程序代写代做代考 graph compiler Java x86 cache algorithm computer architecture distributed system cache simulator C c++ assembly go database concurrency data structure Carnegie Mellon

Carnegie Mellon
Bryant and O’Hallaron, Computer Systems: A Programmer’s Perspective, Third Edition
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Carnegie Mellon
Course Overview
15-213/18-213/15-513: Introduction to Computer Systems 1st Lecture, May 19, 2020
Instructors:
Brian Railing
The course that gives CMU its “Zip”!
Bryant and O’Hallaron, Computer Systems: A Programmer’s Perspective, Third Edition
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Carnegie Mellon
Overview
 Big Picture
▪ Course theme
▪ Five realities
▪ How the course fits into the CS/ECE curriculum
 Academic integrity
 Logistics and Policies
Bryant and O’Hallaron, Computer Systems: A Programmer’s Perspective, Third Edition
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The Big Picture
Bryant and O’Hallaron, Computer Systems: A Programmer’s Perspective, Third Edition
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 Why take this course?
 What do you want to learn?
Bryant and O’Hallaron, Computer Systems: A Programmer’s Perspective, Third Edition
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Course Theme:
(Systems) Knowledge is Power!
 Systems Knowledge
▪ How hardware (processors, memories, disk drives, network infrastructure) plus software (operating systems, compilers, libraries, network protocols) combine to support the execution of application programs
▪ How you as a programmer can best use these resources
 Useful outcomes from taking 213/513 ▪ Become more effective programmers
▪ Able to find and eliminate bugs efficiently
▪ Able to understand and tune for program performance ▪ Prepare for later “systems” classes in CS & ECE
▪ Compilers, Operating Systems, Networks, Computer Architecture, Embedded Systems, Storage Systems, etc.
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It’s Important to Understand How Things Work
 Why do I need to know this stuff?
▪ Abstraction is good, but don’t forget reality
 Most CS and CE courses emphasize abstraction ▪ Abstract data types
▪ Asymptotic analysis
 These abstractions have limits
▪ Especially in the presence of bugs
▪ Need to understand details of underlying implementations
▪ Sometimes the abstract interfaces don’t provide the level of control or
performance you need
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Great Reality #1:
Ints are not Integers, Floats are not Reals  Example 1: Is x2 ≥ 0?
▪ Float’s: Yes!
▪ Int’s:
▪ 40000 * 40000 –> 1600000000 ▪ 50000 * 50000 –> ?
 Example 2: Is (x + y) + z = x + (y + z)? ▪ Unsigned & Signed Int’s: Yes!
▪ Float’s:
▪ (1e20 + -1e20) + 3.14 –> 3.14
▪ 1e20 + (-1e20 + 3.14) –> ?? Bryant and O’Hallaron, Computer Systems: A Programmer’s Perspective, Third Edition
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Computer Arithmetic
 Does not generate random values
▪ Arithmetic operations have important mathematical properties
 Cannot assume all “usual” mathematical properties ▪ Due to finiteness of representations
▪ Integer operations satisfy “ring” properties
▪ Commutativity, associativity, distributivity
▪ Floating point operations satisfy “ordering” properties
▪ Monotonicity, values of signs
 Observation
▪ Need to understand which abstractions apply in which contexts
▪ Important issues for compiler writers and serious application programmers
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Great Reality #2:
You’ve Got to Know Assembly
 Chances are, you’ll never write programs in assembly
▪ Compilers are much better & more patient than you are
 But: Understanding assembly is key to machine-level execution model
▪ Behavior of programs in presence of bugs ▪ High-level language models break down
▪ Tuning program performance
▪ Understand optimizations done / not done by the compiler ▪ Understanding sources of program inefficiency
▪ Implementing system software
▪ Compiler has machine code as target
▪ Operating systems must manage process state
▪ Creating / fighting malware
▪ x86 assembly is the language of choice! Bryant and O’Hallaron, Computer Systems: A Programmer’s Perspective, Third Edition
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Great Reality #3: Memory Matters
Random Access Memory Is an Unphysical Abstraction
 Memory is not unbounded
▪ It must be allocated and managed
▪ Many applications are memory dominated
 Memory referencing bugs especially pernicious ▪ Effects are distant in both time and space
 Memory performance is not uniform
▪ Cache and virtual memory effects can greatly affect program performance
▪ Adapting program to characteristics of memory system can lead to major speed improvements
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Memory Referencing Bug Example
typedef struct {
int a[2];
double d;
} struct_t;
double fun(int i) {
volatile struct_t s;
s.d = 3.14;
s.a[i] = 1073741824; /* Possibly out of bounds */ return s.d;
}
fun(0) –> fun(1) –> fun(2) –> fun(3) –> fun(4) –> fun(6) –>
3.14
3.14 3.1399998664856 2.00000061035156 3.14
Segmentation fault
▪ Result is system specific
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Memory Referencing Bug Example
typedef struct {
int a[2];
double d;
} struct_t;
Explanation:
fun(0) –> fun(1) –> fun(2) –> fun(3) –> fun(4) –> fun(6) –>
6 5 4 3 2 1 0
3.14
3.14 3.1399998664856 2.00000061035156 3.14
Segmentation fault
Critical State
?
