviation of 22.48. If you are graded by Deepa Sreekumar <dsreekum@usc.edu>, her warmup2 average was 76.72 with a standard deviation of 31.03. The overall class average for warmup2 was 82.71 with a standard deviation of 28.10.

      To figure out your normalized score for warmup2, here's what you can do. If your grader-dependent part of your grade is X and your grader's average is A with a standard deviation of D, then Y=(X-A)/D is the number of standard deviations away your score is from your grader's average. Therefore, your normalized grader-dependent part of your grade would be 82.71+Y*28.10 (i.e., same number of standard deviation away from the overall class average). Your minimum score is still one point if you have submitted something for grading.

      As I have mentioned in Lecture 1, although we assume that we have a bell-shaped curve, when your score is normalized, linear interpolation is used. It's clearly not perfect since the actual curve will never be bell-shaped and linear interpolation is not the same as bell-shaped-curve interpolation. But this is what was announced at the beginning of the semester, and therefore, we will stick to this particular way of normailzation for all the programming assignments for the rest of the semester, knowing that it's not perfect.


  • 3/5/2019:  The midterm exam will be closed book, closed notes, and closed everything (and no "cheat sheet"). Also, no calculators, cell phones, or any electronic gadgets are allowed. Please bring a photo ID. Your ID will be collected at the beginning of the exam and will be returned to you when you turn in your exam. Please only go to the exam for the section you are registered. Also, no matter how late you show up for the exam, your exam must end at the same time as everyone else in your section. There will be assigned seating.

    The midterm exam will cover everything from the beginning of the semester to slide 20 of Lecture 17 on 3/4,5/2019, MINUS Chapter 5 (i.e., material in Ch 5 is excluded from the midterm).

    Regarding what types of questions will be on the midterm, please see the Exams section of the course description web page and slides 22 through 31 of Lecture 17 on 3/4,5/2019. Regarding regrade policy, please see the Regrade section of the course description web page.

    Here is a quick summary of the midterm exam topics (not all topics covered may be listed):

    • Ch 1 - Introduction
      • introduction
      • a simple OS
      • files
    • Ch 2 - Multithreaded Programming
      • thread creation, termination, synchronization
      • thread safety, deviations
    • Ch 3 - Basic Concepts
      • context switching, I/O
      • dynamic storage allocation
      • static linking and loading
      • booting
    • Ch 4 - Operating-System Design
      • a simple system
      • storage management
    • Warmup assignments 1 & 2
      • specs
      • FAQs
      • my posts to class Google Group
    • Kernel assignment 1
      • spec
      • FAQ
      • my posts to class Google Group

    Please note that kernel 1 is included in the midterm coverage but Chaper 5 is not. This mean that I can ask weenix-specific questions.


  • 2/18/2019:
    • Just like a few weeks ago, we will have a make-up recording for today's lecture and it will be done at the time of the PM section lecture tomorrow. Sorry about the inconvenience, but this is the only time the schedule would work. Tomorrow's PM section lecture (12:30-1:50pm on 2/19/2019) is cancelled so I can record today's lecture on DEN. I will be giving the AM section lecture (9:30-10:50am) tomorrow as before (although there will be no roll sheet signing and everyone will get credit). Please watch the recorded lecture tomorrow night. The lecture this Wed/Thu will pick up where the recorded video leaves off. Both lectures will be very closely related to your kernel 1 assignment!

  • 2/4/2019:
    • Below is the grade normalization information for warmup1. Please note that this only applies to the grader-dependent part of your grade. If you are graded by Parth Kapadia <pkapadia@usc.edu>, his warmup1 average was 83.62 with a standard deviation of 24.97. If you are graded by Vickram Pentyala <vpentyal@usc.edu>, his warmup1 average was 87.35 with a standard deviation of 22.00. If you are graded by Deepa Sreekumar <dsreekum@usc.edu>, her warmup1 average was 84.93 with a standard deviation of 22.47. The overall class average for warmup1 was 85.30 with a standard deviation of 23.24.

