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C语言代写 | Project 3 – Multithreaded MapReduce

C语言代写 | Project 3 – Multithreaded MapReduce

这个Project是使用C语言完成多线程MapReduce

Project 3 – Multithreaded MapReduce
CSci 4061: Introduction to Operating Systems
Due Date: Wednesday, 13th Nov, 2019
1. Instructions
*(this section is modified from Programming Assignment 2 document)
– You could complete this project in a group of up to two students.
– Each group should turn in one copy with the names of all group members on it.
– The code must be originally written by your group. No code from outside the
course texts and slides may be used—your code cannot be copied or derived
from the Web, from past offerings, other students, programmer friends, etc.
– All submissions must compile and run on any CSE Labs machine located in KH
4-250.
– A zip file should be submitted through Canvas by 11:59pm on Friday, Nov 15th.
– Note: Do not publicize this assignment or your answer to the Internet, e.g., public
GitHub repo. To share code between team members, create a private repo in
github.umn.edu.
2. Background and Objective
In multi-threads programming, threads can perform the same or different roles. In
some multithreading scenarios like producer-consumer, producer and consumer threads
have different functionality. In other multithreading scenarios like many parallel
algorithms, threads have almost the same functionality. In this programming
assignment, we want to work on both sides in problem “word count statistics”, which is
the multithreaded version of MapReduce application in programming assignment 2.
An example problem is shown in Fig. 1 below. A text file contains several lines of
English characters, and this application will count the histogram of the starting letter for
each word.
Fig. 1, an example of “word count statistics”
One idea to solve this problem is to cut the file into smaller pieces and hand it
over to many workers. Then in this programming assignment, we will use multithreading
to create a producer to read the file and multiple consumers to process the smaller
piece data. We will have two forms of synchronization: a shared queue synchronized
between producer and consumers, and a global result histogram synchronized by
consumers.
Topics covered: POSIX-threading, synchronization, producer-consumer model, file IO
and string processing.
3. Project Overview
In multithreading “word count statistics” application, the program contains: a
master thread, one producer thread and many consumer threads (shown in Fig. 2
below). The producer and consumers will share a queue for data transfering. For
program execution flow, the entire program contains 4 parts: 1) the master thread
initializes the shared queue, result histogram, producer thread and consumer threads;
2) the producer thread will read the input file, cut the data into smaller pieces and feed
into the shared queue; 3) the consumers will read from the shared queue, compute the
“word count statistics” for its data pieces and synchronize the result with a global
histogram; 4) after producer and all consumers complete their work, the master thread
writes the final result into the output file.
Fig. 2, 4 parts of multithreading “word count statistics”
In the next section, we will go through the implementation details of this project.
The implementation requirements will be provided.
4. Project Implementation and Specifics
Before we go into each part, the application comparison between PA2
(Programming Assignment 2) and PA3 (Programming Assignment 3) needs to be
clarified. Even though we use the same application “word count statistics” in PA2, there
are several key differences in the input file and processing flows: 1) PA2 works on
different files located in different directories, but PA3 takes only one file as your input
file. 2) In PA2, each file contains multiple lines and each line contains only one word.
But PA3’s input file is more “natural” , which contains multiple words in each line and
the file contains multiple lines. 3) PA2 uses multi-processing, which means the parent
forks several children and seperate the workload. But in PA3, you only have one
process but multiple threads . PA3 is mainly focused on the usage of threads and
thread-safe data structures.
4.0 Shared Queue
The core of this multithreading “word count statistic” application is a thread-safe
shared queue. This shared queue should be implemented as a linked-list unbounded
buffer . The producer inserts the data in the tail of the linked-list and the consumer
extracts the data from the head. Also, it should be implemented in a non-busy-waiting
way (use “mutex lock + conditional variable” or “semaphore”).
4.1 Master Initialization
In this stage, the program needs to check the input arguments and print error
messages if argument mismatch (see Section 6 execution for arguments). Then it
should perform the initialization on data structure and launch the producer/consumers
thread. Then the master will wait for all threads to join back (4.4 Master Finalize).
4.2 Producer functionality
The main functionality of the producer thread is to read the input file and pass the
data into the shared queue (by a package). The file is required to be read by line
granularity (one line at a time), thus each consumer will work on one line at a time.
Since there are multiple consumers, if the EOF is reached, the producer should send