?
d7 … d4
d3 … d0
a[1]
a[0]
struct_t
Bryant and O’Hallaron, Computer Systems: A Programmer’s Perspective, Third Edition
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Location accessed by
fun(i)

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Memory Referencing Errors
 C and C++ do not provide any memory protection ▪ Out of bounds array references
▪ Invalid pointer values ▪ Abuses of malloc/free
 Can lead to nasty bugs
▪ Whether or not bug has any effect depends on system and compiler ▪ Action at a distance
▪ Corrupted object logically unrelated to one being accessed
▪ Effect of bug may be first observed long after it is generated
 How can I deal with this?
▪ Program in Java, Ruby, Python, ML, …
▪ Understand what possible interactions may occur
▪ Use or develop tools to detect referencing errors (e.g. Valgrind)
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Great Reality #4: There’s more to performance than asymptotic complexity
 Constant factors matter too!
 And even exact op count does not predict performance
▪ Easily see 10:1 performance range depending on how code written
▪ Must optimize at multiple levels: algorithm, data representations, procedures, and loops
 Must understand system to optimize performance
▪ How programs compiled and executed
▪ How to measure program performance and identify bottlenecks
▪ How to improve performance without destroying code modularity and
generality
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Memory System Performance Example
void copyij(int src[2048][2048],
int dst[2048][2048])
{
int i,j;
for (i = 0; i < 2048; i++) for (j = 0; j < 2048; j++) } dst[i][j] = src[i][j]; 4.3ms 81.8ms void copyji(int src[2048][2048], int dst[2048][2048]) { int i,j; for (j = 0; j < 2048; j++) for (i = 0; i < 2048; i++) } 2.0 GHz Intel Core i7 Haswell  Hierarchical memory organization  Performance depends on access patterns ▪ Including how step through multi-dimensional array Bryant and O’Hallaron, Computer Systems: A Programmer’s Perspective, Third Edition 16 dst[i][j] = src[i][j]; Carnegie Mellon Why The Performance Differs copyij 16000 14000 12000 10000 8000 6000 4000 2000 0 copyji s1 s3 Stride (x8 bytes) 32k 128k s5 s7 8m 32m 2m 512k Size (bytes) s9 Bryant and O’Hallaron, Computer Systems: A Programmer’s Perspective, Third Edition 17 s11 128m Read throughput (MB/s) Carnegie Mellon Great Reality #5: Computers do more than execute programs  They need to get data in and out ▪ I/O system critical to program reliability and performance  They communicate with each other over networks ▪ Many system-level issues arise in presence of network ▪ Concurrent operations by autonomous processes ▪ Coping with unreliable media ▪ Cross platform compatibility ▪ Complex performance issues Bryant and O’Hallaron, Computer Systems: A Programmer’s Perspective, Third Edition 18 Carnegie Mellon Course Perspective  Most Systems Courses are Builder-Centric ▪ Computer Architecture ▪ Design pipelined processor in Verilog ▪ Operating Systems ▪ Implement sample portions of operating system ▪ Compilers ▪ Write compiler for simple language ▪ Networking ▪ Implement and simulate network protocols Bryant and O’Hallaron, Computer Systems: A Programmer’s Perspective, Third Edition 19 Carnegie Mellon Course Perspective (Cont.)  Our Course is Programmer-Centric ▪ By knowing more about the underlying system, you can be more effective as a programmer ▪ Enable you to ▪ Write programs that are more reliable and efficient ▪ Incorporate features that require hooks into OS – E.g., concurrency, signal handlers ▪ Cover material in this course that you won’t see elsewhere ▪ Not just a course for dedicated hackers ▪ We bring out the hidden hacker in everyone! Bryant and O’Hallaron, Computer Systems: A Programmer’s Perspective, Third Edition 20 Carnegie Mellon Role within CS/ECE Curriculum Foundation of Computer Systems Underlying principles for