

Operating Systems 
CSCI 402, Summer 2017

Click here to see a PREVIEW of important rules
that was posted before the summer session 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.)

 AM Section (30026D+29947D)
 PM Section (29900D)

Time 
TT 10:00am  11:55am 
TT 1:00pm  2:55pm 
Location 
OHE 122 
(NEW) ZHS 163 
TAs 
SungHan Lin,
Email:
<sunghan@usc.edu>
Office Hours: Mon/Tue/Wed 1:00pm  2:00pm in SAL open lab (in front of SAL 126)

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, Thu, 7/6/2017 (firm)

during class time, Thu, 7/6/2017 (firm)

Final Exam 
10:00am11:55am, Tue, 8/1/2017 (firm).

1:00pm2:55pm, Tue, 8/1/2017 (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)
 8/4/2017:
 Below is the grade normalization information for kernel3.
If you are graded by
Chujun Geng <chujunge@usc.edu>,
his kernel3 average was 94.27 with a standard deviation of 20.13.
If you are graded by Jichao Zhong <jichaozh@usc.edu>,
his kernel3 average was 81.81 with a standard deviation of 28.15.
The overall average for kernel3 was 88.24 with a standard deviation of 25.12.
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 88.24+Y*25.12 (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.
 7/28/2017:
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 62 of
Lecture 9 to slide 83 of
Lecture 10
PLUS from slide 16 of Lecture 12 to the last slide of
Lecture 21.
Also included are discussion section slides from Week 7
through Week 10.
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 20
(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
 7/21/2017:
 Below is the grade normalization information for kernel2.
If you are graded by
Chujun Geng <chujunge@usc.edu>,
his kernel2 average was 96.12 with a standard deviation of 14.44.
If you are graded by Jichao Zhong <jichaozh@usc.edu>,
his kernel2 average was 85.07 with a standard deviation of 16.29.
The overall average for kernel2 was 90.42 with a standard deviation of 16.38.
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 90.42+Y*16.38 (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.
 7/15/2017:
 Due to regrade appointments, the TA will not be holding office hours on 7/17/2017, 7/18/2017, and 7/19/2017.
 Due to regrade appointments, my office hour on 7/18/2017 and 7/20/2017 will be 12:00pm12:35pm and 3:00pm3:25pm.
 7/4/2017:
 Below is the grade normalization information for kernel1.
If you are graded by
Chujun Geng <chujunge@usc.edu>,
his kernel1 average was 91.95 with a standard deviation of 12.12.
If you are graded by Jichao Zhong <jichaozh@usc.edu>,
his kernel1 average was 95.04 with a standard deviation of 11.57.
The overall average for kernel1 was 93.45 with a standard deviation of 11.96.
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 93.45+Y*11.96 (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 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.
 6/28/2017:
 Below is the grade normalization information for warmup2.
If you are graded by
Chujun Geng <chujunge@usc.edu>,
his warmup2 average was 93.01 with a standard deviation of 21.36.
If you are graded by Jichao Zhong <jichaozh@usc.edu>,
his warmup2 average was 82.33 with a standard deviation of 31.47.
The overall average for warmup2 was 87.67 with a standard deviation of 27.42.
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 87.67+Y*27.42 (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.
 6/27/2017:
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 13 of Lecture 12 on 6/27/2017,
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 15 through 21 of Lecture 12 on 6/27/2017.
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
 6/9/2017:
 Below is the grade normalization information for warmup1.
If you are graded by
Chujun Geng <chujunge@usc.edu>,
his warmup1 average was 94.27 with a standard deviation of 23.81.
If you are graded by Jichao Zhong <jichaozh@usc.edu>,
his warmup1 average was 97.15 with a standard deviation of 13.60.
The overall average for warmup1 was 95.69 with a standard deviation of 19.49.
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=(XA)/D is how many numbers of standard deviations away you score is from your grader's average.
Therefore, your normalized score would be 95.69+Y*19.49 (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 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.
 5/30/2017:
 Please noticed that I have updated my schedule for office hours.
 Roll sheet signing starts this Thursday since students can still register for this class today!
 5/19/2017:
 As I have mentioned in Lecture 1, the TA is out of town and he will not be conducting the 11am discussion section today.
If you go to OHE 132 at 11am, you will see a prerecorded lecture that I recorded yesterday.
The prerecorded discussion section lecture is available to all. You should watch the lecture video as soon as possible.
 The 12pm discussion section is brand new and we don't have a TA for it yet. Therefore, it's canceled today. Sorry about the inconvenience.
 I have asked DEN to make the lecture and discussion section videos available to both sections of this class.
I'm hoping that this will be working by the end of today.
 5/18/2017:
 The office hour today is shortened to be only 45 minutes long (from 12:0012:45pm) because
I have to prerecord tomorrow's discussion session at 1pm.
Sorry about the inconvenience and the short notice.
 5/17/2017:
 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 summer session 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!


