Discussion Overview
The discussion revolves around the use of integer pointers in OpenMP parallel processing, specifically addressing the privatization of pointers defined outside of for loops. Participants explore the implications of making pointers private or firstprivate in a multi-threaded context, as well as the behavior of nested loops in parallelization.
Discussion Character
- Technical explanation
- Debate/contested
Main Points Raised
- Some participants inquire whether an integer pointer defined outside a for loop can be made private for threads, similar to integer variables.
- Others suggest that using thread local storage might be relevant, though its connection to OpenMP is questioned.
- Concerns are raised about the undefined behavior of reading a variable modified within a loop from outside that loop.
- It is noted that declaring a pointer as private results in each thread having its own pointer pointing to random memory, while firstprivate keeps the address the same across threads.
- Clarifications are sought regarding the use of the "new" function for pointer creation and its implications during privatization.
- Participants discuss the use of break and goto statements in nested loops when only the outer loop is parallelized, with differing views on their appropriateness and performance impact.
- Some express skepticism about the effectiveness of parallelizing loops with branches, suggesting that it may lead to poor performance.
- Concerns are raised about the clarity of the original poster's intentions and the need for more context to provide useful advice.
Areas of Agreement / Disagreement
Participants express differing views on the implications of pointer privatization and the use of control statements in nested parallel loops. There is no consensus on the best practices for these scenarios, indicating ongoing debate and uncertainty.
Contextual Notes
Limitations include unclear definitions of variable scope and the potential for undefined behavior when accessing modified variables outside their loops. The discussion also highlights the complexity of achieving efficient parallel performance with OpenMP.