Python library to interface with PARSEC 2.1 and 3.0 Benchmark, controlling execution triggers and processing the output measures times data to calculate speedups. Further, the library can generate a mathematical model of speedup of a parallel application, based on "Particles Swarm Optimization" or "Coupled Simulating Annealing" algorithms to discover the parameters that minimize a "objective function". The objective function can be build with a module python passed as argument to library on execution script.
- Run parsec application with multiple input sizes and, optionally, repet the execution to better find outs.
- Process a group of Parsec 2.1 or 3.0 logs files, generates from a shell direct execution of parsec.
- Manipulate of resulting data from logs process or online execution, obtained by module run script itself.
- Calculate the speedups and efficency of applications, if it's possible, using the measured times of execution.
- provide a "PSO" algorithm to model the speedup of a parallel application with regression process.
- Provide a "CSA" algorithm to model the speedup of a parallel application with regression process.
- Calculate statistics scores of model data using cross validate process.
- Parsec 2.1 or newer
- Python3 or newer
- Numpy
- Pandas
- Matplotlib with Mplot3D Toolkit (Optional, to plot 3D surface)
- Scikit-learn
$ pip3 install parsecpy
Class used to generate the measured times structure, to save such data in a "json" file, to load a previously saved json data file, to calculate the speedup or efficiency of application and to plot 2D or 3D graph of time, speedup or efficiency versus the number of cores and frequency or input size.
>>> from parsecpy import ParsecData
>>> d = ParsecData('path_to_datafile')
>>> print(d) # Print summary informations
>>> d.times() # Show a Dataframe with mesures times
>>> d.speedups() # Show a Dataframe with speedups
>>> d.plot3D(d.speedups(), title='Speedup', zlabel='speedup') # plot a 3D Plot : speedups x number of cores x input sizes
>>> d.plot3D(d.efficiency(), title='Efficiency', zlabel='efficiency') # plot a 3D Plot : speedups x number of cores x input sizes
Class used to generate the result of modeling of the application, using any of supported algorithms (PSO, CSA or
SVR). The class allows to save the modeling results, load previously saved model data, and plot the model data
together with the real measurements.
>>> from parsecpy import ParsecModel
>>> m = ParsecModel('path_to_model_datafile')
>>> print(m) # Print summary informations
>>> print(m.measure) # Show a Dataframe with mesures speedups
>>> print(m.y_model) # Show a Dataframe with modeled speedups
>>> print(m.error) # Show the Mean Squared Error between measured and modeled speedup
>>> m.plot3D(title='Speedup', showmeasures=True) # plot a 3D Plot with measurements and model data
>>> from parsecpy import ParsecLogsData
>>> l = ParsecLogsData('path_to_folder_with_logfiles')
>>> print(l) # Print summary informations
>>> l.times() # Show a Dataframe with mesures times
>>> l.speedups() # Show a Dataframe with speedups
>>> l.plot3D() # plot a 3D Plot : speedups x number of cores x input sizes
>>> from parsecpy import data_detach, Swarm, ParsecModel
>>> parsec_date = ParsecData("my_output_parsec_file.dat")
>>> out_measure = parsec_exec.speedups()
>>> meas = data_detach(out_measure)
>>> overhead = False
>>> kwargsmodel = {'overhead': overhead}
>>> sw = Swarm([0,0,0,0], [2.0,1.0,1.0,2.0], kwargs=kwargsmodel, threads=10,
size=100, maxiter=1000, modelpath=/root/mymodelfunc.py,
x_meas=meas['x'], y_meas=meas['y'])
>>> error, solution = sw.run()
>>> model = ParsecModel(bsol=solution,
berr=error,
ymeas=out_measure,
modelcodesource=sw.modelcodesource,
modelexecparams=sw.get_parameters())
>>> scores = model.validate(kfolds=10)
>>> print(model.sol)
>>> print(model.scores)
>>> import numpy as np
>>> import random
>>> from parsecpy import data_detach, Swarm, ParsecModel
>>> parsec_date = ParsecData("my_output_parsec_file.dat")
>>> out_measure = parsec_exec.speedups()
>>> meas = data_detach(out_measure)
>>> overhead = False
>>> kwargsmodel = {'overhead': overhead}
>>> initial_state = initial_state = np.array([np.random.uniform(size=5)
for _ in range(10)])
>>> csa = CoupledAnnealer(n_annealers=10, initial_state=initial_state,
tgen_initial=0.01, tacc_initial=0.1,
threads=10, steps=1000, update_interval=100, dimension=5,
args=argscsa, modelpath=/root/mymodelfunc.py
x_meas=meas['x'], y_meas=meas['y'])
>>> error, solution = csa.run()
>>> model = ParsecModel(bsol=solution,
berr=error,
measure=out_measure,
modelcodesource=csa.modelcodesource,
modelexecparams=csa.get_parameters())
>>> scores = model.validate(kfolds=10)
>>> print(model.sol)
>>> print(model.scores)
The python module file provided by user should has the following requirements:
To PSO model, should has the constraint function as following:
def constraint_function(par, x_meas, **kwargs): # your code # arguments: # par - particle object # kwargs - Dict with extra parameters: # kwargs['overhead'] - boolean value (if overhead should be considerable) # analize the feasable of particles position (searched parameters) # return True or False, depend of requirements return boolean_value
To CSA model, should has probe function as following:
def probe_function(par, tgen): # your code # arguments: # par - actual parameters values # tgen - actual temperature of generation # generate a new probe solution # return a list os parameters of probe solution return probe_solution
And the models files should has a objective function as following:
def objective_function(par, x_meas, y_meas, **kwargs): # your code # arguments: # par - particle object # x_meas - Measures array of independent variables # y_meas - Measures array of dependent variable # kwargs - Dict with extra parameters: # kwargs['overhead'] - boolean value (if overhead should be considerable) # calculate the function with should be minimized # return the calculated value return float_value
Script to run parsec app with repetitions and multiples inputs sizes
usage: parsecpy_runprocess [-h] -p PACKAGE
[-c {gcc,gcc-serial,gcc-hooks,gcc-openmp,gcc-pthreads,gcc-tbb}]
[-f FREQUENCY] [-i INPUT] [-r REPETITIONS]
[-b CPUBASE] [-v VERBOSITY]
c
Script to run parsec app with repetitions and multiples inputs sizes
positional arguments:
c List of cores numbers to be used. Ex: 1,2,4
optional arguments:
-h, --help show this help message and exit
-p PACKAGE, --package PACKAGE
Package Name to run
-c {gcc,gcc-serial,gcc-hooks,gcc-openmp,gcc-pthreads,gcc-tbb}, --compiler {gcc,gcc-serial,gcc-hooks,gcc-openmp,gcc-pthreads,gcc-tbb}
Compiler name to be used on run. (Default: gcc-hooks).
