Parallelizaton for stdlist problem

  • Thread starter xxh418
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In summary, the conversation discusses the use of OpenMP to parallelize a loop in C++ using iterators defined by the C++ standard library containers. However, the use of OpenMP command (#pragma) is not working and the simulation runs slowly. The conversation also touches on the limitations of using OpenMP with lists and suggests splitting the list into sub-lists as a possible solution for parallelization.
  • #1
xxh418
11
0
Hi all,
I am trying to using OpenMP to parallelize a loop in C++.
The iterator for the loop is defined by std::list using C++ standard library containers.
The following is an example:


for (std::list<int>::iterator it=mylist.begin() ; it != mylist.end(); ++it)
std::cout << ' ' << *it;

However, I found I can not use OpenMP command to do the parallelzation (#pragma). The simulation runs very slowly. For OpenMP, the first index in the for loop must be a integer for parallelzation. Does anyone encounter the same problem and have any idea how to solve it? Thank you very much!
 
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  • #2
You want to parallelize "cout"s? I don't see how this is supposed to work.
 
  • #3
The basic idea of a "list" is that the entries are linked in sequence, by pointers.

All the "nice" C++ syntax like iterators, the ++ operator, etc isn't "magic". All it does is hide the details from you (which is useful, because you don't have to change the code if you replace the list by a diferent data structure).

The only way to parallelize access to the list entries would be to split it into several sub-lists.
 

1. What is parallelization for stdlist problem?

Parallelization for stdlist problem is a technique in computer science where a task is divided into smaller sub-tasks that can be executed simultaneously on multiple processors or cores. This allows for faster execution of tasks and can improve overall performance.

2. Why is parallelization important for stdlist problem?

Parallelization is important for stdlist problem because it allows for better utilization of resources and can significantly reduce the time it takes to complete a task. This is especially useful for problems that involve large datasets or complex computations.

3. What are the benefits of using parallelization for stdlist problem?

The main benefits of using parallelization for stdlist problem include faster execution times, improved performance, and better scalability. It also allows for better use of resources, as multiple processors can work on different parts of the problem simultaneously.

4. What are the potential challenges of implementing parallelization for stdlist problem?

One potential challenge of implementing parallelization for stdlist problem is the need for specialized hardware or software, as well as the additional complexity of managing multiple processors and coordinating their tasks. Additionally, parallelization may not always result in significant performance improvements and can even lead to slower execution times if not implemented properly.

5. How can I determine if parallelization is suitable for my stdlist problem?

The best way to determine if parallelization is suitable for your stdlist problem is to analyze the problem and its requirements, as well as the available resources and potential benefits. It is also important to consider the scalability and complexity of the problem, as well as the potential challenges and trade-offs of using parallelization.

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