      To figure out your normalized score for warmup1, here's what you can do. If your grader-dependent part of your grade is X and your grader's average is A with a standard deviation of D, then Y=(X-A)/D is the number of standard deviations away your score is from your grader's average. Therefore, your normalized grader-dependent part of your grade would be 85.30+Y*23.24 (i.e., same number of standard deviation away from the overall class average). Your minimum score is still one point if you have submitted something for grading.

      As I have mentioned in Lecture 1, although we assume that we have a bell-shaped curve, when your score is normalized, linear interpolation is used. It's clearly not perfect since the actual curve will never be bell-shaped and linear interpolation is not the same as bell-shaped-curve interpolation. But this is what was announced at the beginning of the semester, and therefore, we will stick to this particular way of normailzation for all the programming assignments for the rest of the semester, knowing that it's not perfect.


  • 1/11/2019:
    • I have a scheduling conflict today and I won't be able to lead the 11am discussion section. Therefore, I'm canceling today's 11am discussion section. For the students who are registered for the 11am discussion section, please watch the recorded video on D2L (just like lectures, all 3 discussion sections cover the same material). Sorry about the inconvenience.

  • 12/27/2018:
    • Watch this area for important announcements.

    • To get user ID and password for accessing protected area of this web site, please visit the request access page after semester starts and submit the requested information. (You do not have to be registered for the course to get the password. You just need to have an USC e-mail address.)

    • Please do not send request to join the class Google Group until after the first lecture.
In the official syllabus, it is listed that the prerequisites are:
(CSCI 201L or CSCI 455x) and (EE 357 or EE 352L)

Please see:

Apparently, they are the prerequisites for undergraduate students only. The CS department would waive these prerequisites for graduate students. Since undergraduate students are required to take CS 350 for OS credit, there should only be graduate students enrolled in CS 402. Therefore, these prerequisites are really not prerequisites. They should be considered recommended preparation for graduate students. The basic idea behind these prerequisites is that you are expected to know how to program and you are expected to know something about computer architecture (such as what the CPU does).
 
The programming assignments of this class will be very demanding. You will be required to write C code. Since C is a proper subset of C++, knowing C++ well would give you enough background. However, some of the things that available in C++, such as strings and streams, are not be available in C. So, you need to know how to do things such as manipulating null-terminated array of characters (using functions such as strchr, strrchr, strlen, strcmp, strncpy, etc.) and performing console and file I/O (using functions such as printf/snprintf, fread/fwrite, read/write, fgets, etc.) in C. No other programming language will be accepted. We will not teach C in this class. You are expected to pick up C on your own if you are not familiar with it.

You should also get familiar with the Unix development environment (vi/pico/emacs, cc/gcc, make, etc.) You are expected to know how to use Unix. If you are not familiar with Unix, please read Unix for the Beginning Mage, a tutorial written by Joe Topjian. You can also visit UNIX Tutorial for Beginners or Learn tcsh in Y Minutes. If you knew how to use Unix/Linux before and just need a refresher, please review a summary of some commonly used Unix commands.

The kernel programming assignments must run on Ubuntu 16.04. Therefore, you should install Ubuntu 16.04 on your laptop or desktop as soon as possible. If you do not have a personal laptop or desktop that runs Windows or Mac OS X, please contact the instructor as soon as possible. If you are considering buying a laptop, please buy a laptop that runs Windows or Mac OS X.

These days, I have been using VagrantBox (i.e., Vagrant with Virtualbox) to install and run Ubuntu 16.04. I think it has a better integration with Windows 10 than other systems. The down side is that it does not have a desktop environment. If you would prefer to run Ubuntu Linux without a desktop, You can install Vagrant on your laptop/desktop.

If a student registered late for this class or could not be present at the beginning of the semester, he/she is still required to turn all projects and homeworks on time or he/she will receive a score of 0 for these assignments. No exceptions!