notifications to consumers specifying there will be no more data. The producer
terminates after sending those notifications. Note that the package is the information
transferred between producer/consumers via shared queue. It should contain the data
for consumers and other information if needed.
4.3 Consumer functionality
The consumer will repeatedly check the queue for a new package, work on it for
word count statistics , and then go back to get the next package. This will continue
until receiving the EOF notification, then it will terminate. Besides package handling, it’s
the consumer’s responsibility to synchronize the result with the global final histogram.
Then after all consumers finish their work, the master thread could summarize it and
generate the output.
The word count statistics will check the beginning character of a word. The
definition of a word is a continuous a-zA-Z character (shown in Fig. 3 below). Note that,
all other characters are treated as space. The characters like “-”, “_” are not connecting
words. Same as PA2, we don’t differentiate between uppercase and lowercase letters.
Fig. 3, the word count statistics function
4.4 Master Finalize
After the producer and consumers have joined back, the master thread will write
the final result (global histogram) into the output file named “result.txt” . The output
format should be: “%c: %d\n” , and it will loop through the global histogram from ‘a’ to
‘z’ (shown in Fig. 4 below).
Fig. 4, the output file format
4.5 Log Printout
The program will also print a log file if the argument option is specified . The
log file should be named as “log.txt” . The producer and consumers should print their
execution information into the log:
Producer:
1. Print “producer\n” when the producer is launched
2. Print “producer: %d\n” for the current line number (starts from 0)
Consumer:
1. Print “consumer %d\n” when launched, with the consumer id (0 to
number of consumers minus 1)
2. Print “consumer %d: %d\n” for the consumer id and the line number
it currently works on.
There are some other notes when writing the log:
– The print library functions are usually thread-safe, so you don’t need to
use a lock or worry about messy printing (unless you meet that).
– Since the execution order of threads is nondeterministic, you usually will
not get a stable log print out.
– EOF notification should be printed out as line number “-1”.
5. Extra Credits
The extra credits will be granted if a bounded buffer shared queue is
implemented. The application could choose the unbounded/bounded buffer by the
commandline argument option. Also the bounded buffer should have a configurable
buffer size specified by commandline argument option.
6. Execution Syntax, Options
The “word count statistic” will take the arguments syntax:
$ ./wcs #consumer filename [option] [#queue_size]
– The second argument “#consumer” is the number of consumers the
program will create.
– The third argument “filename” is the input file name
– Options have only three possibilities: “-p”, “-b”, “-bp”.
1) “-p” means printing, the program will generate log in this case.
2) “-b” means bounded buffer (extra credit), the program will use it
instead of unbounded buffer.
3) “-bp” means both bounded buffer and log printing.
– The last option argument is the queue size if using bounded buffer (extra
credits).
7. Testing
There will be 4 testing files in the “testing” folder, each of them has different
scenarios specified in the first line of testfile. Also, the solution for 4 test files is
contained in the “testing/sol” folder.
8. Assumptions and Hints
– One line of input file has at most 1024 chars.
– You are allowed to use library file IO functions (instead of open/read/write
syscall).
– Use the keyword “extern” in the header file for the global variable consistency.
– Check if the library function is thread-safe before using them (if you use them in
concurrent threads scenario).
9. Submission
*(this section is modified from Programming Assignment 2 document)
One student from each group should upload to Canvas, a zip file containing their
C source files, a makefile, and a README that includes the following details:
– Team names and x500
– How to compile the program
– Your and your partner’s individual contributions
– Any assumptions outside this document
– What exactly your program does
– If you have attempted extra credit
The README file does not have to be long, but must properly describe the
above points. Your source code should provide appropriate comments for functions
and critical code sections (like the purpose of these code and logical execution flow). At
the top of your README file and each C source file please include the following
comment:
/*test machine: CSELAB_machine_name * date: mm/dd/yy
* name: full_name1 , [full_name2]
* x500: id_for_first_name , [id_for_second_name] */
10. Grading Policy
1. (5%) correct README content
2. (5%) appropriate code style and comments
3. (40%) passed all tests (there may be some other tests when grading)
4. (15%) correct shared queue data structure and functions (like insert, extract,
non-busy-waiting, and etc. )
5. (5%) correct thread create and join usage
6. (10%) correct global histogram usage (like synchronization)
7. (10%) correct producer functionality
8. (10%) correct consumer functionality
9. (10%) extra credit: bounded buffer

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