Investigation on the dynamic of computation of semi autonomous evolutionary computation for syntactic optimization of a set of programming codes
In parallel programming, the challenges in optimizing the codes in general are more than that for serial programming. They have to be optimized for parallel execution while some parts still do have sequential execution due to data dependencies, which makes the optimization problem two folds, para...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Research Report |
| Language: | English |
| Published: |
Universiti Malaysia Sabah
2007
|
| Online Access: | https://eprints.ums.edu.my/id/eprint/22681/1/Investigation%20on%20the%20dynamic%20of%20computation%20of%20semi%20autonomous%20evolutionary%20computation%20for%20syntactic%20optimization%20of%20a%20set%20of%20programming%20codes.pdf https://eprints.ums.edu.my/id/eprint/22681/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | In parallel programming, the challenges in optimizing the codes in general are more
than that for serial programming. They have to be optimized for parallel execution
while some parts still do have sequential execution due to data dependencies, which
makes the optimization problem two folds, parallel and serial. This work focuses on
the optimization of the parallelization of a sequential code. To begin with, in parallel
computational machines, aside from the single-node performance, there exist two
important factors affecting the performance of programs written for such machines.
Firstly, the distribution of the data among the processors has an effect on the
communication time. Secondly, the number of processors in use at each step of the
parallel code (degree of parallelism) has an effect on the computation time and the
communication time as well. The more data size being transferred per processor in
one stage leads to the more communication time in that stage. The more processors
utilized leads to the less computation time but the more communication time. In
order to have a realistic characteristic of a parallel computing engine, a Rocks based
computer cluster was built and used for the test. Genetic Algorithm as one of the
Evolutionary Computation method improve the execution of parallel programming
codes by optimizing the number of processors and the distribution of data. Since
programming is not very exact and can be considered partially art then the Genetic
Algorithm is not designed to be fully autonomous and programmers hand still have to
be there, but with much reduced work. |
|---|