6. cvičenie – pokročilé MPI 📬

Úvod

Zdrojové súbory na cvičenie si stiahnite spoločne ako archív.

Úlohy cvičenia pokrývajú problematiku preberanú na prednáške. Vašou úlohou je dokončiť kód v každej úlohe podľa TODO pokynov v komentároch.

1. Rozdelenie komunikátora


#################################################################
#                                                               #
#  This file has been written as a sample solution to an        #
#  exercise in a course given at the High Performance           #
#  Computing Centre Stuttgart (HLRS).                           #
#  The examples are based on the examples in the MPI course of  #
#  the Edinburgh Parallel Computing Centre (EPCC).              #
#  It is made freely available with the understanding that      #
#  every copy of this file must include this header and that    #
#  HLRS and EPCC take no responsibility for the use of the      #
#  enclosed teaching material.                                  #
#                                                               #
#  Authors: Joel Malard, Alan Simpson,            (EPCC)        #
#           Rolf Rabenseifner, Traugott Streicher,              #
#           Tobias Haas (HLRS)                                  #
#                                                               #
#  Contact: rabenseifner@hlrs.de                                #
#                                                               #
#  Purpose: A program to try MPI_Comm_split                     #
#                                                               #
#  Contents: Python code, buffer send version (comm.Send)       #
#                                                               #
#################################################################

from mpi4py import MPI
import numpy as np

sumA = np.empty((), dtype=np.intc)
sumB = np.empty((), dtype=np.intc)

comm_world = MPI.COMM_WORLD
world_size = comm_world.Get_size()
my_world_rank = np.array(comm_world.Get_rank(), dtype=np.intc)

# TODO: Nastavte 'mycolor' tak, rozdelila uzly na 1/3 a 2/3 celkoveho poctu
#       uzlov 'world_size'.
# Pomocka: 'mycolor' by mal byt nastaveny podla vysledku logickeho vyrazu. To
#          znamena, ze prva farba bude napriklad 0 ('False') a druha nenulova
#          ('True').
mycolor = False

# TODO: Rozdelte 'comm_world' na zaklade farby 'mycolor'. Ranking by sa nemal
#       zmenit. Vytvorte komunikator 'sub_comm'.
sub_comm = None

# TODO: Zistite velkost vytvoreneho komunikatora a ulozte ju do 'sub_size'.
sub_size = 0

# TODO: Alokujte a inicializujte Numpy pole s jednym prvkom (jeho hodnota bude
#       rank uzla v sub-komunikatore) typu 'intc'.
my_sub_rank = np.array(0)

# TODO: Vypocitajte sucet vsetkych rankov:
# 1) my_world_rank; sucet bude ulozeny v 'sumA'
# 2) my_sub_rank; sucet bude ulozeny v 'sumB'
# Pouzite operaciu 'Allreduce()' na sub-komunikatore.



print(
    "PE world:{:3d}, color={:d} sub:{:3d}, SumA={:3d}, SumB={:3d} in sub_comm".format(
        my_world_rank, mycolor, my_sub_rank, sumA, sumB
    )
)

#################################################################

# #

# This file has been written as a sample solution to an #

# exercise in a course given at the High Performance #

# Computing Centre Stuttgart (HLRS). #

# The examples are based on the examples in the MPI course of #

# the Edinburgh Parallel Computing Centre (EPCC). #

# It is made freely available with the understanding that #

# every copy of this file must include this header and that #

# HLRS and EPCC take no responsibility for the use of the #

# enclosed teaching material. #

# #

# Authors: Joel Malard, Alan Simpson, (EPCC) #

# Rolf Rabenseifner, Traugott Streicher, #

# Tobias Haas (HLRS) #

# #

# Contact: rabenseifner@hlrs.de #

# #

# Purpose: A program to try MPI_Comm_split #

# #

# Contents: Python code, buffer send version (comm.Send) #

# #

#################################################################

from mpi4py import MPI

import numpy as np

sumA = np.empty((), dtype=np.intc)

sumB = np.empty((), dtype=np.intc)

comm_world = MPI.COMM_WORLD

world_size = comm_world.Get_size()

my_world_rank = np.array(comm_world.Get_rank(), dtype=np.intc)