hardware, software, and networking CS Systems • 15-319 • 15-330 • 15-410 • 15-411 • 15-415 • 15-418 • 15-440 • 15-441 • 15-445 Cloud Computing Computer Security Operating Systems Compiler Design Database Applications Parallel Computing Distributed Systems Computer Networks Database Systems 213/513 ECE Systems CS 122 Imperative Programming • 18-349 • 18-349 • 18-441 • 18-447 • 18-452 • 18-451 Computer Security Intro to Embedded Systems Computer Networks Computer Architecture Wireless Networking Cyberphysical Systems Bryant and O’Hallaron, Computer Systems: A Programmer’s Perspective, Third Edition 21 CS Graphics • 15-462 Computer Graphics • 15-463 Comp. Photography Carnegie Mellon Academic Integrity Please pay close attention, especially if this is your first semester at CMU Bryant and O’Hallaron, Computer Systems: A Programmer’s Perspective, Third Edition 22 Carnegie Mellon Cheating/Plagiarism: Description  Unauthorized use of information ▪ Borrowing code: by copying, retyping, looking at a file ▪ Describing: verbal description of code from one person to another. ▪ Searching the Web for solutions ▪ Copying code from a previous course or online solution ▪ Reusing your code from a previous semester (here or elsewhere) Bryant and O’Hallaron, Computer Systems: A Programmer’s Perspective, Third Edition 23 Carnegie Mellon Cheating/Plagiarism: Description (cont.)  Unauthorized supplying of information ▪ Providing copy: Giving a copy of a file to someone ▪ Providing access: ▪ Putting material in unprotected directory ▪ Putting material in unprotected code repository (e.g., Github) ▪ Applies to this term and the future ▪ There is no statute of limitations for academic integrity violations Bryant and O’Hallaron, Computer Systems: A Programmer’s Perspective, Third Edition 24 Carnegie Mellon Cheating/Plagiarism: Description  What is NOT cheating? ▪ Explaining how to use systems or tools ▪ Helping others with high-level design issues ▪ Using code supplied by us ▪ Using code from the CS:APP web site  See the course syllabus for details. ▪ Ignorance is not an excuse Bryant and O’Hallaron, Computer Systems: A Programmer’s Perspective, Third Edition 25 Carnegie Mellon Cheating: Consequences  Penalty for cheating: ▪ Best case: -100% for assignment ▪ You would be better off to turn in nothing ▪ Worst case: Removal from course with failing grade ▪ This is the default ▪ Permanent mark on your record ▪ Loss of respect by you, the instructors and your colleagues ▪ If you do cheat – come clean asap!  Don’t do it! ▪ Manage your time carefully ▪ Ask the staff for help when you get stuck Bryant and O’Hallaron, Computer Systems: A Programmer’s Perspective, Third Edition 26 Carnegie Mellon Cheating Notes  Prof. Railing has written over 100 letters for cheating cases ▪ Don’t add to this total ▪ Some have been for years earlier  Your work is sophisticated enough that there are many different solutions ▪ Things that look the same are very suspicious ▪ If you do your own work and commit regularly, your work is unique  We use PhD-level research to detect similarities ▪ Inputs include: multiple tools, online searches, past semester submissions Bryant and O’Hallaron, Computer Systems: A Programmer’s Perspective, Third Edition 27 Carnegie Mellon Some Concrete Examples:  This is Cheating: ▪ Searching the internet with the phrase 15-213, 15213, 213, 18213, malloclab, etc. ▪ That’s right, just entering it in a search engine ▪ Looking at someone’s code on the computer next to yours ▪ Giving your code to someone else, now or in the future ▪ Posting your code in a publicly accessible place on the Internet, now or in the future ▪ Hacking the course infrastructure  This is OK (and encouraged): ▪ Googling a man page for fputs ▪ Asking a friend for help with gdb ▪ Asking a TA or course instructor for help, showing them your code, ... ▪ Looking in the textbook for a code example ▪ Talking about a (high-level) approach to the lab with a classmate Bryant and O’Hallaron, Computer Systems: A Programmer’s Perspective, Third Edition 28 Carnegie Mellon How it Feels: Student and Instructor  Fred is desperate. He can’t get his code to work and the deadline is drawing near. In panic and frustration, he searches the web and finds a solution posted by a student at U. Oklahoma on Github. He carefully strips out the comments and inserts his own. He changes the names of the variables and functions. Phew! Got it done!  