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fearghalodonnchafearghalodonncha
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added script for rapid observation generation
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‎Dockerfile-amdados‎

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# Building:
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# docker build --tag dev-allscale:compiler-env . -f Dockerfile-amdados
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# Removing intermediate images and inactive containers afterwards:
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# docker images -q --filter dangling=true | xargs docker rmi
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# docker rm $(docker ps -qa --no-trunc --filter "status=exited")
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# Extracting for sharing on another machine:
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# docker save dev-allscale:compiler-env > amdados-cpu-ubuntu.tar
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# Loading on another machine:
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# docker load < amdados-cpu-ubuntu.tar
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# Running with network, sharing of the directory "work" and
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# full access to physical memory:
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# docker run -it --volume /home/albert/work:/root/work \
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# --cap-add=NET_ADMIN --device=/dev/net/tun \
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# --privileged --volume /dev/shm:/dev/shm \
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# dev-images:tf-cpu-ubuntu /bin/bash
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FROM debian:9.4
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LABEL maintainer="Fearghal O'Donncha, feardonn@ie.ibm.com"
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ENV LC_ALL=
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# System package.
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RUN apt-get update && apt-get -y upgrade && \
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apt-get install -y --no-install-recommends \
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build-essential gdb gfortran cmake make automake \
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unzip bzip2 curl wget rsync tmux htop \
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libfreetype6-dev libpng12.* libzmq3-dev libjpeg-dev libtiff[0-9]*-dev librsvg2-dev \
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pkg-config software-properties-common \
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libcurl.*-openssl-dev libpcre++-dev libxml2-dev \
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git mc vim nano make cmake gdb openssh-client openssh-server \
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binutils binutils-dev sshfs exuberant-ctags valgrind \
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libblas-dev liblapack-dev liblapacke-dev \
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libarpack2-dev libarpack2++-dev libopenblas-dev \
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libatlas-base-dev libsuperlu.*-dev libopenblas-dev \
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python3 python3-dev python3-pip \
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python3-numpy python3-scipy python3-matplotlib python3-setuptools \
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&& \
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apt-get clean && apt-get autoremove && \
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apt-get update && \
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apt-get install -y --no-install-recommends groff && \
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rm -rf /var/lib/apt/lists/* \
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&& \
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ssh-keygen -t rsa -f ${HOME}/.ssh/id_rsa -q -P "" \
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&& \
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echo '' >> ~/.bashrc && \
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echo 'alias nano="nano -i -c --tabsize=4 --tabstospaces "' \
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>> ~/.bashrc && \
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echo 'alias py3="python3 "' \
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>> ~/.bashrc && \
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echo 'alias py3d="python3 -m pdb "' \
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>> ~/.bashrc && \
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echo 'PS1="\[\e[1;32m\]\u\[\e[1;36m\]@\[\e[1;31m\]\h\[\e[1;35m\]:\w\$\[\e[0;30m\] "' \
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>> ~/.bashrc
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# Working directory.
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WORKDIR /root/work
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# Execute this command on start-up.
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CMD ["/bin/bash"]
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‎python/RandObservationsGenerator.py‎

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# -----------------------------------------------------------------------------
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# Author : Albert Akhriev, albert_akhriev@ie.ibm.com
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# Copyright : IBM Research Ireland, 2017-2018
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# -----------------------------------------------------------------------------
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import sys, traceback, os, re, getopt, math, argparse, subprocess
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import numpy as np
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from timeit import default_timer as timer
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from Configuration import Configuration
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from Utility import *
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# Full-path name of C++ executable.
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AMDADOS_EXE = "build/app/amdados"
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def Amdados2D(config_file, demo):
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""" Advection-diffusion PDE forward solver.
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"""
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# Initialize parameters, global indices and output directory.
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conf = Configuration(config_file)
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conf = InitDependentParams(conf)
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glo_idx = GlobalIndices(conf)
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if demo: conf.PrintParameters()
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MakePath(conf.output_dir + os.sep)
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# Load sensor locations previously generated by C++ 'amdados' application:
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# './build/app/amdados --scenario sensors'
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# If does not exist, the file of sensor locations will be regenerated.
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sensors_filename = MakeFileName(conf, "sensors")
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if not os.path.exists(sensors_filename):
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print("")
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print("")
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print("WARNING: missed file of sensor locations: " + sensors_filename)
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print("generating a new one ...")
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assert os.path.exists(AMDADOS_EXE), (
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"Application 'amdados' must be built before running this script")
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subprocess.Popen([AMDADOS_EXE, "--scenario", "sensors",
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"--config", config_file])
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os.sync()
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print("")
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print("")
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sensor_idx = LoadSensorLocations(conf)
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Nt = round(conf.Nt)
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writer = Writer(conf)
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for k in range(Nt):
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# Write the field entries at sensors into the file of observations from random number
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writer.WriteField(sensor_idx, k)
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# Run forward simulation and record the "true" solutions into a file.
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# with open(MakeFileName(conf, "true_field"), "wb") as fid:
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# ForwardSolver(conf, glo_idx, sensor_idx, fid, demo)
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###############################################################################
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# Initialization.
