讲解Multi-threaded Map编程、

Assignment 6: Multi-threaded Map Reduce in Rust
Assignment 6: Multi-threaded Map Reduce in Rust
Due Sunday by 11:59pm
Points 50
Submitting a file upload
Available after May 23 at 8am
Start Assignment
Introduction
In this assignment, you'll write a program that will get you familiar with writing multi-threaded programs in Rust.
Learning Outcomes
Write simple programs in Rust (Module 9, MLO 2)
Compile, debug, manage and run Rust programs (Module 9, MLO 3)
Explain the facility for threads provided by Rust (Module 10, MLO 2)
Map Reduce
According to the Wikipedia article on MapReduce
MapReduce is a programming model and an associated implementation for processing and generating big data sets with a
parallel, distributed algorithm on a cluster.
A MapReduce program is composed of a map procedure, which performs filtering and sorting (such as sorting students by first
name into queues, one queue for each name), and a reduce method, which performs a summary operation (such as counting the
number of students in each queue, yielding name frequencies).
Instructions
For this assignment, we are providing you with a single-threaded Rust program
processes input numbers to produce a sum. The program contains extensive comments that explain the functionality and give directions
on what parts of code you are allowed to change (look for comments starting with CHANGE CODE).
Assignment 6: Multi-threaded Map Reduce in Rust
Here is a description of the program: currently the main() function does the following
Generates data for the rest of the program
Calls generate_data() to generates a vector of numbers that serves as input for the rest of the program.
Partitions the data
Calls partition_data_in_two() which partitions the input numbers into two partitions
Performs the map step
Calls map_data() for each of the two partitions, which returns the sum of all the numbers in that partition.
Performs the reduce step
Gathers the intermediate results produced by each call to map_data()
Calls reduce_data() that sums up the intermediate results produced by the map step to produce the final sum of all the input
numbers.
You have to modify the program to accomplish the following tasks:
Modify the program to create 2 threads, each of which concurrently runs the map_data() function on one of the two partitions
created by the program given to you.
Add code for the function partition_data() to partition the data into equal-sized partitions based on the argument num_partitions
In case num_elements is not a multiple of num_partitions, some partitions can have one more element than other partitions
Add code to the function main() to
Partition the data into equal-size partitions
Create as many threads as the number of partitions and have each thread concurrently run the map_data() function to process
one partition each
Gather the intermediate results returned by each thread
Run the reduce step to process the intermediate results and produce the final result
See detailed comments in the provided program to see how you can go about making the required changes.
Example Usage
Here are some example executions of the program.
An execution of the program with 5 partitions and 150 elements.
Assignment 6: Multi-threaded Map Reduce in Rust
Since the number of elements is a multiple of the number of partitions, it is required that each partition should have the same
number of elements.
However, there is no requirement about which element is put into which partition. Thus the intermediate sums in your solution can be
different from what is shown below.
./main 5 150
Number of partitions = 2
size of partition 0 = 75
size of partition 1 = 75
Intermediate sums = [2775, 8400]
Sum = 11175
Number of partitions = 5
size of partition 0 = 30
size of partition 1 = 30
size of partition 2 = 30
size of partition 3 = 30
size of partition 4 = 30
Intermediate sums = [435, 1335, 2235, 3135, 4035]
Sum = 11175
An execution of the program with 6 partitions and 150 elements.
./main 6 150
Number of partitions = 2
size of partition 0 = 75
size of partition 1 = 75
Intermediate sums = [2775, 8400]
Sum = 11175
Number of partitions = 6
size of partition 0 = 25
size of partition 1 = 25
size of partition 2 = 25
size of partition 3 = 25
size of partition 4 = 25
size of partition 5 = 25
Intermediate sums = [300, 925, 1550, 2175, 2800, 3425]
Sum = 11175
An execution of the program with 5 partitions and 153 elements.
In this example the number of elements is not a multiple of the number of partitions.
Based on the requirement that some partitions can have one element more than other partitions, in this case 3 partitions must have
31 elements and 2 partitions must have 30 elements.
Assignment 6: Multi-threaded Map Reduce in Rust
In the example shown below, the 3 partitions with 31 elements are at position 0, 1 and 2 in the vector of partitions, and the 2
partitions with 30 elements are at position 3 and 4 in that vector. However, there is no requirement about the order in which partitions
that have one more element than other partitions appear in the vector of partitions. Thus, the order of the partitions in your solution
can be different from what is shown below.
./main 5 153
Number of partitions = 2
size of partition 0 = 76
size of partition 1 = 77
Intermediate sums = [2850, 8778]
Sum = 11628
Number of partitions = 5
size of partition 0 = 31
size of partition 1 = 31
size of partition 2 = 31
size of partition 3 = 30
size of partition 4 = 30
Intermediate sums = [585, 1486, 2387, 3135, 4035]
Sum = 11628
Hints
The function thread::spawn()
returns JoinHandle

where T is the type of the
return value of the function the thread runs. This means that
Because map_data() returns an integer of type usize
If you spawn a thread that runs map_data()
thread::spawn() will return a value of type JoinHandle
What to turn in?
Required: Upload one file main.rs with all of your code.
When you resubmit a file in Canvas, Canvas can attach a suffix to the file, e.g., the file name may become main-1.rs . Don't
worry about this name change as no points will be deducted because of this.
Optional: If you have any meta-comments about the program, create a file README.txt with these comments, and upload it with
your submission as a separate file (i.e., don't zip up the two files together).
Grading Criteria
Assignment 6: Multi-threaded Map Reduce in Rust

5/5
Total Points: 50
Assignment 6 Rust
Criteria
Ratings
This assignment is worth 8% of your final grade. The breakup of points is given in the grading rubric.
The grading will be done on os1.
To test your program, we will compile the code as follows
rustc main.rs
We will run the program as follows
./main num_partitions num_elements
E.g.,
./main 5 150
This criterion is linked to a Learning OutcomeProcesses the two partitions in the provided program by creating two threads
each of which concurrently runs map_data() on one partition each.
partition_data() partitions the data into num_partitions and the sizes of the partitions are correct.
Implements map-reduce using as many concurrent threads as the number of partitions in the argument.