In my recent Applied High Performance Computing (HPC) lectures, I have been learning to better utilize OpenMP constructs and functions . If these terms mean little to nothing to you, welcome to the boat – it’ll be a collaborative learning experience. For full disclosure, this blog post assumes some basic knowledge of parallel programming and its related terminology. But just in case, here is a quick glossary:
- Node: One computing unit within a supercomputing cluster. Can be thought of as on CPU unit on a traditional desktop/laptop.
- Core: The physical processing unit within the CPU that enables computations to be executed. For example, an 11th-Gen Intel i5 CPU has four cores.
- Processors/threads: Simple, lightweight “tasks” that can be executed within a core. Using the same example as above, this Intel i5 CPU has eight threads, indicating that each of its cores can run two threads simultaneously.
- Shared memory architecture: This means that all cores share the same memory within a CPU, and are controlled by the same operating system. This architecture is commonly found in your run-of-the-mill laptops and desktops.
- Distributed memory architecture: Multiple cores run their processes independently of each other. They communicate and exchange information via a network. This architecture is more commonly found in supercomputer clusters.
- Serial execution: Tasks and computations are completed one at a time. Typically the safest and most error-resistant form of executing tasks, but is the least efficient.
- Parallel execution: Tasks and computations are completed simultaneously. Faster and more efficient than serial execution, but runs the risk of errors where tasks are completed out of order.
- Synchronization: The coordination of multiple tasks or computations to ensure that all processes aligns with each other and abide by a certain order of execution. Synchronization takes up more time than computation. AN overly-cautious code execution with too many synchronization points can result in unnecessary slowdown. However, avoiding it altogether can result in erroneous output due to out-of-order computations.
- Race condition: A race condition occurs when two threads attempt to access the same location on the shared memory at the same time. This is a common problem when attempting parallelized tasks on shared memory architectures.
- Load balance: The distribution of work (tasks, computations, etc) across multiple threads. OpenMP implicitly assumes equal load balance across all its threads within a parallel region – an assumption that may not always hold true.
This is intended to be a two-part post where I will first elaborate on the OpenMP API and how to use basic OpenMP clauses to facilitate parallelization and code speedup. In the second post, I will provide a simple example where I will use OpenMP to delineate my code into serial and parallel regions, force serialization, and synchronize memory accesses and write to prevent errors resulting from writing to the same memory location simultaneously.
What is OpenMP?
OpenMP stands for “Open Specification for Multi-Processing”. It is an application programming interface (API) that enables C/C++ or Fortran code to “talk” (or interface) with multiple threads within and across multiple computing nodes on shared memory architectures, therefore allowing parallelism. It should not be confused with MPI (Message Passing Interface), which is used to develop parallel programs on distributed memory systems (for a detailed comparison, please refer to this set of Princeton University slides here). This means that unlike a standalone coding language like C/C++. Python, R, etc., it depends only on a fixed set of simple implementations using lightweight syntax (#pragma omp [clause 1] [clause 2]) . To begin using OpenMP, you should first understand what it can and cannot do.
Things you can do with OpenMP
Explicit delineation of serial and parallel regions
One of the first tasks that OpenMP excels at is the explicit delineation of your code into serial and parallel regions. For example:
#include <stdio.h>
#include <omp.h>
void some_function() {
#pragma omp parallel {
do_a_parallel_task();
}
do_a_serial_task();
}
In the code block above, we are delineating an entire parallel region where the task performed by the do_a_parallel_task() function is performed independently by each thread on a single node. The number of tasks that can be completed at any one time depends on the number of threads that are available on a single node.
Hide stack management
In addition, using OpenMP allows you to automate memory allocation, or “hide stack management”. Most machines are able to read, write, and (de)allocate data to optimize performance, but often some help from the user is required to avoid errors caused by multiple memory access or writes . OpenMP allows the user to automate stack management without explicitly specifying memory reads or writes in certain cache locations via straightforward clauses such as collapse() (we will delve into the details of such clauses in later sections of this blog post).
Allow synchronization
OpenMP also provides synchronization constructs to enforce serial operations or wait times within parallel regions to avoid conflicting or simultaneous read or write operations where doing so might result in erroneous memory access or segmentation faults. For example, a sequential task (such as a memory write to update the value in a specific location) that is not carefully performed in parallel could result in output errors or unexpected memory accessing behavior. Synchronization ensures that multiple threads have to wait until the last operation on the last thread is completed, or only one thread is allowed to perform an operation at a time. It also protects access to shared data to prevent multiple reads (false sharing). All this can be achieved via the critical, barrier, or single OpenMP clauses.
Creating a specific number of threads
Unless explicitly stated, the system will automatically select the number of threads (or processors) to use for a specific computing task. Note that while you can request for a certain number of threads in your environment using export OMP_NUM_THREADS, the number of threads assigned to a specific task is set by the system unless otherwise specified. OpenMP allows you to control the number of threads you want to use in a specific task in one of two ways: (a) using omp_set_num_threads() and using (b) #pragma omp parallel [insert num threads here]. The former allows you to enforce an upper limit of the number of threads to be freely assigned to tasks throughout the code, while the latter allows you to specify a set number of threads to perform an operation(s) within a parallel region.
