# Lab 12

Deadline: End of lab Friday, December 6th

## Objectives:

• TSW learn about how to use various OpenMP directives
• TSW write code to learn two ways of how #pragma omp for could be implemented, and will learn what false sharing is.
• TSW learn about reduction and what it does

## Setup

Pull the Lab 12 files from the lab starter repository with

git pull starter master


## Exercise 1 - OpenMP Hello World

For this lab, we will use C to leverage our prior programming experience with it. OpenMP is a framework with a C interface, and it is not a built-in part of the language. Most OpenMP features are actually directives to the compiler. Consider the following implementation of Hello World (hello.c):

int main() {
#pragma omp parallel
{
}
}


This program will fork off the default number of threads and each thread will print out “hello world” in addition to which thread number it is. You can change the number of OpenMP threads by setting the environment variable OMP_NUM_THREADS or by using the omp_set_num_threads function in your program. The #pragma tells the compiler that the rest of the line is a directive, and in this case it is omp parallel. omp declares that it is for OpenMP and parallel says the following code block (what is contained in { }) can be executed in parallel. Give it a try:

make hello
./hello


If you run ./hello a couple of times, you should see that the numbers are not always in numerical order and will most likely vary across runs. This is because within the parallel region, OpenMP does the code in parallel and as a result does not enforce an ordering across all the threads. It is also vital to note that the variable thread_ID is local to a specific thread and not shared across all threads. In general with OpenMP, variables declared inside the parallel block will be private to each thread, but variables declared outside will be global and accessible by all the threads.

## Exercise 2 - Vector Addition

Vector addition is a naturally parallel computation, so it makes for a good first exercise. The v_add function inside v_add.c will return the array that is the cell-by-cell addition of its inputs x and y. A first attempt at this might look like:

void v_add(double* x, double* y, double* z) {
#pragma omp parallel
{
for(int i=0; i<ARRAY_SIZE; i++)
z[i] = x[i] + y[i];
}
}


You can run this (make v_add followed by ./v_add) and the testing framework will automatically time it and vary the number of threads. You will see that this actually seems to do worse as we increase the number of threads. The issue is that each thread is executing all of the code within the omp parallel block, meaning if we have 8 threads, we will actually be adding the vectors 8 times. DON’T DO THIS! To get speedup when increasing the number of threads, we need each thread to do less work, not the same amount as before. Rather than have each thread run the entire for loop, we need to split up the for loop across all the threads so each thread does only a portion of the work:

Your task is to modify v_add.c so there is some speedup (speedup may plateau as the number of threads continues to increase). To aid you in this process, two useful OpenMP functions are:

• int omp_get_num_threads();
• int omp_get_thread_num();

The function omp_get_num_threads() will return how many threads there are in a omp parallel block, and omp_get_thread_num() will return the thread ID.

Divide up the work for each thread through two different methods (write different code for each of these methods):

1. First task, slicing: have each thread handle adjacent sums: i.e. Thread 0 will add the elements at indices i such that i % omp_get_num_threads() is 0, Thread 1 will add the elements where i % omp_get_num_threads() is 1, etc.
2. Second task, chunking: if there are N threads, break the vectors into N contiguous chunks, and have each thread only add that chunk (like the figure above).

Hints:

• Use the two functions we listed above somehow in the for loop to choose which elements each thread handles.
• You may need a special case to prevent going out of bounds for v_add_optimized_chunks. Don’t be afraid to write one.
• As you’re working on this exercise, you should be thinking about false sharing–read more here and here.

Fact: For this exercise, we are asking you to manually split the work amongst threads. Since this is such a common pattern for software, the designers of OpenMP actually made the #pragma omp for directive to automatically split up independent work. Here is the function rewritten using it. You may NOT use this directive in your solution to this exercise.

void v_add(double* x, double* y, double* z) {
#pragma omp parallel
{
#pragma omp for
for(int i=0; i<ARRAY_SIZE; i++)
z[i] = x[i] + y[i];
}
}


make v_add


### Checkpoint

• Show the TA or AI checking you off your code for both optimized versions of v_add that manually splits up the work. Remember, you should not have used #pragma omp for here.
• Run your code to show that it gets parallel speedup.
• Which version of your code runs faster, chunks or adjacent? What do you think the reason for this is? Explain to the person checking you off.

## Exercise 3 - Dot Product

The next interesting computation we want to compute is the dot product of two vectors. At first glance, implementing this might seem not too different from v_add, but the challenge is how to sum up all of the products into the same variable (reduction). A sloppy handling of reduction may lead to data races: all the threads are trying to read and write to the same address simultaneously. One solution is to use a critical section. The code in a critical section can only be executed by a single thread at any given time. Thus, having a critical section naturally prevents multiple threads from reading and writing to the same data, a problem that would otherwise lead to data races. A naive implementation would protect the sum with a critical section, like (dotp.c):

double dotp(double* x, double* y) {
double global_sum = 0.0;
#pragma omp parallel
{
#pragma omp for
for(int i=0; i<ARRAY_SIZE; i++)
#pragma omp critical
global_sum += x[i] * y[i];
}
return global_sum;
}


Try out the code (make dotp and ./dotp). Notice how the performance gets much worse as the number of threads goes up? By putting all of the work of reduction in a critical section, we have flattened the parallelism and made it so only one thread can do useful work at a time (not exactly the idea behind thread-level parallelism). This contention is problematic; each thread is constantly fighting for the critical section and only one is making any progress at any given time. As the number of threads goes up, so does the contention, and the performance pays the price. Can you fix this performance bottleneck?

Hint: REDUCE the number of times that each thread needs to use a critical section!

1. First task: try fixing this code without using the OpenMP Reduction keyword. Hint: Try reducing the number of times a thread must add to the shared global_sum variable.
2. Second task: fix the problem using OpenMP’s built-in Reduction keyword (Google for more information on it). Is your performance any better than in the case of the manual fix? Why or why not?

Note: The exact syntax for using these directives can be kind of confusing. Here are the key things to remember:

• A #pragma omp parallel section should be specified with curly braces around the block of code to be parallelized. The opening curly brace cannot not be on the same line as this directive.
• A #pragma omp for section should not be accompanied with extra curly braces. Just stick that directive directly above a for loop.

Hint: You’ll need to type the ‘+’ operator somewhere when using reduction.

Hint: If you used the reduction keyword correctly, your code should no longer contain #pragma omp critical.

Finally, run your code to examine the performance:

make dotp
./dotp


### Checkpoint

• Show the TA or AI checking you off your manual fix to dotp.c that gets speedup over the single threaded case.
• Show the TA or AI checking you off your Reduction keyword fix for dotp.c, and explain the difference in performance, if there is any.
• Run your code to show the speedup.

## Checkoff

Exercise 1:

• Nothing to show

Exercise 2:

• Show the TA or AI checking you off your code for both optimized versions of v_add that manually splits up the work. Remember, you should not have used #pragma omp for here.
• Run your code to show that it gets parallel speedup.
• Which version of your code runs faster, chunks or adjacent? What do you think the reason for this is? Explain to the person checking you off.

Exercise 3:

• Show the TA or AI checking you off your manual fix to dotp.c that gets speedup over the single threaded case.
• Show the TA or AI checking you off your Reduction keyword fix for dotp.c, and explain the difference in performance, if there is any.
• Run your code to show the speedup.