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###############################################################################
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def InitDependentParams(conf):
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""" Function initializes dependent parameters given
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the primary ones specified by user.
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"""
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# Ensure integer values.
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conf.nx = round(conf.num_subdomains_x * conf.subdomain_x)
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conf.ny = round(conf.num_subdomains_y * conf.subdomain_y)
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conf.integration_nsteps = round(conf.integration_nsteps)
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# Diffusion coefficient must be positive float value.
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D = float(conf.diffusion_coef)
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assert D > 0
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# Deduce space discretization steps.
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conf.problem_size = int(conf.nx * conf.ny)
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dx = float(conf.domain_size_x) / float(conf.nx-1)
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dy = float(conf.domain_size_y) / float(conf.ny-1)
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assert (dx > 0) and (dy > 0)
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conf.dx = dx
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conf.dy = dy
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# Deduce the optimal time step from the stability criteria.
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tiny = np.finfo(float).tiny / np.finfo(float).eps**3
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dt_base = float(conf.integration_period) / float(conf.integration_nsteps)
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max_vx = float(conf.flow_model_max_vx)
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max_vy = float(conf.flow_model_max_vy)
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dt = min(dt_base, min( min(dx**2, dy**2)/(2.0*D + tiny),
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1.0/(abs(max_vx)/dx + abs(max_vy)/dy + tiny) ))
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assert(dt > 0)
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conf.dt = dt
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conf.Nt = round(math.ceil(float(conf.integration_period) / dt))
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# Compute coefficients that will be used in the finite-difference scheme.
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conf.rho_x = float(D * dt / dx**2)
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conf.rho_y = float(D * dt / dy**2)
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conf.v0x = float(2.0 * dx / dt)
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conf.v0y = float(2.0 * dy / dt)
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return conf
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def GlobalIndices(conf):
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""" Each nodal point gets a unique global index on the grid.
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Function initializes a 2D array of indices:
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index of (x,y) = glo_idx(x,y).
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"""
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glo_idx = np.arange(round(conf.problem_size)).reshape((conf.nx, conf.ny))
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return glo_idx
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def LoadSensorLocations(conf):
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""" Function loads sensor locations, i.e. indices of domain
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points occupied by sensors.
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"""
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print("Loading sensor locations ...")
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Nx = conf.nx
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Ny = conf.ny
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Np = round(math.ceil(Nx * Ny * conf.sensor_fraction))
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data = np.loadtxt(MakeFileName(conf, "sensors"), dtype=int)
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assert (data is not None) and (data.size > 0), "empty file of sensors"
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assert len(data.shape) == 2 and data.shape[1] == 2, "wrong layout"
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assert data.shape[0] <= Nx * Ny, "too many records"
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assert data.dtype == int, "type mismatch"
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assert np.all(data >= 0), "negative index"
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assert np.all(data[:,0] < Nx), "x-index is out of bound"
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assert np.all(data[:,1] < Ny), "y-index is out of bound"
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return data
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###############################################################################
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# Utilities.
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###############################################################################
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class Writer:
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""" Class for writing simulated solution at sensor locations in a text file.
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"""
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def __init__(self, conf):
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""" Constructor. TODO: could be useful to add a header to file:
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"""
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# N O T E: np.savetxt() expects (!?) file opened in binary format "wb".
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self.fid = open(MakeFileName(conf, "analytic"), "wb")
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def __del__(self):
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""" This method is not a destructor, but a normal method that is always
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called before the garbage collector destroys the object.
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https://stackoverflow.com/questions/37852560/is-del-really-a-destructor
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"""
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if self.fid is not None:
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self.fid.flush()
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self.fid.close()
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self.fid = None
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def WriteField(self, sensor_idx, discrete_time):
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""" Function appends the new field to the output file.
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Only values at sensor locations are written.
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column 1: discrete time;
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column 2: sensor abscissas (point indices) on the grid;
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column 3: sensor ordinates (point indices) on the grid;
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column 4: values at sensor locations.
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"""
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assert isinstance(discrete_time, int) and discrete_time >= 0
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assert len(sensor_idx.shape) == 2 and sensor_idx.shape[1] == 2
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xind = sensor_idx[:, 0].astype(int)
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yind = sensor_idx[:, 1].astype(int)
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# For the purpose of performance analysis just set vals as random number
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vals = np.random.rand(len(xind)).astype(float)
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assert np.isnan(vals).any() == False, "NaN in observations"
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vals[np.fabs(vals) <= np.finfo(np.float64).eps]=0
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data = np.column_stack((xind, yind, vals))
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num = data.shape[0]
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np.savetxt(self.fid, np.array([discrete_time, num]), fmt="%d")
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np.savetxt(self.fid, data, fmt="%d %d %g");
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###############################################################################
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# Entry point.