# TODO: Nastavte 'mycolor' tak, rozdelila uzly na 1/3 a 2/3 celkoveho poctu

# uzlov 'world_size'.

# Pomocka: 'mycolor' by mal byt nastaveny podla vysledku logickeho vyrazu. To

# znamena, ze prva farba bude napriklad 0 ('False') a druha nenulova

# ('True').

mycolor = False

# TODO: Rozdelte 'comm_world' na zaklade farby 'mycolor'. Ranking by sa nemal

# zmenit. Vytvorte komunikator 'sub_comm'.

sub_comm = None

# TODO: Zistite velkost vytvoreneho komunikatora a ulozte ju do 'sub_size'.

sub_size = 0

# TODO: Alokujte a inicializujte Numpy pole s jednym prvkom (jeho hodnota bude

# rank uzla v sub-komunikatore) typu 'intc'.

my_sub_rank = np.array(0)

# TODO: Vypocitajte sucet vsetkych rankov:

# 1) my_world_rank; sucet bude ulozeny v 'sumA'

# 2) my_sub_rank; sucet bude ulozeny v 'sumB'

# Pouzite operaciu 'Allreduce()' na sub-komunikatore.

print(

"PE world:{:3d}, color={:d} sub:{:3d}, SumA={:3d}, SumB={:3d} in sub_comm".format(

my_world_rank, mycolor, my_sub_rank, sumA, sumB

)

2. Jednorozmerná karteziánska topológia

#################################################################
#                                                               #
#  This file has been written as a sample solution to an        #
#  exercise in a course given at the High Performance           #
#  Computing Centre Stuttgart (HLRS).                           #
#  The examples are based on the examples in the MPI course of  #
#  the Edinburgh Parallel Computing Centre (EPCC).              #
#  It is made freely available with the understanding that      #
#  every copy of this file must include this header and that    #
#  HLRS and EPCC take no responsibility for the use of the      #
#  enclosed teaching material.                                  #
#                                                               #
#  Authors: Joel Malard, Alan Simpson,            (EPCC)        #
#           Rolf Rabenseifner, Traugott Streicher,              #
#           Tobias Haas (HLRS)                                  #
#                                                               #
#  Contact: rabenseifner@hlrs.de                                #
#                                                               #
#  Purpose: Creating a 1-dimens. topology with MPI_Cart_create  #
#                                                               #
#  Contents: Python code, buffer send version (comm.Send)       #
#                                                               #
#################################################################

from mpi4py import MPI
import numpy as np

# Alokacia priestoru pre vstupne argumenty kartezianskej topologie.
rcv_buf = np.empty((), dtype=np.intc)
dims = np.empty(1, dtype=np.intc)
periods = [False]

status = MPI.Status()

comm_world = MPI.COMM_WORLD
size = comm_world.Get_size()
my_rank = comm_world.Get_rank()

# TODO
# Pripravte vstupne argumenty pre vytvorenie jednorozmernej kartezianskej topologie
# v kruhovom bufferi so zapnutym precislovanim rankov.
dims[0] = 0
periods[0] = False
reorder = False

# TODO: Vytvorte karteziansku topologiu. Novy komunikator ulozte do premennej
#       'new_comm'.
new_comm = None
# TODO: Zistite rank vytvoreneho komunikatora.
my_rank = 0
# TODO: Zistite koordinaty 'my_coords' v kartezianskej topologii.
my_coords = [0]

# Vypocet ranku susedov.
right = (my_rank + 1) % size
left = (my_rank - 1 + size) % size

# Komunikacia v kruhovom bufferi.
sum = 0
snd_buf = np.array(my_rank, dtype=np.intc)

for i in range(size):
    request = new_comm.Issend((snd_buf, 1, MPI.INT), right, 17)
    new_comm.Recv((rcv_buf, 1, MPI.INT), left, 17, status)
    request.Wait(status)
    np.copyto(snd_buf, rcv_buf)
    sum += rcv_buf