-f FREQUENCY, --frequency FREQUENCY
List of frequencies (KHz). Ex: 2000000, 2100000
-i INPUT, --input INPUT
Input name to be used on run. (Default: native).
Syntax: inputsetname[<initialnumber>:<finalnumber>].
From lowest to highest size. Ex: native or native_1:10
-r REPETITIONS, --repetitions REPETITIONS
Number of repetitions for a specific run. (Default: 1)
-b CPUBASE, --cpubase CPUBASE
If run with thread affinity(limiting the running cores
to defined number of cores), define the cpu base
number.
-v VERBOSITY, --verbosity VERBOSITY
verbosity level. 0 = No verbose
Example:
parsecpy_runprocess -p freqmine -c gcc-hooks -r 5 -i native 1,2,4,8 -v 3
Script to run swarm modelling to predict a parsec application output. On examples folder, exists a template file of configurations parameters to use on execution of this script
usage: parsecpy_runmodel [-h] --config CONFIG -f PARSECPYFILEPATH
[-p PARTICLES] [-x MAXITERATIONS]
[-l LOWERVALUES] [-u UPPERVALUES]
[-n PROBLEMSIZES] [-o OVERHEAD] [-t THREADS]
[-r REPETITIONS] [-c CROSSVALIDATION]
[-v VERBOSITY]
Script to run modelling algorithm to predict a parsec application output
optional arguments:
-h, --help show this help message and exit
--config CONFIG Filepath from Configuration file configurations
parameters
-p PARSECPYDATAFILEPATH, --parsecpydatafilepath PARSECPYDATAFILEPATH
Path from input data file from Parsec specificated
package.
-f FREQUENCIES, --frequency FREQUENCIES
List of frequencies (KHz). Ex: 2000000, 2100000
-n PROBLEMSIZES, --problemsizes PROBLEMSIZES
List of problem sizes to model used. Ex:
native_01,native_05,native_08
-o OVERHEAD, --overhead OVERHEAD
If it consider the overhead
-t THREADS, --threads THREADS
Number of Threads
-c CROSSVALIDATION, --crossvalidation CROSSVALIDATION
If run the cross validation of modelling
-m MEASURESFRACTION, --measuresfraction MEASURESFRACTION
Fraction of measures data to calculate the model
-v VERBOSITY, --verbosity VERBOSITY
verbosity level. 0 = No verbose
Example
parsecpy_runmodel --config my_config.json
-p /var/myparsecsim.dat -c True -v 3
Script to parse a folder with parsec log files and save measures an output file
parsecpy_processlogs [-h] foldername outputfilename
positional arguments:
foldername Foldername with parsec log files.
outputfilename Filename to save the measures dictionary.
optional arguments:
-h, --help show this help message and exit
Example:
parsecpy_processlogs logs_folder my-logs-folder-data.dat
Script to split a parsec input file on specific parts
parsecpy_createinputs [-h] -p {freqmine,fluidanimate} -n NUMBEROFPARTS
[-t {equal,diff}] -x EXTRAARG
inputfilename
positional arguments:
inputfilename Input filename from Parsec specificated package.
optional arguments:
-h, --help show this help message and exit
-p {freqmine,fluidanimate}, --package {freqmine,fluidanimate}
Package name to be used on split.
-n NUMBEROFPARTS, --numberofparts NUMBEROFPARTS
Number of split parts
-t {equal,diff}, --typeofsplit {equal,diff}
Split on equal or diferent size partes parts
-x EXTRAARG, --extraarg EXTRAARG
Specific argument: Freqmine=minimum support (11000),
Fluidanimate=Max number of frames
Example:
parsec_createinputs -p fluidanimate -n 10 -t diff -x 500 fluidanimate_native.tar