Operating Systems - CSCI 402, Spring 2018, All Sections

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)	PM Section (29971D)	TT Section (30331D)
Time	MW 9:30am - 10:50am	MW 12:00pm - 1:50pm	TT 9:30am - 10:50am
Location	OHE 100D	GFS 116	GFS 116
TA	Ben Yan, E-mail: <wumoyan@usc.edu> Office Hours: Fri 8:00am - 10:00am in SAL open lab (in front of SAL 126)	Long Li, E-mail: <longl@usc.edu> Office Hours: Mon 3:00pm - 4:00pm in SAL open lab (in front of SAL 126)	Yue Shi, E-mail: <yueshi@usc.edu> Office Hours: M/W 10:30am - 11:30am in SAL open lab (in front of SAL 126)
Course Producer	Chujun Geng, E-mail: <chujunge@usc.edu>, Helpdesk Hours: Tue/Thu 3:30pm - 5:00pm in SAL open lab (in front of SAL 126)
Graders	Aparna Subbaraya, E-mail: <asubbara@usc.edu> Erli Wang, E-mail: <erliwang@usc.edu> Shreesh Kulkarni, E-mail: <shreeshk@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, 3/21/2018 (firm) (if your nunki login ID begins with "a", "b", or "c", please go to RTH 109)	during class time, Wed, 3/21/2018 (firm)	during class time, Thu, 3/22/2018 (firm)
Final Exam	8am-10am, Fri, 5/4/2018 (firm), in THH 102,  (THH is located in section 4E of the campus map).	11am-1pm, Fri, 5/4/2018 (firm).	8am-10am, Tue, 5/8/2018 (firm).

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.

• 5/6/2018:
  • Below is the grade normalization information for kernel3. Please note that this only applies to the grader-dependent part of your grade. If you are graded by Aparna Subbaraya <asubbara@usc.edu>, her kernel3 average was 91.13 with a standard deviation of 17.80. If you are graded by Erli Wang <erliwang@usc.edu>, his kernel3 average was 86.99 with a standard deviation of 14.13. If you are graded by Shreesh Kulkarni <shreeshk@usc.edu>, his kernel3 average was 80.12 with a standard deviation of 29.99. The overall class average for kernel3 was 86.30 with a standard deviation of 21.61.

    To figure out your normalized score for kernel3, 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 86.30+Y*21.61 (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.

• 4/25/2018: 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. There will be assigned seating.

  The final exam will cover everything from slide 21 of Lecture 13 to slide 37 of Lecture 15 PLUS from slide 29 of Lecture 17 to the last slide of Lecture 30. Also included are discussion section slides from Week 9 through Week 14.

  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 29 (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

• 4/21/2018:
  • 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 Aparna Subbaraya <asubbara@usc.edu>, her kernel2 average was 96.57 with a standard deviation of 4.33. If you are graded by Erli Wang <erliwang@usc.edu>, his kernel2 average was 93.83 with a standard deviation of 14.29. If you are graded by Shreesh Kulkarni <shreeshk@usc.edu>, his kernel2 average was 96.08 with a standard deviation of 6.71. The overall class average for kernel2 was 95.54 with a standard deviation of 9.35.

    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 95.54+Y*9.35 (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.

• 3/31/2018:
  • 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 Aparna Subbaraya <asubbara@usc.edu>, her kernel1 average was 90.00 with a standard deviation of 16.44. If you are graded by Erli Wang <erliwang@usc.edu>, his kernel1 average was 87.86 with a standard deviation of 23.02. If you are graded by Shreesh Kulkarni <shreeshk@usc.edu>, his kernel1 average was 93.13 with a standard deviation of 10.69. The overall class average for kernel1 was 90.37 with a standard deviation of 17.52.

    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 90.37+Y*17.52 (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.

• 3/13/2018:
  • Below is the udpated grade normalization information for warmup2. Please note that this only applies to the grader-dependent part of your grade. If you are graded by Aparna Subbaraya <asubbara@usc.edu>, her warmup2 average was 83.06 with a standard deviation of 26.09. If you are graded by Erli Wang <erliwang@usc.edu>, his warmup2 average was 80.26 with a standard deviation of 28.59. If you are graded by Shreesh Kulkarni <shreeshk@usc.edu>, his warmup2 average was 77.28 with a standard deviation of 31.61. The overall class average for warmup2 was 80.19 with a standard deviation of 28.96.