# Vysledok.
print(
    f"Node {my_rank}:\tSum = {sum}, Coords = {my_coords[0]}, world rank = {comm_world.Get_rank()}"
)

#################################################################

# #

# This file has been written as a sample solution to an #

# exercise in a course given at the High Performance #

# Computing Centre Stuttgart (HLRS). #

# The examples are based on the examples in the MPI course of #

# the Edinburgh Parallel Computing Centre (EPCC). #

# It is made freely available with the understanding that #

# every copy of this file must include this header and that #

# HLRS and EPCC take no responsibility for the use of the #

# enclosed teaching material. #

# #

# Authors: Joel Malard, Alan Simpson, (EPCC) #

# Rolf Rabenseifner, Traugott Streicher, #

# Tobias Haas (HLRS) #

# #

# Contact: rabenseifner@hlrs.de #

# #

# Purpose: Creating a 1-dimens. topology with MPI_Cart_create #

# #

# Contents: Python code, buffer send version (comm.Send) #

# #

#################################################################

from mpi4py import MPI

import numpy as np

# Alokacia priestoru pre vstupne argumenty kartezianskej topologie.

rcv_buf = np.empty((), dtype=np.intc)

dims = np.empty(1, dtype=np.intc)

periods = [False]

status = MPI.Status()

comm_world = MPI.COMM_WORLD

size = comm_world.Get_size()

my_rank = comm_world.Get_rank()

# TODO

# Pripravte vstupne argumenty pre vytvorenie jednorozmernej kartezianskej topologie

# v kruhovom bufferi so zapnutym precislovanim rankov.

dims[0] = 0

periods[0] = False

reorder = False

# TODO: Vytvorte karteziansku topologiu. Novy komunikator ulozte do premennej

# 'new_comm'.

new_comm = None

# TODO: Zistite rank vytvoreneho komunikatora.

my_rank = 0

# TODO: Zistite koordinaty 'my_coords' v kartezianskej topologii.

my_coords = [0]

# Vypocet ranku susedov.

right = (my_rank + 1) % size

left = (my_rank - 1 + size) % size

# Komunikacia v kruhovom bufferi.

sum = 0

snd_buf = np.array(my_rank, dtype=np.intc)

for i in range(size):

request = new_comm.Issend((snd_buf, 1, MPI.INT), right, 17)

new_comm.Recv((rcv_buf, 1, MPI.INT), left, 17, status)

request.Wait(status)

np.copyto(snd_buf, rcv_buf)

sum += rcv_buf

# Vysledok.

print(

f"Node {my_rank}:\tSum = {sum}, Coords = {my_coords[0]}, world rank = {comm_world.Get_rank()}"

)

3. Karteziánska topológia valca

#################################################################
#                                                               #
#  This file has been written as a sample solution to an        #
#  exercise in a course given at the High Performance           #
#  Computing Centre Stuttgart (HLRS).                           #
#  The examples are based on the examples in the MPI course of  #
#  the Edinburgh Parallel Computing Centre (EPCC).              #
#  It is made freely available with the understanding that      #
#  every copy of this file must include this header and that    #
#  HLRS and EPCC take no responsibility for the use of the      #
#  enclosed teaching material.                                  #
#                                                               #
#  Authors: Joel Malard, Alan Simpson,            (EPCC)        #
#           Rolf Rabenseifner, Traugott Streicher,              #
#           Tobias Haas (HLRS)                                  #
#                                                               #
#  Contact: rabenseifner@hlrs.de                                #
#                                                               #
#  Purpose: Creating a 2-dimensional topology.                  #
#                                                               #
#  Contents: Python code, buffer send version (comm.Send)       #
#                                                               #
#################################################################

from mpi4py import MPI
import numpy as np

max_dims = 2

rcv_buf = np.empty((), dtype=np.intc)

dims = [0] * max_dims
periods = [False] * max_dims
coords = np.empty((max_dims), dtype=np.intc)

status = MPI.Status()

comm_world = MPI.COMM_WORLD
size = comm_world.Get_size()

