parsecpy 1.0.6

Description:

parsecpy 1.0.6

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.

Features

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.



Prerequisites

Parsec 2.1 or newer
Python3 or newer
Numpy
Pandas
Matplotlib with Mplot3D Toolkit (Optional, to plot 3D surface)
Scikit-learn



Site

https://github.com/alexfurtunatoifrn/parsecpy



Installation
$ pip3 install parsecpy


Usage

Class ParsecData
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 ParsecModel
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


Class ParsecLogsData
>>> 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


Class Swarm
>>> 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)


Class CoupledAnnealer
>>> 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)

Requirements for model python module
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





Run Parsec
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


Run PSO or CSA Modelling script
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


Logs process
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


Create split parts
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