Nested parallelism exists in scientific codes that are searching multi-dimensional spaces. However, implementations of nested parallelism often have overhead and load balance issues. The Orbital Analysis code we present exhibits a sparse search space, significant load imbalances, and stopping when the first solution is reached. All these aspects of the algorithm exacerbate the problem of using nested parallelism effectively. In this paper, we present an inspector executor strategy for chunking such computations into parallel wavefronts. The presented shared memory parallelization is no longer nested and exhibits significantly less load imbalance. We evaluate this approach on an Orbital analysis code, and we improve the execution time from the original implementation by an order of magnitude. As part of a Graduate Computer Science course in Parallel Programming models, we show how the approach can be implemented in parallel Perl, Python, Chapel, Pthreads, and OpenMP. Future work includes investigating how to automate and generalize the parallelization approach.
@cite_8 describe a parallel algorithm implemented in MPI for the analysis of objects that are close to the earth. The algorithms in question are different than those we study in this paper.
@cite_8
2074345167
Collision threat is becoming a serious problem since the amount of space debris increases fast recently. Besides, the detective capabilities are enhanced year by year. Thus calculation time cost on orbital data processing and close approach analysis gets much longer. Single processor cannot meet the requirements to keep the analysis results up to date. We review the procedure of close approach analysis and its time cost on single processor computer, and introduce several methods we've adopted to boost up the entire procedure. Finally we show a system of parallel computing environment on a high performance computer realized in National Astronomical Observatory for the purpose of collision risk assessment.