# TODO: Nastavte karteziansku topologiu.

# 1) Nastavte 'periods': os 0 bude cyklicka, os 1 nebude cyklicka.
# periods[0] = ???; periods[1] = ???
periods[0] = False
periods[1] = False

# 2) Nastavte precislovanie rankov 'reorder' na 'True'.
reorder = False

# 3) Vypocitajte dimezie 'dims' pomocou funkcie 'Compute_dims()'.
#    Druhy argument musi byt tuple/zoznam dvoch nul.
#    Pocet prvkov v poslednom argumente urcuje na kolko dimenzii budu uzly
#    rozdelene.
dims = [0, 0]

# TODO: Vytvorte Katrteziansku topologiu. Kuminikator ulozte do premennej
#       'new_comm'.
new_comm = None

# TODO: Zistite 'my_rank' v novom komunikatore.
my_rank = 0

# TODO: Zistite koordinaty 'my_coords' v kartezianskej topologii.
my_coords = [0, 0]

# TODO:
# Vypocitajte ranky laveho a praveho suseda podla 'my_coords',
# pouzite Get_cart_rank(). Vypocitajte susedov iba pre dimenziu 0.
# pomocka: Get_Cart_rank() vie spracovat pozicie mimo rozsahu pre kruhove dimenzie!
for i in range(max_dims):
    coords[i] = my_coords[i]
coords[0] = 0
left = new_comm.Get_cart_rank([0, 0])
coords[0] = 0
right = new_comm.Get_cart_rank([0, 0])

# Vypocet celkoveho suctu

sum = 0
snd_buf = np.array(my_rank, dtype=np.intc)

# TODO: Nastavte cyklus iba napriec dimenziou 0.
for i in range(0):
    request = new_comm.Issend((snd_buf, 1, MPI.INT), right, 17)

    new_comm.Recv((rcv_buf, 1, MPI.INT), left, 17, status)

    request.Wait(status)

    np.copyto(snd_buf, rcv_buf)
    sum += rcv_buf

print(f"{my_rank:2d}, Coords=({my_coords[0]},{my_coords[1]}): Sum = {sum}")

#################################################################

# #

# This file has been written as a sample solution to an #

# exercise in a course given at the High Performance #

# Computing Centre Stuttgart (HLRS). #

# The examples are based on the examples in the MPI course of #

# the Edinburgh Parallel Computing Centre (EPCC). #

# It is made freely available with the understanding that #

# every copy of this file must include this header and that #

# HLRS and EPCC take no responsibility for the use of the #

# enclosed teaching material. #

# #

# Authors: Joel Malard, Alan Simpson, (EPCC) #

# Rolf Rabenseifner, Traugott Streicher, #

# Tobias Haas (HLRS) #

# #

# Contact: rabenseifner@hlrs.de #

# #

# Purpose: Creating a 2-dimensional topology. #

# #

# Contents: Python code, buffer send version (comm.Send) #

# #

#################################################################

from mpi4py import MPI

import numpy as np

max_dims = 2

rcv_buf = np.empty((), dtype=np.intc)

dims = [0] * max_dims

periods = [False] * max_dims

coords = np.empty((max_dims), dtype=np.intc)

status = MPI.Status()

comm_world = MPI.COMM_WORLD

size = comm_world.Get_size()

# TODO: Nastavte karteziansku topologiu.

# 1) Nastavte 'periods': os 0 bude cyklicka, os 1 nebude cyklicka.

# periods[0] = ???; periods[1] = ???

periods[0] = False

periods[1] = False

# 2) Nastavte precislovanie rankov 'reorder' na 'True'.

reorder = False

# 3) Vypocitajte dimezie 'dims' pomocou funkcie 'Compute_dims()'.

# Druhy argument musi byt tuple/zoznam dvoch nul.

# Pocet prvkov v poslednom argumente urcuje na kolko dimenzii budu uzly

# rozdelene.

dims = [0, 0]

# TODO: Vytvorte Katrteziansku topologiu. Kuminikator ulozte do premennej

# 'new_comm'.

new_comm = None

# TODO: Zistite 'my_rank' v novom komunikatore.

my_rank = 0

# TODO: Zistite koordinaty 'my_coords' v kartezianskej topologii.

my_coords = [0, 0]