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###############################################################################
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if __name__ == "__main__":
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try:
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# CheckPythonVersion()
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parser = argparse.ArgumentParser()
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parser.add_argument("--config",
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type=str, default="amdados.conf",
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help="path to configuration file")
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parser.add_argument("--demo", nargs="?", const=True,
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type=bool, default=False,
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help="show live progress in a window")
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param = parser.parse_args()
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param.config = os.path.expanduser(param.config)
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print("Options:")
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print("Visualization: " + str(param.demo))
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print("Configuration files: " + param.config)
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print("")
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Amdados2D(param.config, param.demo)
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except subprocess.CalledProcessError as error:
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traceback.print_exc()
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if error.output is not None:
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print("ERROR: " + str(error.output))
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else:
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print("CalledProcessError")
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except AssertionError as error:
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traceback.print_exc()
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print("ERROR: " + str(error.args))
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except ValueError as error:
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traceback.print_exc()
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print("ERROR: " + str(error.args))
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except Exception as error:
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traceback.print_exc()
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print("ERROR: " + str(error.args))

‎python/RunMultipleConfigForExperiment.py‎

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# -----------------------------------------------------------------------------
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# Author : Albert Akhriev, albert_akhriev@ie.ibm.com
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# Copyright : IBM Research Ireland, 2017-2018
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# -----------------------------------------------------------------------------
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""" This script runs several simulations with increasing problem size
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utilizing all available CPUs. It saves the execution time of each
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simulation in a file that can be used to plot the scalability profile.
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Essential parameters, listed in the first lines, include the set of
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problem sizes and integration period.
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Each simulation is twofold. First, we run the Python forward solver that
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generates the ground-truth and observations ("ObservationsGenerator.py").
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The Python code itself uses the C++ code running in the special mode for
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generating sensor locations (scenario "sensors"). Second, the C++ data
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assimilation application is launched (scenario "simulation") with
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observations generated by "ObservationsGenerator.py".
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The results of all the simulations are accumulated in the output
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directory and can be visualized later on by the script "Visualize.py".
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The configuration file "amdados.conf" is used in all the simulations with
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modification of three parameters: grid sizes (number of subdomains) in both
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dimensions and integration time. Other parameters remain intact. It is not
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recommended to tweak parameters unless their meaning is absolutely clear.
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If you had modified the parameters, please, consider to rerun this script
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because the results in the output directory a not valid any longer.
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The script was designed to fulfil the formal requirements of the
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Allscale project.
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"""
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print(__doc__)
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from timeit import default_timer as timer
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import os, cmd
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from RandObservationsGenerator import InitDependentParams, Amdados2D
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from Utility import *
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GridSizes = [(2,2),(4,4)]
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# Integration period in seconds.
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IntegrationPeriod = 100
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# Path to the C++ executable.
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AMDADOS_EXE = "build/app/amdados"
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if __name__ == "__main__":
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try:
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# Read configuration file.
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conf = Configuration("amdados.conf")
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# Create the output directory, if it does not exist.
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if not os.path.isdir(conf.output_dir):
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os.mkdir(conf.output_dir)
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# Check existence of "amdados" application executable.
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assert os.path.isfile(AMDADOS_EXE), "amdados executable was not found"
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# For all the grid sizes in the list ...
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exe_time_profile = np.zeros((len(GridSizes),2))
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for grid_no, grid in enumerate(GridSizes):
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assert grid[0] >= 2 and grid[1] >= 2, "the minimum grid size is 2x2"
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# Modify parameters given the current grid size.
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setattr(conf, "num_subdomains_x", int(grid[0]))
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setattr(conf, "num_subdomains_y", int(grid[1]))
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setattr(conf, "integration_period", int(IntegrationPeriod))
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InitDependentParams(conf)
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conf.PrintParameters()
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config_file = conf.WriteParameterFile("scalability_test.conf")
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os.sync()
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# Python simulator generates the ground-truth and observations.
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Amdados2D(config_file, False)
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# Get the starting time.
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start_time = timer()
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# Run C++ data assimilation application.
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print("##################################################")
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print("Simulation by 'amdados' application ...")
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print("silent if debugging & messaging were disabled")
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print("##################################################")
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print(AMDADOS_EXE, config_file)
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subprocess.call([AMDADOS_EXE, "--scenario", "simulation",
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"--config", config_file])
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os.sync()
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# Get the execution time and corresponding (global) problem size
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# and save the current scalability profile into the file.
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problem_size = ( conf.num_subdomains_x * conf.subdomain_x *
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conf.num_subdomains_y * conf.subdomain_y )
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exe_time_profile[grid_no,0] = problem_size
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exe_time_profile[grid_no,1] = timer() - start_time
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np.savetxt(os.path.join(conf.output_dir, "scalability_performance.txt"),
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exe_time_profile)
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except subprocess.CalledProcessError as error:
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traceback.print_exc()
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if error.output is not None:
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print("ERROR: " + str(error.output))
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else:
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print("CalledProcessError")
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except AssertionError as error:
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traceback.print_exc()
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print("ERROR: " + str(error.args))
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except ValueError as error:
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traceback.print_exc()
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print("ERROR: " + str(error.args))
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except Exception as error:
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traceback.print_exc()
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print("ERROR: " + str(error.args))
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