The course staff run checking tools that compare all submitted solutions to the solutions from this and other semesters, along with ones that are on the Web. ▪ Remember: We are as good at web searching as you are  Meanwhile, Fred has had an uneasy feeling: Will I get away with it? Why does my conscience bother me?  Fred gets email from an instructor: “Please see me tomorrow at 9:30 am.” ▪ Fred does not sleep well that night Bryant and O’Hallaron, Computer Systems: A Programmer’s Perspective, Third Edition 29 Carnegie Mellon How it Feels: Student and Instructor  The instructor feels frustrated. His job is to help students learn, not to be police. Every hour he spends looking at code for cheating is time that he cannot spend providing help to students. But, these cases can’t be overlooked  At the meeting: ▪ Instructor: “Explain why your code looks so much like the code on Github.” ▪ Fred: “Gee, I don’t know. I guess all solutions look pretty much alike.” ▪ Instructor: “I don’t believe you. I am going to file an academic integrity violation.” ▪ Fred will have the right to appeal, but the instructor does not need him to admit his guilt in order to penalize him.  Consequences ▪ Fred may (most likely) will be given a failing grade for the course ▪ Fred will be reported to the university ▪ A second AIV will lead to a disciplinary hearing ▪ Fred will go through the rest of his life carrying a burden of shame ▪ The instructor will experience a combination of betrayal and distress Bryant and O’Hallaron, Computer Systems: A Programmer’s Perspective, Third Edition 30 Carnegie Mellon A Scenario: Cheating or Not? Alice is working on malloc lab and is just plain stuck. Her code is seg faulting and she doesn't know why. It is only 2 days until malloc lab is due and she has 3 other assignments due this same week. She is in the cluster. Bob is sitting next to her. He is pretty much done. Sitting next to Bob is Charlie. He is also stuck.  1. Charlie gets up for a break and Bob makes a printout of his own code and leaves it on Charlie’s chair. ▪ Who cheated: Charlie? Bob?  2. Charlie finds the copy of Bob’s malloc code, looks it over, and then copies one function, but changes the names of all the variables. ▪ Who cheated: Charlie? Bob? Bryant and O’Hallaron, Computer Systems: A Programmer’s Perspective, Third Edition 31 Carnegie Mellon Another Scenario Alice is working on malloc lab and is just plain stuck. Her code is seg faulting and she doesn't know why. It is only 2 days until malloc lab is due and she has 3 other assignments due this same week. She is in the cluster. Bob is sitting next to her. He is pretty much done. Sitting next to Bob is Charlie. He is also stuck.  1. Bob offers to help Alice and they go over her code together. ▪ Who cheated: Bob? Alice?  2. Bob gets up to go to the bathroom and Charlie looks over at his screen to see how Bob implemented his free list. ▪ Who cheated: Charlie? Bob? Bryant and O’Hallaron, Computer Systems: A Programmer’s Perspective, Third Edition 32 Carnegie Mellon Another Scenario (cont.)  3. Alice is having trouble with GDB. She asks Bob how to set a breakpoint, and he shows her. ▪ Who cheated: Bob? Alice?  4. Charlie goes to a TA and asks for help ▪ Who cheated: Charlie?  