EASEA command line
- 1 Command Line Parameters To Control Parallelism
- 2 EASENA Compiler Parameters
- 3 Miscellaneous parameters
Command Line Parameters To Control Parallelism
Controlling the number of CPU threads
$ OMP_NUM_THREADS=1 ./weierstrass
will launch the weierstrass program on only 1 CPU thread (useful to evaluate the impact of CPU parallelization
Controlling which CUDA card is visible to EASEA
$ CUDA_VISIBLE_DEVICES = 1,3
makes cards 1 and 3 only visible to EASEA (useful if your computer hosts different NVIDIA graphics cards, or if the chipset of the motherboard is from NVIDIA: if you run EASEA on all NVIDIA devices, EASEA will dispatch the population on all cards, and slow cards (or the motherboard chipset) will be the bottleneck).
This is also controllable from the parameters section of the .ez file
EASENA Compiler Parameters
Out of the same .ez file, EASEA can create different source codes, depending on whether you want to use an NVIDIA GPU card or not, or whether you want to evolve structures (genetic programming) or optimise parameters (evolutionary strategies, genetic algorithms). It is also possible to create Covariance Matrix Adaptation Evolutionary Strategy (CMA-ES) code.
No option (default)
By default, EASEA parallelizes the evaluation of the individuals on the cores of a multi-core CPU using OpenMP. To try this out, cd to the the examples/weierstrass directory and run:
$ easena weierstrass.ez
If you want to see the obtained acceleration, compare the parallel evaluation execution time with evaluation using a single thread. For this, run:
$ OMP_NUM_THREADS=1 ./weierstrass
This should be slower.
EASEA can also automatically parallelize the evaluation function on the many cores of an NVIDIA card. If you want / need to clean up the directory, run:
$ make easeaclean
The -cuda command line option makes EASEA create .cu files to exploit the massive parallelism of GPU cards rather than .cpp files, that exploit the parallelism of CPU cores. The created Makefile is also different.
In order to try this out, still in the weierstrass directory, run:
$ easena weierstrass.ez -cuda
If your computer is equipped with an NVIDIA GPU card, you should experiment a nice acceleration. To evaluate the gain obtained by parallelizing on a GPU card, do as above: compare the execution time on the GPU with the time obtained on one thread of the CPU.
This makes EASEA create individuals that store equations as trees, in order to model some data.
Try this out in the regression directory with:
$ easena regression.ez -gp
To see the acceleration obtained by parallelizing on OpenMP, run with:
$ OMP_NUM_THREADS=1 ./regression
You can simplify the expression that has been found thanks to online symbolic tools such as: https://www.symbolab.com/solver/simplify-calculator
As above, but parallelized on an NVIDIA GPU card.
$ easena regression.ez -cuda_gp
Compare the execution time to a single thread CPU execution to evaluate the obtained GPU acceleration.
This option for Multi-Objective Problems (MOPs). It makes EASENA to use a template of Nondominated Sorting genetic algorithm II (tpl/NSGAII.tpl). NSGA-II is a very popular Multi-Objective Evolutionary Algorithm (MOEA) in multi-objective optimization area. This algorithm makes offspring by using chosen crossover and mutation operators and selects individuals for a new generation by nondominated-sorting (NDS) and by crowding distance (CD) comparator. In order to compile your .ez file, for example dtlz1.ez (see folder examples/dtlz/dtlz1), following steps have to be done:
$ easena -nsgaii dtlz1.ez
If you have successfully compiled you .ez file you can find .cpp, .h, Makefile and .prm files in the current folder. And now, you can compile obtained .cpp files by using Makefile:
And if an executable file is built seccessfully, you can run it:
This option for MOPs. It makes EASENA to use a template of Nondominated Sorting genetic algorithm III (tpl/NSGAIII.tpl). NSGA-III extends NSGA-II to using reference points to handle many-objective problems. In order to compile your .ez file, the steps are following:
$ easena -nsgaiii dtlz1.ez
This option for MOPs. It makes EASENA to use a template of Archived-Based Stcochastic Ranking Evolutionary Algorithm (tpl/ASREA.tpl). This MOEA ranks the population by comparing individuals with members of an archive, that breaks complexity into O(man) (m being the number of objectives, a the size of the archive and n the population size). The principe of compilation and of launching is the same as in case of -nsgaii and -nsgaiii:
$ easena -asrea dtlz1.ez
This option for MOPs. It makes EASENA to use a template of Indicator based evolutionary algorithm (tpl/IBEA.tpl). In current template the IBEA is based on progressively improvement the epsilon indicator function. Compilation and launching steps are the same as in all multi-objective cases above:
$ easena -ibea dtlz1.ez
This option for MOPs. It makes EASENA to use a template of Controlling Dominance Area of Solutions optimization algorithm (tpl/CDAS.tpl). CDAS controls the degree of expansion or contraction of the dominance area of solutions in order to improve the performance. Compilation steps are the same as in all multi-objective cases above:
$ easena -cdas dtlz1.ez
The idea behind this option is that you can modify the genome of the individual in the evaluation function, if you want to run a local optimizer there, such as a gradient descent, for instance.
Implements a CMA-ES algorithm to optimize continuous problems.
This will compile a file in verbose mode. Mainly used for debug purposes.
This true line compiling mode will locate errors differently. Useful it it seems to you that the error is not located where the easea compiler tells you it is.