

Operating Systems 
CSCI 402, Fall 2017, All Sections

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.


General Information


Instructor 
Bill Cheng
(click to see office hours)
Email:
<bill.cheng@usc.edu>. (Please do not send HTMLonly emails.
They will not be read.)

 DEN Section (29945D+29946D)
 PM Section (30243D)
 TT Section (30203D)

Time 
MW 10:00am  11:50am 
MW 12:00pm  1:50pm 
TT 9:30am  10:50am 
Location 
OHE 122 
SGM 226 
VHE 217 
TA 
Kwame Wright,
Email:
<kwamelaw@usc.edu>
Office Hours: Mon 3:00pm  5:00pm in SAL open lab (in front of SAL 126), or by appointment

Long Li,
Email:
<longl@usc.edu>
Office Hours: Tue 2:00pm  3:00pm in SAL open lab (in front of SAL 126), or by appointment

Ben Yan,
Email:
<wumoyan@usc.edu>
Office Hours: Fri 8:00am  10:00am in SAL open lab (in front of SAL 126), or by appointment

Graders 
Chujun Geng, Email: <chujunge@usc.edu>
 Jichao Zhong, Email: <jichaozh@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, 10/25/2017 (firm)

during class time, Wed, 10/25/2017 (firm)

during class time, Thu, 10/26/2017 (firm)

Final Exam 
8am10am, Mon, 12/11/2017 (firm).

11am1pm, Fri, 12/8/2017 (firm).

11am1pm, Thu, 12/7/2017 (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.