If you are uncertain which of these constitutes cheating, and which do not, please read the syllabus carefully. If you’re still uncertain, ask one of the staff Bryant and O’Hallaron, Computer Systems: A Programmer’s Perspective, Third Edition 33 Carnegie Mellon Version Control: Your Good Friend  All labs will be distributed via GitHub Classroom  Must be used by all students  Students must commit early and often  If a student is accused of cheating (plagiarism), we will consult the GIT server and look for a reasonable commit history  Missing GIT history will count against you  Please make sure you have one! Bryant and O’Hallaron, Computer Systems: A Programmer’s Perspective, Third Edition 34 Carnegie Mellon Logistics Bryant and O’Hallaron, Computer Systems: A Programmer’s Perspective, Third Edition 35 Carnegie Mellon Instructor Carnegie Mellon Bryant and O’Hallaron, Computer Systems: A Programmer’s Perspective, Third Edition 36 Brian Railing Carnegie Mellon 15-213/18-213 and 15-513  15-213/18-213 ▪ Only undergraduates ▪ 12 units ▪ Live lectures via Zoom ▪ Lectures on TWR, F? 12:00-1:20 (see website) ▪ Midterm 12% / Final 18%  15-513 ▪ Only Masters students ▪ 6 units ▪ If you have the proper background, take 6 credits ▪ If this is all new to you, take 12 credits in the Fall ▪ Lectures by video (on the website and panopto) ▪ Midterm 12% / Final 18%  Everything else is the same for all the courses Bryant and O’Hallaron, Computer Systems: A Programmer’s Perspective, Third Edition Carnegie Mellon 37 Carnegie Mellon Lecture Style  You are going to be active learners ▪ Come prepared to class based on the readings / videos ▪ Practice and gain assessment feedback in class ▪ Immediately address misconceptions with expert intervention ▪ You will work in teams (Zoom breakout rooms of ~20) ▪ 70% attendance (~21/30), you are “pre-excused” to miss 9.  You learn by: ▪ Making mistakes ▪ Practicing  If you have questions or concerns, please come by ▪ Or ask your advisor Bryant and O’Hallaron, Computer Systems: A Programmer’s Perspective, Third Edition 38 Carnegie Mellon Textbooks  Randal E. Bryant and David R. O’Hallaron, ▪ Computer Systems: A Programmer’s Perspective, Third Edition (CS:APP3e), Pearson, 2016 ▪ http://csapp.cs.cmu.edu ▪ This book really matters for the course! ▪ How to solve labs ▪ Practice problems typical of exam problems ▪ Digital materials at: https://cmu.redshelf.com/  Brian Kernighan and Dennis Ritchie, ▪ The C Programming Language, Second Edition, Prentice Hall, 1988 ▪ Still the best book about C, from the originators ▪ Even though it does not cover more recent extensions of C Bryant and O’Hallaron, Computer Systems: A Programmer’s Perspective, Third Edition 39 Carnegie Mellon Course Components  Lectures ▪ Higher level concepts  Recitations ▪ Material is part of lectures during summer  Written Assignments (6-10) ▪ 1 – 2 Paragraphs long  Labs (7) ▪ The heart of the course ▪ 1-2+ weeks each ▪ Provide in-depth understanding of an aspect of systems ▪ Programming and measurement  Exams (midterm + final) ▪ Test your understanding of concepts & mathematical principles Bryant and O’Hallaron, Computer Systems: A Programmer’s Perspective, Third Edition 40 CaCranrnegeigeieMeellolonn Getting Help  Class Web page: http://www.cs.cmu.edu/~213 ▪ Complete schedule of lectures, exams, and assignments ▪ Copies of lectures, assignments, exams, solutions ▪ FAQ  Piazza ▪ Best place for questions about assignments ▪ By default, your posts will be private ▪ We will fill the FAQ and Piazza with answers to common questions  Canvas ▪ Daily formative quizzes ▪ Can provide access to Piazza and occasional material Bryant and O’Hallaron, Computer Systems: A Programmer’s Perspective, Third Edition 41 CaCranrnegeigeieMeellolonn Getting Help  Office hours (starting later this week): ▪ TAs: TBD ▪ Faculty: Brian Railing (Zoom): TBD  Email staff only for: ▪ Special issues, such as extensions, incompletes, etc.  1:1 Appointments ▪ You can schedule 1:1 appointments with the instructor Bryant and O’Hallaron, Computer Systems: A Programmer’s