I just want to start this blog post with - 'OPENMP IS SUPER AWESOME!!'
Remember, highly optimized serial code could beat naive Parallelized code.
Note about how the for loop gets parallelized: static sharing [Search for 'for loop' although there are lots of useful stuff in this article].
It's the programmer's responsibility to avoid any race conditions by
Remember, highly optimized serial code could beat naive Parallelized code.
Note about how the for loop gets parallelized: static sharing [Search for 'for loop' although there are lots of useful stuff in this article].
It's the programmer's responsibility to avoid any race conditions by
- using locks or
- avoiding shared resources as much as possible.
This is important:
'parallel' creates a parallel region - which means every thread will execute that particular block of code.
'parallel' creates a parallel region - which means every thread will execute that particular block of code.
'for' is a work-sharing directive. When called within a parallel directive, it tells OpenMP to have its iterations divided among the thread team. Be ware of the Barrier Synchronization at the end of the parallel region. All threads will block there until the last thread completes. If the code doesn't use '#pragma omp for', then each thread would execute the complete for loop. When parallelizing your loops. you must make sure your loop iterations do not have dependencies.
Reduction
The variable is initialized to the value listed in the reduction operators table [Fig. 4] for each thread. At the end of the code block [not at the end of each iteration, I'm guessing], the reduction operator is applied to each of the private copies of the variable, as well as to the original value of the variable.
Use Dynamic & Guided Scheduling are good for [Load Balancing] those situations where:
The variable is initialized to the value listed in the reduction operators table [Fig. 4] for each thread. At the end of the code block [not at the end of each iteration, I'm guessing], the reduction operator is applied to each of the private copies of the variable, as well as to the original value of the variable.
Use Dynamic & Guided Scheduling are good for [Load Balancing] those situations where:
- each iteration has variable amounts of work or
- some processors are faster than others
Guided does better than Dynamic due to less overhead associated with scheduling.
Guided & Dynamic does load balancing where as static does none of it.
Guided & Dynamic does load balancing where as static does none of it.
Nested For Loops
This link is good.
At the entrance of the inner parallel [for] directive, the OpenMP runtime library [libgomp] detects that there already exists a team and instead of a new team of N threads, it will create a team consisting of only the calling thread. Based on the version of the gcc compiler, there are different ways of handling this problem. Avoid it as much as possible is what I'm going to do.
At the entrance of the inner parallel [for] directive, the OpenMP runtime library [libgomp] detects that there already exists a team and instead of a new team of N threads, it will create a team consisting of only the calling thread. Based on the version of the gcc compiler, there are different ways of handling this problem. Avoid it as much as possible is what I'm going to do.
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