In the next section, we will list some commonly-used and helpful OpenMP constructs and provide examples of their use cases.
OpenMP constructs and their use cases
OMP_NUM_THREADS
This is an environment variable that can be set either in the section of your code where you defined your global variables, or in the SLURM submission script you are using to execute your parallel code.
omp_get_num_threads()
This function returns the number of threads currently executing the parallel region from which it is called. Use this to gauge how many active threads are being used within a parallel region
omp_set_num_threads()
This function specifies the number of threads to use for any following parallel regions. It overrides the OMP_NUM_THREADS environment variable. Be careful to not exceed the maximum number of threads available across all your available cores. You will encounter an error otherwise.
omp_get_max_threads()
To avoid potential errors from allocating too many threads, you can use omp_set_num_threads() with omp_get_max_threads(). This returns the maximum number of threads that can be used to form a new “team” of threads in a parallel region.
#pragma omp parallel
This directive instructs your compiler to create a parallel region within which it instantiates a set (team) of threads to complete the computations within it. Without any further specifications, the parallelization of the code within this block will be automatically determined by the compiler.
#pragma omp for
Use this directive to instruct the compiler to parallelize for-loop iterations within the team of threads that it has instantiated. This directive is often used in combination with #pragma omp parallel to form the #pragma omp parallel for construct which both creates a parallel region and distributes tasks across all threads.
To demonstrate:
#include <stdio.h>
#include <omp.h>
void some_function() {
#pragma omp parallel {
#pragma omp for
for (int i = 0; i < some_num; i++) {
perform_task_here();
}
}
}
is equivalent to
#include <stdio.h>
#include <omp.h>
void some_function() {
#pragma omp parallel for {
for (int i = 0; i < some_num; i++) {
perform_task_here();
}
}
#pragma omp barrier
This directive marks the start of a synchronization point. A synchronization point can be thought of as a “gate” at which all threads must wait until all remaining threads have completed their individual computations. The threads in the parallel region beyond omp barrier will not be executed until all threads before is have completed all explicit tasks.
#pragma omp single
This is the first of three synchronization directives we will explore in this blog post. This directive identifies a section of code, typically contained within “{ }”, that can only be run with one thread. This directive enforces serial execution, which can improve accuracy but decrease speed. The omp single directive imposes an implicit barrier where all threads after #pragma omp single will not be executed until the single thread running its tasks is done. This barrier can be removed by forming the #pragma omp single nowait construct.
#pragma omp master
This directive is similar to that of the #pragma omp single directive, but chooses only the master thread to run the task (the thread responsible creating, managing and discarding all other threads). Unlike #pragma omp single, it does not have an implicit barrier, resulting in faster implementations. Both #pragma omp single and #pragma omp master will result in running only a section of code once, and it useful for controlling sections in code that require printing statements or indicating signals.
#pragma omp critical
A section of code immediate following this directive can only be executed by one thread at a time. It should not be confused with #pragma omp single. The former requires that the code be executed by one thread at a time, and will be executed as many times as has been set by omp_set_num_threads(). The latter will ensure that its corresponding section of code be executed only once. Use this directive to prevent race conditions and enforce serial execution where one-at-a-time computations are required.
Things you cannot (and shouldn’t) do with OpenMP
As all good things come to an end, all HPC APIs must also have limitations. In this case, here is a quick rundown in the case of OpenMP:
- OpenMP is limited to function on shared memory machines and should not be run on distributed memory architectures.
- It also implicitly assumes equal load balance. Incorrectly making this assumption can lead to synchronization delays due to some threads taking significantly longer to complete their tasks compared to others immediately prior to a synchronization directive.
- OpenMP may have lower parallel efficiency due to it functioning on shared memory architectures, eventually being limited by Amdahl’s law.
- It relies heavily on parallelizable for-loops instead of atomic operations.
Summary
In this post, we introduced OpenMP and a few of its key constructs. We also walked through brief examples of their use cases, as well as limitations of the use of OpenMP. In our next blog post, we will provide a tutorial on how to write, compile, and run a parallel for-loop on Cornell’s Cube Cluster. Until then, feel free to take a tour through the Cornell Virtual Workshop course on OpenMP to get a head start!
References
IBM documentation. (n.d.). https://www.ibm.com/docs/en/xl-c-aix/13.1.3
Jones, M. D. (2009). Practical issues in Openmp. University at Buffalo (UB) Department of Computer Science and Engineering . https://cse.buffalo.edu/~vipin/nsf/docs/Tutorials/OpenMP/omp-II-handout.pdf
Pros and cons of OpenMP. (n.d.). https://www.dartmouth.edu/~rc/classes/intro_openmp/Pros_and_Cons.html
What is load balancing? – load balancing algorithm explained – AWS. (n.d.). https://aws.amazon.com/what-is/load-balancing/
Ziskovin, G. (2022, October 29). #pragma OMP single [explained with example]. OpenGenus IQ: Computing Expertise & Legacy. https://iq.opengenus.org/pragma-omp-single/
Water Programming: A Collaborative Research Blog 