Perspective, Third Edition 42 CaCranrnegeigeieMeellolonn 213 Student HowTo  Attend Lectures  Attend boot camps  Start labs early (really) and use GIT properly  TA office hours: we need to manage load and waiting time ▪ lab-related concrete questions ▪ must write them down before getting help ▪ Time slots  Faculty Office Hours ▪ Grading, special cases, issues, lab-related questions ▪ Conceptual and longer questions ▪ Open discussions Bryant and O’Hallaron, Computer Systems: A Programmer’s Perspective, Third Edition 43 Carnegie Mellon Policies: Labs And Exams  Work groups ▪ You must work alone on all lab assignments  Handins ▪ Labs due at 11:59pm ▪ Electronic handins using Autolab (no exceptions!)  Exams ▪ Exams will be online in network-isolated clusters ▪ Held over multiple days. Self-scheduled; just sign up!  Appealing grades ▪ Via detailed private post to Piazza within 7 days of completion of grading ▪ Follow formal procedure described in syllabus Bryant and O’Hallaron, Computer Systems: A Programmer’s Perspective, Third Edition 44 Carnegie Mellon Facilities  Labs will use the Intel Computer Systems Cluster ▪ The “shark machines” ▪ linux> ssh shark.ics.cs.cmu.edu
▪ 21 servers donated by Intel for 213/513
▪ 10 student machines (for student logins) ▪ 1 head node (for instructor logins)
▪ 10 grading machines (for autograding)
▪ Each server: Intel Core i7: 8 Nehalem cores, 32 GB DRAM, RHEL 6.1 ▪ Rack-mounted in Gates machine room
▪ Login using your Andrew ID and password
 Getting help with the cluster machines: ▪ Please direct questions to piazza
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Timeliness
 Grace days
▪ 5 grace days for the semester
▪ Limit of 0, 1, or 2 grace days per lab used automatically
▪ Covers scheduling crunch, out-of-town trips, illnesses, minor setbacks
 Lateness penalties
▪ Once grace day(s) used up, get penalized 15% per day ▪ No handins later than 3 days after due date
 Catastrophic events
▪ Major illness, death in family, …
Really, Really Hard! ▪ Once you start running late, it’s really hard to catch up
▪ Try to save your grace days until the last few labs Bryant and O’Hallaron, Computer Systems: A Programmer’s Perspective, Third Edition
▪ Formulate a plan (with your academic advisor) to get back on track
 Advice
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Policies: Grading
 Exams (30%): midterm (12%), final (18%)
 Labs (50%): weighted according to effort
 Active Work (20%):
▪ Short assignments (peer assessed)
▪ Lecture-time participation (such as via Canvas)
 Final grades based on a straight scale (90/80/70/60) with a small amount of curving
▪ Only upward
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Programs and Data
 Topics
▪ Bit operations, arithmetic, assembly language programs
▪ Representation of C control and data structures ▪ Includes aspects of architecture and compilers
 Assignments
▪ L1 (datalab): Manipulating bits
▪ L2 (bomblab): Defusing a binary bomb
▪ L3 (attacklab): The basics of code injection attacks
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The Memory Hierarchy
 Topics
▪ Memory technology, memory hierarchy, caches, disks, locality ▪ Includes aspects of architecture and OS
 Assignments
▪ L4 (cachelab): Building a cache simulator and optimizing for locality.
▪ Learn how to exploit locality in your programs.
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Virtual Memory
 Topics
▪ Virtual memory, address translation, dynamic storage allocation ▪ Includes aspects of architecture and OS
 Assignments
▪ L5 (malloclab): Writing your own malloc package
▪ Get a real feel for systems-level programming
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Exceptional Control Flow  Topics
▪ Hardware exceptions, processes, process control, Unix signals, nonlocal jumps
▪ Includes aspects of compilers, OS, and architecture
 Assignments
▪ L6 (tshlab): Writing your own Unix shell.
