Synchronization in OpenMP

In parallel programming, multiple threads or processes work concurrently, and often on shared data. While this approach can dramatically speed up computations, it also introduces potential issues, especially when multiple threads attempt to access and modify shared resources simultaneously. Synchronization is essential to ensure that shared data remains consistent and that operations on shared resources do not interfere with each other.

OpenMP, a widely used tool for parallel programming in Fortran, provides various synchronization mechanisms to handle these challenges. These mechanisms include critical sections, barriers, and other constructs that ensure correct data access and manipulation by parallel threads. Let’s dive deeper into synchronization in OpenMP and explore how to use these features effectively.

What is Synchronization in Parallel Programming?

Synchronization in parallel programming is the coordination of threads to ensure that the execution of a program remains correct when multiple threads access shared resources. Without proper synchronization, multiple threads might attempt to read or write to shared memory locations simultaneously, leading to race conditions, incorrect results, and unpredictable behavior.

When using parallel computing, several types of synchronization may be necessary, including:

Data synchronization: Ensuring that multiple threads access and modify shared data in a way that prevents conflicts.
Execution synchronization: Ensuring that threads execute certain operations in a specific order or wait for each other at certain points in the program.

In OpenMP, there are a few primary mechanisms for handling synchronization, including critical sections and barriers. Let’s explore these in more detail.

1. Critical Sections in OpenMP

A critical section is a block of code that must be executed by only one thread at a time. When a thread reaches a critical section, it must wait for other threads to finish their work in that section before it can proceed. This ensures that shared resources are not modified simultaneously by multiple threads, which could lead to inconsistencies in the data.

In OpenMP, the !$omp critical directive is used to define a critical section. Code inside the critical section will be executed exclusively by one thread at a time, preventing data races and ensuring that shared resources are updated in a controlled manner.

Example: Using Critical Sections in OpenMP

Let’s look at an example where multiple threads perform computations on an array and print the result once the computation is complete. Since the print statement is intended to output to the screen, it needs to be executed by only one thread at a time to avoid interleaved outputs from multiple threads.

!$omp parallel
!$omp do
do i = 1, 1000
    a(i) = b(i) + c(i)
end do
!$omp end do
!$omp critical
print *, "Computation complete!"
!$omp end critical!$omp end parallel

In this example, the loop do i = 1, 1000 performs computations on the arrays a, b, and c in parallel, with each thread handling a subset of the loop iterations. After the computation, the print statement is enclosed in a critical section. This ensures that only one thread can print the message “Computation complete!” at a time, preventing race conditions that could result in garbled or mixed outputs.

When to Use Critical Sections

You should use critical sections when:

A block of code accesses shared resources or variables that could be modified simultaneously by multiple threads.
You need to ensure that only one thread at a time performs specific operations, such as printing or updating shared variables.

However, critical sections can introduce overhead because they force threads to wait for each other, potentially reducing the parallelism in the program. Therefore, critical sections should be used sparingly and only when necessary to ensure the correctness of the program.

2. Barriers in OpenMP

A barrier is a synchronization point where threads must wait for all other threads to reach that point before they can continue. Barriers are useful when certain sections of code depend on the completion of other sections, and you need to ensure that all threads have finished their work before proceeding.

In OpenMP, the !$omp barrier directive is used to create a barrier. Once a thread reaches the barrier, it waits until all other threads have reached it before continuing execution.

Example: Using Barriers in OpenMP

Here’s an example where we use a barrier to synchronize threads after a computation step:

!$omp parallel
!$omp do
do i = 1, 1000
    a(i) = b(i) + c(i)
end do
!$omp end do
!$omp barrier
! Now all threads must wait until all have completed the previous loop
!$omp do
do i = 1, 1000
    d(i) = a(i) * 2.0
end do
!$omp end do!$omp end parallel

In this example, the !$omp barrier ensures that all threads complete the first loop (computing values for a(i)) before any thread starts the second loop (computing values for d(i)). Without the barrier, some threads might start the second loop before others have finished the first, leading to incorrect results.

When to Use Barriers

Barriers are useful when:

You need to ensure that all threads finish a certain section of code before proceeding to the next stage.
Different sections of code depend on the output from the previous sections, and you want to synchronize the threads to maintain consistency.

However, barriers can reduce performance if they are overused, as threads might spend time waiting at the synchronization point instead of performing useful work. Therefore, it’s essential to use barriers judiciously.

3. Other Synchronization Mechanisms in OpenMP

In addition to critical sections and barriers, OpenMP provides other synchronization tools, such as atomic operations and flush. These tools can provide more fine-grained control over synchronization and can be more efficient in certain situations.

Atomic Operations

Atomic operations ensure that a specific operation on a variable is completed without interference from other threads. OpenMP provides the !$omp atomic directive to perform atomic operations. This is particularly useful when updating a shared variable incrementally, such as summing values across threads.

Example:

integer :: total
total = 0

!$omp parallel do
do i = 1, 1000
!$omp atomic
total = total + a(i)end do
!$omp end parallel do

In this example, the total variable is updated atomically to ensure that no two threads modify it at the same time, preventing data races.

Flush

The flush directive is used to ensure that changes to variables are visible to other threads at the appropriate time. It ensures memory consistency by forcing threads to write updates to memory and synchronize their views of memory.