# TODO:

# Vypocitajte ranky laveho a praveho suseda podla 'my_coords',

# pouzite Get_cart_rank(). Vypocitajte susedov iba pre dimenziu 0.

# pomocka: Get_Cart_rank() vie spracovat pozicie mimo rozsahu pre kruhove dimenzie!

for i in range(max_dims):

coords[i] = my_coords[i]

coords[0] = 0

left = new_comm.Get_cart_rank([0, 0])

coords[0] = 0

right = new_comm.Get_cart_rank([0, 0])

# Vypocet celkoveho suctu

sum = 0

snd_buf = np.array(my_rank, dtype=np.intc)

# TODO: Nastavte cyklus iba napriec dimenziou 0.

for i in range(0):

request = new_comm.Issend((snd_buf, 1, MPI.INT), right, 17)

new_comm.Recv((rcv_buf, 1, MPI.INT), left, 17, status)

request.Wait(status)

np.copyto(snd_buf, rcv_buf)

sum += rcv_buf

print(f"{my_rank:2d}, Coords=({my_coords[0]},{my_coords[1]}): Sum = {sum}")

4. Súvislý údajový typ

#################################################################
#                                                               #
#  This file has been written as a sample solution to an        #
#  exercise in a course given at the High Performance           #
#  Computing Centre Stuttgart (HLRS).                           #
#  The examples are based on the examples in the MPI course of  #
#  the Edinburgh Parallel Computing Centre (EPCC).              #
#  It is made freely available with the understanding that      #
#  every copy of this file must include this header and that    #
#  HLRS and EPCC take no responsibility for the use of the      #
#  enclosed teaching material.                                  #
#                                                               #
#  Authors: Joel Malard, Alan Simpson,            (EPCC)        #
#           Rolf Rabenseifner, Traugott Streicher,              #
#           Tobias Haas (HLRS)                                  #
#                                                               #
#  Contact: rabenseifner@hlrs.de                                #
#                                                               #
#  Purpose: A program with derived datatypes.                   #
#                                                               #
#  Contents: Python code, buffer send version (comm.Send)       #
#                                                               #
#################################################################

from mpi4py import MPI
import numpy as np

np_dtype = np.dtype([('i', np.intc), ('j', np.intc)])

snd_buf = np.empty((), dtype=np_dtype)
rcv_buf = np.empty_like(snd_buf)
sum = np.empty_like(snd_buf)

status = MPI.Status()

comm_world = MPI.COMM_WORLD
my_rank = comm_world.Get_rank()
size = comm_world.Get_size()

right = (my_rank + 1) % size
left = (my_rank - 1 + size) % size

# TODO: Nastavte MPI datove typy na posielanie a prijimanie ciastocnych suctov.
# 1) Odvodte typ z 'MPI.INT', v novom type budu dva prvky tohto typu.
#    Novy typ ulozte do premennej 'send_recv_type'.
# 2) Pred prvym pouzitim nezabudnite definiciu typu zaregistrovat.
send_recv_type = None


sum['i'] = 0
sum['j'] = 0
snd_buf['i'] = my_rank
snd_buf['j'] = 10 * my_rank

for i in range(size):
    # TODO: upravte metody 'Issend()'/'Recv()' tak, aby pouzivali novy typ.
    request = comm_world.Issend((snd_buf, 1, MPI.CHAR), right, 17)
    comm_world.Recv((rcv_buf, 1, MPI.CHAR), left, 17, status)
    request.Wait(status)
    np.copyto(snd_buf, rcv_buf)
    sum['i'] += rcv_buf['i']
    sum['j'] += rcv_buf['j']

print(f"PE{my_rank}:\tSum = {sum['i']}\t{sum['j']}")