News

(in reversed chronological order)
 12/15/2017:
 Below is the grade normalization information for kernel3.
If you are graded by
Chujun Geng <chujunge@usc.edu>,
his kernel3 average was with a standard deviation of .
If you are graded by Jichao Zhong <jichaozh@usc.edu>,
his kernel3 average was with a standard deviation of .
The overall average for kernel3 was with a standard deviation of .
To figure out your normalized score for kernel3, 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=(XA)/D is how many numbers of standard deviations away you score is from your grader's average.
Therefore, your normalized score would be +Y* (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 bellshaped curve,
when your score is normalized, linear interpolation is used. It's clearly not perfect since
the actual curve will never be bellshaped and linear interpolation is not the same as bellshapedcurve 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.
 12/5/2017:
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 39 of
Lecture 12 to slide 51 of
Lecture 14
PLUS from slide 1 of Lecture 17 to the last slide of
Lecture 30.
Also included are discussion section slides from Week 9
through Week 13.
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).
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.
Here is a quick summary of the final exam topics (not all topics covered may be listed):
 Ch 3  Basic Concepts
 Ch 4  OperatingSystem 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
 Kernel assignments 2 & 3
 spec
 FAQ
 my posts to class Google Group
 11/18/2017:
 Below is the grade normalization information for kernel2.
If you are graded by
Chujun Geng <chujunge@usc.edu>,
his kernel2 average was 95.72 with a standard deviation of 14.36.
If you are graded by Jichao Zhong <jichaozh@usc.edu>,
his kernel2 average was 96.11 with a standard deviation of 20.88.
The overall average for kernel2 was 95.90 with a standard deviation of 17.79.
To figure out your normalized score for kernel2, 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=(XA)/D is how many numbers of standard deviations away you score is from your grader's average.
Therefore, your normalized score would be 95.90+Y*17.79 (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 bellshaped curve,
when your score is normalized, linear interpolation is used. It's clearly not perfect since
the actual curve will never be bellshaped and linear interpolation is not the same as bellshapedcurve 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.
 10/30/2017:
 Below is the grade normalization information for kernel1.
If you are graded by
Chujun Geng <chujunge@usc.edu>,
his kernel1 average was 98.45 with a standard deviation of 11.13.
If you are graded by Jichao Zhong <jichaozh@usc.edu>,
his kernel1 average was 93.98 with a standard deviation of 10.48.
The overall average for kernel1 was 96.13 with a standard deviation of 11.02.
To figure out your normalized score for kernel1, 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=(XA)/D is how many numbers of standard deviations away you score is from your grader's average.
Therefore, your normalized score would be 96.13+Y*11.02 (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 bellshaped curve,
when your score is normalized, linear interpolation is used. It's clearly not perfect since
the actual curve will never be bellshaped and linear interpolation is not the same as bellshapedcurve 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.
 10/17/2017:
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 43 of Lecture 16 on 10/11,12/2017,
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 2 through 9 of Lecture 17 on 10/16,17/2017.
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  OperatingSystem 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 weenixspecific questions.
 10/11/2017:
 Below is the grade normalization information for warmup2.
If you are graded by
Chujun Geng <chujunge@usc.edu>,
his warmup2 average was 82.48 with a standard deviation of 30.70.
If you are graded by Jichao Zhong <jichaozh@usc.edu>,
his warmup2 average was 79.50 with a standard deviation of 29.51.
The overall average for warmup2 was 81.02 with a standard deviation of 30.16.
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=(XA)/D is how many numbers of standard deviations away you score is from your grader's average.
Therefore, your normalized score would be 81.02+Y*30.16 (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 bellshaped curve,
when your score is normalized, linear interpolation is used. It's clearly not perfect since
the actual curve will never be bellshaped and linear interpolation is not the same as bellshapedcurve 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.
 10/9/2017:
 The following has been canceled! So, I will have regular office hours this Wednesday (10/11/2017) and Thursday (10/12/2017).
I have to cancel my office hour this Thursday (10/12/2017) due to a dental appointment.
I'm extending my office hour this Wednesday by one hour (to go from 2pm to 4pm) to make up for it.
Sorry about the inconvenience.
 9/25/2017:
 I kind of need to leave campus earlier today. For today's office hour, I will stay in my office till 2:15pm.
I will continue to keep the office hour going as long as there are students who wants to talk to me.
But as soon as the queue is empty, I will end the office hour.
Tomorrow's office will be extended to go from 11pm to 12:45pm.
Sorry about the short notice and inconvenience.
 9/20/2017:
 Since I cut office hour short yesterday, office hour today will go till 3:30pm.
Sorry about the short notice.
 9/19/2017:
 I have to leave campus at 11:30am today. So, today's office hour today will only go from 10:55am to 11:30am.
Sorry about the inconvenience and the short notice.
 9/17/2017:
 Below is the grade normalization information for warmup1.
If you are graded by
Chujun Geng <chujunge@usc.edu>,
his warmup1 average was 92.57 with a standard deviation of 22.07.
If you are graded by Jichao Zhong <jichaozh@usc.edu>,
his warmup1 average was 83.99 with a standard deviation of 32.43.
The overall average for warmup1 was 88.28 with a standard deviation of 28.07.
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=(XA)/D is how many numbers of standard deviations away you score is from your grader's average.
Therefore, your normalized score would be 88.28+Y*28.07 (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 bellshaped curve,
when your score is normalized, linear interpolation is used. It's clearly not perfect since
the actual curve will never be bellshaped and linear interpolation is not the same as bellshapedcurve 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.
 9/9/2017:
 Starting with next week (and all the way till the end of the semester), my Tu/Th
office hours will be from 11am to 12pm. Sorry if this cause any inconvenience.
If you can only come by on Tu/Th between 12pm and 1pm, please make an appointment by sending me email.
 8/27/2017:
 The office hour tomorrow (8/28/2017) is canceled because I have to leave campus at 2pm.
My office hour on Tuesday, 8/29/2017 will to from 11am to 1pm to make up for the lost hour.
Sorry about the inconvenience and the short notice.
 8/7/2017:
 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 email address.)
 Please do not send request to join the
class Google Group until after the first lecture.


Prerequisites

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).


Important Information about Programming Assignments

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 nullterminated 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.
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)
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!