    To figure out your normalized score for warmup2, here's what you can do. If your score 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 score would be 80.19+Y*28.96 (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.

• 3/13/2018:
  • Below is the udpated grade normalization information for warmup1. Please note that this only applies to the grader-dependent part of your grade. If you are graded by Aparna Subbaraya <asubbara@usc.edu>, her warmup1 average was 87.21 with a standard deviation of 24.16. If you are graded by Erli Wang <erliwang@usc.edu>, his warmup1 average was 91.36 with a standard deviation of 17.41. If you are graded by Shreesh Kulkarni <shreeshk@usc.edu>, his warmup1 average was 90.54 with a standard deviation of 19.11. The overall class average for warmup1 was 89.72 with a standard deviation of 20.49.

    To figure out your normalized score for warmup1, here's what you can do. If your score 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 score would be 89.72+Y*20.49 (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.

• 3/7/2018: 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. There will be assigned seating.

  The midterm exam will cover everything from the beginning of the semester to slide 19 of Lecture 17 on 3/5,6/2018, 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 20 through 28 of Lecture 17 on 3/5,7/2018. 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.

• 3/6/2018:
  • Below is the grade normalization information for warmup2. If you are graded by Aparna Subbaraya <asubbara@usc.edu>, her warmup2 average was 85.81 with a standard deviation of 27.93. If you are graded by Erli Wang <erliwang@usc.edu>, his warmup2 average was 83.62 with a standard deviation of 30.90. If you are graded by Shreesh Kulkarni <shreeshk@usc.edu>, his warmup2 average was 80.89 with a standard deviation of 33.51. The overall class average for warmup2 was 83.43 with a standard deviation of 30.95.

    To figure out your normalized score for warmup2, here's what you can do. If your score 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 score would be 83.43+Y*30.95 (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.

• 2/18/2018:
  • Below is the grade normalization information for warmup1. If you are graded by Aparna Subbaraya <asubbara@usc.edu>, her warmup1 average was 89.90 with a standard deviation of 26.98. If you are graded by Erli Wang <erliwang@usc.edu>, his warmup1 average was 95.53 with a standard deviation of 19.51. If you are graded by Shreesh Kulkarni <shreeshk@usc.edu>, his warmup1 average was 92.90 with a standard deviation of 24.08. The overall class average for warmup1 was 92.78 with a standard deviation of 23.83.

    To figure out your normalized score for warmup1, here's what you can do. If your score 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 score would be 92.78+Y*23.83 (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.

• 2/6/2018:
  • I'm still quite sick and I don't think I can handle giving two lectures a day for the next 2 days. So, I will still give a lecture tomorrow in the DEN section so it can be recorded and I'm canceling PM and TT sections lectures on tomorrow and Thursday. There will be no roll sheet signing and everyone will get credit.
  • Office hour tomorrow (Wed, 2/7/2018) is moved to 1:30-1:55pm followed by 2:35-3:10pm. Sorry about the inconvenience.

• 2/5/2018:
  • Office hour tomorrow (Tue, 2/6/2018) is canceled since I'm still sick. Sorry about the inconvenience.

• 2/5/2018:
  • PM section lecture today and TT section lecture tomorrow are canceled because I'm sick. I will still lecture today during the DEN section so the lecture can be recorded. Please watch this lecture video before class on Wednesday/Thursday. No roll sheet signing today and tomorrow. Everyone will get credit for it.
  • Office hour today is canceled for the same reason. If you have questions about the programming assignments, please see the CP and the TAs. Sorry about the inconvenience and the short notice.

• 1/7/2018:
  • 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.

(CSCI 201L or CSCI 455x) and (EE 357 or EE 352L)

Please see:

• CSci 201L (Principles of Software Development) or CSci 455x (Introduction to Programming Systems Design).
• EE 357 (Basic Organization of Computer Systems) or EE 352L (Computer Organization and Architecture).

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. If you forgot how to use Unix, please summary of some commonly used Unix commands. The kernel programming assignments must run on Ubuntu 12.04 or Ubuntu 14.04. Therefore, you should install Ubuntu 14.04 (or Ubuntu 12.04) 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. Please note that the preferred version of Ubuntu is Ubuntu 14.04 (unless you have a laptop with only 2GB of memory or a slow CPU, then you should install Ubuntu 12.04)

These days, I have been using VagrantBox (i.e., Vagrant with Virtualbox) to install and run Ubuntu 14.04. I think it has a better integration with Windows 10 than other systems. If you are running Windows, I would recommend installing 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!