#################################################################

# #

# This file has been written as a sample solution to an #

# exercise in a course given at the High Performance #

# Computing Centre Stuttgart (HLRS). #

# The examples are based on the examples in the MPI course of #

# the Edinburgh Parallel Computing Centre (EPCC). #

# It is made freely available with the understanding that #

# every copy of this file must include this header and that #

# HLRS and EPCC take no responsibility for the use of the #

# enclosed teaching material. #

# #

# Authors: Joel Malard, Alan Simpson, (EPCC) #

# Rolf Rabenseifner, Traugott Streicher, #

# Tobias Haas (HLRS) #

# #

# Contact: rabenseifner@hlrs.de #

# #

# Purpose: A program with derived datatypes. #

# #

# Contents: Python code, buffer send version (comm.Send) #

# #

#################################################################

from mpi4py import MPI

import numpy as np

np_dtype = np.dtype([('i', np.intc), ('j', np.intc)])

snd_buf = np.empty((), dtype=np_dtype)

rcv_buf = np.empty_like(snd_buf)

sum = np.empty_like(snd_buf)

status = MPI.Status()

comm_world = MPI.COMM_WORLD

my_rank = comm_world.Get_rank()

size = comm_world.Get_size()

right = (my_rank + 1) % size

left = (my_rank - 1 + size) % size

# TODO: Nastavte MPI datove typy na posielanie a prijimanie ciastocnych suctov.

# 1) Odvodte typ z 'MPI.INT', v novom type budu dva prvky tohto typu.

# Novy typ ulozte do premennej 'send_recv_type'.

# 2) Pred prvym pouzitim nezabudnite definiciu typu zaregistrovat.

send_recv_type = None

sum['i'] = 0

sum['j'] = 0

snd_buf['i'] = my_rank

snd_buf['j'] = 10 * my_rank

for i in range(size):

# TODO: upravte metody 'Issend()'/'Recv()' tak, aby pouzivali novy typ.

request = comm_world.Issend((snd_buf, 1, MPI.CHAR), right, 17)

comm_world.Recv((rcv_buf, 1, MPI.CHAR), left, 17, status)

request.Wait(status)

np.copyto(snd_buf, rcv_buf)

sum['i'] += rcv_buf['i']

sum['j'] += rcv_buf['j']

print(f"PE{my_rank}:\tSum = {sum['i']}\t{sum['j']}")

5. Štruktúra

#################################################################
#                                                               #
#  This file has been written as a sample solution to an        #
#  exercise in a course given at the High Performance           #
#  Computing Centre Stuttgart (HLRS).                           #
#  The examples are based on the examples in the MPI course of  #
#  the Edinburgh Parallel Computing Centre (EPCC).              #
#  It is made freely available with the understanding that      #
#  every copy of this file must include this header and that    #
#  HLRS and EPCC take no responsibility for the use of the      #
#  enclosed teaching material.                                  #
#                                                               #
#  Authors: Joel Malard, Alan Simpson,            (EPCC)        #
#           Rolf Rabenseifner, Traugott Streicher,              #
#           Tobias Haas (HLRS)                                  #
#                                                               #
#  Contact: rabenseifner@hlrs.de                                #
#                                                               #
#  Purpose: A program with derived datatypes.                   #
#                                                               #
#  Contents: Python code, buffer send version (comm.Send)       #
#                                                               #
#################################################################

from mpi4py import MPI
import numpy as np

np_dtype = np.dtype([('i', np.intc), ('f', np.single)])
snd_buf = np.empty((), dtype=np_dtype)
rcv_buf = np.empty_like(snd_buf)
sum = np.empty_like(snd_buf)

array_of_blocklengths = [None] * 2
array_of_displacements = [None] * 2
array_of_types = [None] * 2

status = MPI.Status()

comm_world = MPI.COMM_WORLD
my_rank = comm_world.Get_rank()
size = comm_world.Get_size()

right = (my_rank + 1) % size
left = (my_rank - 1 + size) % size

# Nastavte datove typy MPI pre posielanie a prijimanie ciastocnych suctov.

# TODO: Nastavte pole 'blocklengths'.
array_of_blocklengths[0] = 0
array_of_blocklengths[1] = 0

# TODO: Ziskajte absolutne adresy premennych v nupy strukture.
addr_of_int = 0
addr_of_float = 0

# TODO: Nastavte displacement pola. Prva premenna ma displacement 0.
array_of_displacements[0] = 0
array_of_displacements[1] = 0

