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Parsec Benchmark interface tool

Project description

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

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

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