Open MP

What is OpenMP?

It is an application programming interface (API) that supports multi-platform shared-memory multiprocessing programming in various popular languages such as C++, C, and Fortran. An application was built up with the hybrid model of very popular parallel programming that can run on a computer cluster by using both OpenMP and Message Passing Interface which is famously called MPI, such that OpenMP is used for parallelism within a node which is generally multi-core while MPI is used for parallelism between nodes.

Brief History

The following features are added or improved in these versions which are released one after one in subsequent years:

Accelerator support,atomic; error handling; thread affinity; task extension; custom reduction; SIMD support

CORE ELEMENTS OF OpenMP

#Thread Creation

The omp parallel pragma is used to fork additional threads to execute the work contained in the construct in parallel. The original thread is called the master thread with thread ID 0.

#Work Sharing Constructs

This is used to assign work that is surely independent of one or more threads.

# Variant Directives

This important feature was introduced in OpenMP 5.0 to facilitate portability for programmers. Enable OpenMP pragmas and user code customization at compile time. This specification defines OpenMP constructs, execution devices and capabilities provided by the implementation, context selectors based on characteristics and user-defined conditions, meta-directives, and declarative directives for users to program the same regions of code using variant directives. It defines characteristics such as a meta-directive is an executable directive that conditionally resolves to another directive at compile time by choosing between multiple directive variants based on characteristics that define an OpenMP condition or context.

The “declare a variant” statement has similar functionality to the meta-directive but selects the functional variant at the caller based on context or user-defined criteria.

# CLAUSES

There are various types of Clauses:

Let me explain you this one by one :

First is Data-Sharing Attribute Clauses

Let’s go to next that is Synchronization clauses

At the end I want to Discuss Scheduling Clauses

First of all what is schedule?

It is kinda useful in the work sharing construct in for-loop. The iterations in the work-sharing construct are assigned to the threads according to the scheduling policy as defined by this clause.SO let’s discuss it’s famous 3 clauses one by one:

# Environment Variables

It is used to alter the executable features of OpenMP applications. It is used to control the loop iterations scheduling by the default number of threads.

PROS OF OpenMP

CONS OF OpenMP

PERFORMANCE CONDITIONS

When running an OpenMP-parallelized program on a N processor platform, one might expect an N-fold speedup. However, thisis rare for the following reasons:

If dependencies exist, the process must wait until dependent datais computed.
When multiple processes share a non-parallel proof resource (such as a file for writing), their requests are executed sequentially. Therefore each thread must wait until the other thread releases the resource.

Most programs should not be parallelized with OpenMP, which limits the theoretical upper bound on acceleration according to Amdahl’s law.

The symmetric multiprocessing (SMP) N processor can have N times the processing power, but memory bandwidth typically does not scale N times.in many cases, the original memory pathis shared between multiple processors, and performance degradationis seen when competing for shared memory bandwidth.

Many other commonissues affecting the ultimate speedup of parallel computing also apply to OpenMP. B. Load balancing and synchronization overhead.

Compiler optimizations may not be as effective when OpenMPisinvoked. This can often cause his OpenMP program, whichis single-threaded, to run slower than the same code compiled without the OpenMP flag (fully serial).

A SMALL DIAGRAM OF PARALLEL REGION

WHY CHOOSE OpenMP?

o Portable
o Standardized for shared memory architecture
Simple and fast
o Parallelization where it is relatively easy to run small portions of the application at once o Incremental parallelization
o Supports both fine and coarse grain parallelism Compact API
o Simple and Limited Set Instruction Count
o None Automatic Parallelization

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