# TODO: Nastavte hodnoty v poli typov.
array_of_types[0] = None
array_of_types[1] = None

# TODO: Vytvorte novy typ struktury v MPI. Nezabudnite zaregistrovat definiciu
#       typu pred jeho prvym pouzitim.
send_recv_type = None


sum['i'] = 0
sum['f'] = 0
snd_buf['i'] = my_rank
snd_buf['f'] = 10 * my_rank

for i in range(size):
    # TODO: upravte funkcie 'Issend()'/'Recv()' tak, aby pouzivali novy typ.
    request = comm_world.Issend((snd_buf, 1, MPI.CHAR), right, 17)
    comm_world.Recv((rcv_buf, 1, MPI.CHAR), left, 17, status)
    request.Wait(status)
    np.copyto(snd_buf, rcv_buf)
    sum['i'] += rcv_buf['i']
    sum['f'] += rcv_buf['f']

print(f"PE{my_rank}:\tSum = {sum['i']}\t{sum['f']}")

#################################################################

# #

# This file has been written as a sample solution to an #

# exercise in a course given at the High Performance #

# Computing Centre Stuttgart (HLRS). #

# The examples are based on the examples in the MPI course of #

# the Edinburgh Parallel Computing Centre (EPCC). #

# It is made freely available with the understanding that #

# every copy of this file must include this header and that #

# HLRS and EPCC take no responsibility for the use of the #

# enclosed teaching material. #

# #

# Authors: Joel Malard, Alan Simpson, (EPCC) #

# Rolf Rabenseifner, Traugott Streicher, #

# Tobias Haas (HLRS) #

# #

# Contact: rabenseifner@hlrs.de #

# #

# Purpose: A program with derived datatypes. #

# #

# Contents: Python code, buffer send version (comm.Send) #

# #

#################################################################

from mpi4py import MPI

import numpy as np

np_dtype = np.dtype([('i', np.intc), ('f', np.single)])

snd_buf = np.empty((), dtype=np_dtype)

rcv_buf = np.empty_like(snd_buf)

sum = np.empty_like(snd_buf)

array_of_blocklengths = [None] * 2

array_of_displacements = [None] * 2

array_of_types = [None] * 2

status = MPI.Status()

comm_world = MPI.COMM_WORLD

my_rank = comm_world.Get_rank()

size = comm_world.Get_size()

right = (my_rank + 1) % size

left = (my_rank - 1 + size) % size

# Nastavte datove typy MPI pre posielanie a prijimanie ciastocnych suctov.

# TODO: Nastavte pole 'blocklengths'.

array_of_blocklengths[0] = 0

array_of_blocklengths[1] = 0

# TODO: Ziskajte absolutne adresy premennych v nupy strukture.

addr_of_int = 0

addr_of_float = 0

# TODO: Nastavte displacement pola. Prva premenna ma displacement 0.

array_of_displacements[0] = 0

array_of_displacements[1] = 0

# TODO: Nastavte hodnoty v poli typov.

array_of_types[0] = None

array_of_types[1] = None

# TODO: Vytvorte novy typ struktury v MPI. Nezabudnite zaregistrovat definiciu

# typu pred jeho prvym pouzitim.

send_recv_type = None

sum['i'] = 0

sum['f'] = 0

snd_buf['i'] = my_rank

snd_buf['f'] = 10 * my_rank

for i in range(size):

# TODO: upravte funkcie 'Issend()'/'Recv()' tak, aby pouzivali novy typ.

request = comm_world.Issend((snd_buf, 1, MPI.CHAR), right, 17)

comm_world.Recv((rcv_buf, 1, MPI.CHAR), left, 17, status)

request.Wait(status)

np.copyto(snd_buf, rcv_buf)

sum['i'] += rcv_buf['i']

sum['f'] += rcv_buf['f']

print(f"PE{my_rank}:\tSum = {sum['i']}\t{sum['f']}")