▪ A first introduction to concurrency
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Networking, and Concurrency
 Topics
▪ High level and low-level I/O, network programming
▪ Internet services, Web servers
▪ concurrency, concurrent server design, threads
▪ I/O multiplexing with select
▪ Includes aspects of networking, OS, and architecture
 Assignments
▪ L7 (proxylab): Writing your own Web proxy
▪ Learn network programming and more about concurrency and synchronization.
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Parallelism
 Topics:
▪ How to use and manage threads to improve performance ▪ Different basic synchronization approaches
 Assignments:
▪ L8 Parallel Lab (under development)
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Lab Rationale
 Each lab has a well-defined goal such as solving a puzzle or winning a contest
 Doing the lab should result in new skills and concepts
 We try to use competition in a fun and healthy way
▪ Set a reasonable threshold for full credit
▪ Post intermediate results (anonymized) on Autolab scoreboard for glory!
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Doing the Lab
 https://autolab.andrew.cmu.edu/courses/15213-m20 ▪ Download the lab materials
▪ (Usually as GitHub classroom link to generate your repo)
 If you have questions ▪ Piazza
▪ Office hours
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Autolab (https://autolab.andrew.cmu.edu)
 Labs are provided by the CMU Autolab system ▪ Project page: http://autolab.andrew.cmu.edu
▪ Developed by CMU faculty and students
▪ Key ideas: Autograding and Scoreboards
▪ Autograding: Providing you with instant feedback.
▪ Scoreboards: Real-time, rank-ordered, and anonymous summary.
▪ Used by over 3,000 students each semester
 With Autolab you can use your Web browser to:
▪ Download the lab materials
▪ Handin your code for autograding by the Autolab server
▪ View the class scoreboard
▪ View the complete history of your code handins, autograded results, instructor’s evaluations, and gradebook.
▪ View the TA annotations of your code for Style points. Bryant and O’Hallaron, Computer Systems: A Programmer’s Perspective, Third Edition
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Autolab accounts
 Students enrolled 11:45am on Tues, May 19 have Autolab accounts
 You must be enrolled to get an account
▪ Autolab is not tied in to the Hub’s rosters
▪ We will update the autolab accounts approximately once a day, so check back in 24 hours.
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Linux/Git bootcamp
 How to tar and untar files
 How to set permissions on local and afs directories
 How to recover old files from git
 How to ssh to the lab machines
 How to use a make file
 And all the other things you were always afraid to ask …
 Watch the schedule page for date and time.
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Version Control: Your Good Friend
 We are using git classroom
 Must be used by all students for lab 1, 4-7
 Students must commit early and often
at least at the end of each day working on a lab
 If a student is accused of cheating (plagiarism), we will consult the GIT server and look for a reasonable commit history
 Missing GIT history will count against you
 We may ask you to include a GIT hash on your submissions
 Learn how to use GIT now
 Each assignment will have a git classroom link Bryant and O’Hallaron, Computer Systems: A Programmer’s Perspective, Third Edition
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Carnegie Mellon
Git basics – create a project for your lab
 Follow link from writeup in TPZ  Use link to create a repo
 Clone to your local machine
 Commit often!
Bryant and O’Hallaron, Computer Systems: A Programmer’s Perspective, Third Edition
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Carnegie Mellon
Git basics – clone it to a working directory  Clone into a directory with the proper permissions
git clone git@github.com:cmu15213s19/213s19-lab0- seth4618.git
cd 213s19-lab0-seth4618
Bryant and O’Hallaron, Computer Systems: A Programmer’s Perspective, Third Edition
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Carnegie Mellon
Waitlist questions
 15-213: Amy Weis alweis@andrew.cmu.edu
 18-213: ECE Academic services (ece-asc@andrew.cmu.edu)  15-513: Amy Weis alweis@andrew.cmu.edu
 Please don’t contact the instructors with waitlist questions.
Bryant and O’Hallaron, Computer Systems: A Programmer’s Perspective, Third Edition
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Carnegie Mellon
Bryant and O’Hallaron, Computer Systems: A Programmer’s Perspective, Third Edition
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Welcome and Enjoy!

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